From ffe7520ef59c2b05b3fa01cdaf4e90bcb5aca892 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 24 May 2023 22:00:12 +1000 Subject: [PATCH 01/85] Bump version --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 34db20a..bb09793 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Type: Package Package: CuratedAtlasQueryR Title: Queries the Human Cell Atlas -Version: 0.99.2 +Version: 0.99.3 Authors@R: c( person( "Stefano", From 3a6435e5eb3281700bf94038ae94519e43194858 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 6 Jul 2023 12:02:56 +1000 Subject: [PATCH 02/85] Fix as.sparse method signature --- R/seurat.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/seurat.R b/R/seurat.R index 484978b..7648f8d 100644 --- a/R/seurat.R +++ b/R/seurat.R @@ -4,7 +4,7 @@ #' @importFrom methods as #' @importFrom SeuratObject as.sparse #' @exportS3Method -as.sparse.DelayedMatrix <- function(x) { +as.sparse.DelayedMatrix <- function(x, ...) { # This is glue to ensure the SCE -> Seurat conversion works properly with # DelayedArray types as(x, "dgCMatrix") From f9fa30e9a01e05b32dc9c018e3fd8cc5590b8d38 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 6 Jul 2023 12:30:34 +1000 Subject: [PATCH 03/85] Bump version --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index bb09793..77336b5 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Type: Package Package: CuratedAtlasQueryR Title: Queries the Human Cell Atlas -Version: 0.99.3 +Version: 0.99.4 Authors@R: c( person( "Stefano", From d5387858c8f1d744d6372533eab9c7083f12d423 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 11 Jul 2023 16:39:35 +1000 Subject: [PATCH 04/85] Document less things --- R/counts.R | 1 + R/dev.R | 1 + R/metadata.R | 3 +++ R/unharmonised.R | 1 + man/COUNTS_VERSION.Rd | 18 ------------------ man/DATABASE_URL.Rd | 3 +++ man/SAMPLE_DATABASE_URL.Rd | 3 +++ man/UNHARMONISED_URL.Rd | 16 ---------------- man/cache.Rd | 16 ---------------- man/downsample_metadata.Rd | 17 ----------------- 10 files changed, 12 insertions(+), 67 deletions(-) delete mode 100644 man/COUNTS_VERSION.Rd delete mode 100644 man/UNHARMONISED_URL.Rd delete mode 100644 man/cache.Rd delete mode 100644 man/downsample_metadata.Rd diff --git a/R/counts.R b/R/counts.R index 3024c3a..6736001 100644 --- a/R/counts.R +++ b/R/counts.R @@ -20,6 +20,7 @@ COUNTS_URL <- single_line_str( ) #' Current version of the counts. This will be incremented when a newer #' version is released +#' @noRd COUNTS_VERSION <- "0.2.1" #' @inherit get_single_cell_experiment diff --git a/R/dev.R b/R/dev.R index fbc7abc..a30d4cd 100644 --- a/R/dev.R +++ b/R/dev.R @@ -254,6 +254,7 @@ hdf5_to_anndata <- function(input_directory, output_directory){ #' Makes a "downsampled" metadata file that only contains the minimal data #' needed to run the vignette. +#' @noRd #' @param output Character scalar. Path to the output file. #' @return NULL #' @keywords internal diff --git a/R/metadata.R b/R/metadata.R index bceba6c..ba5d8c1 100644 --- a/R/metadata.R +++ b/R/metadata.R @@ -4,12 +4,14 @@ NULL #' Environment that we use to cache the DuckDB connections +#' @noRd cache <- rlang::env( metadata_table = rlang::env() ) #' URL pointing to the full metadata file #' @export +#' @return A character scalar consisting of the URL #' @examples #' get_metadata(remote_url = DATABASE_URL) DATABASE_URL <- single_line_str( @@ -20,6 +22,7 @@ DATABASE_URL <- single_line_str( #' URL pointing to the sample metadata file, which is smaller and for test, #' demonstration, and vignette purposes only #' @export +#' @return A character scalar consisting of the URL #' @examples #' get_metadata(remote_url = SAMPLE_DATABASE_URL) SAMPLE_DATABASE_URL <- single_line_str( diff --git a/R/unharmonised.R b/R/unharmonised.R index 951ba49..9e99586 100644 --- a/R/unharmonised.R +++ b/R/unharmonised.R @@ -4,6 +4,7 @@ NULL #' Base URL for all the unharmonised data +#' @noRd UNHARMONISED_URL <- single_line_str( "https://object-store.rc.nectar.org.au/v1/ AUTH_06d6e008e3e642da99d806ba3ea629c5/unharmonised_metadata" diff --git a/man/COUNTS_VERSION.Rd b/man/COUNTS_VERSION.Rd deleted file mode 100644 index 69d6633..0000000 --- a/man/COUNTS_VERSION.Rd +++ /dev/null @@ -1,18 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/counts.R -\docType{data} -\name{COUNTS_VERSION} -\alias{COUNTS_VERSION} -\title{Current version of the counts. This will be incremented when a newer -version is released} -\format{ -An object of class \code{character} of length 1. -} -\usage{ -COUNTS_VERSION -} -\description{ -Current version of the counts. This will be incremented when a newer -version is released -} -\keyword{datasets} diff --git a/man/DATABASE_URL.Rd b/man/DATABASE_URL.Rd index 7438dcd..fa9818f 100644 --- a/man/DATABASE_URL.Rd +++ b/man/DATABASE_URL.Rd @@ -10,6 +10,9 @@ An object of class \code{character} of length 1. \usage{ DATABASE_URL } +\value{ +A character scalar consisting of the URL +} \description{ URL pointing to the full metadata file } diff --git a/man/SAMPLE_DATABASE_URL.Rd b/man/SAMPLE_DATABASE_URL.Rd index 0666b77..52df1d0 100644 --- a/man/SAMPLE_DATABASE_URL.Rd +++ b/man/SAMPLE_DATABASE_URL.Rd @@ -11,6 +11,9 @@ An object of class \code{character} of length 1. \usage{ SAMPLE_DATABASE_URL } +\value{ +A character scalar consisting of the URL +} \description{ URL pointing to the sample metadata file, which is smaller and for test, demonstration, and vignette purposes only diff --git a/man/UNHARMONISED_URL.Rd b/man/UNHARMONISED_URL.Rd deleted file mode 100644 index 0f35d12..0000000 --- a/man/UNHARMONISED_URL.Rd +++ /dev/null @@ -1,16 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/unharmonised.R -\docType{data} -\name{UNHARMONISED_URL} -\alias{UNHARMONISED_URL} -\title{Base URL for all the unharmonised data} -\format{ -An object of class \code{character} of length 1. -} -\usage{ -UNHARMONISED_URL -} -\description{ -Base URL for all the unharmonised data -} -\keyword{datasets} diff --git a/man/cache.Rd b/man/cache.Rd deleted file mode 100644 index 9588ebb..0000000 --- a/man/cache.Rd +++ /dev/null @@ -1,16 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/metadata.R -\docType{data} -\name{cache} -\alias{cache} -\title{Environment that we use to cache the DuckDB connections} -\format{ -An object of class \code{environment} of length 1. -} -\usage{ -cache -} -\description{ -Environment that we use to cache the DuckDB connections -} -\keyword{datasets} diff --git a/man/downsample_metadata.Rd b/man/downsample_metadata.Rd deleted file mode 100644 index 217129b..0000000 --- a/man/downsample_metadata.Rd +++ /dev/null @@ -1,17 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/dev.R -\name{downsample_metadata} -\alias{downsample_metadata} -\title{Makes a "downsampled" metadata file that only contains the minimal data -needed to run the vignette.} -\usage{ -downsample_metadata(output = "sample_meta.parquet") -} -\arguments{ -\item{output}{Character scalar. Path to the output file.} -} -\description{ -Makes a "downsampled" metadata file that only contains the minimal data -needed to run the vignette. -} -\keyword{internal} From c97daeef72b63f0f63c47e3850acc34b455aa596 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 11 Jul 2023 16:58:03 +1000 Subject: [PATCH 05/85] Pin Matrix version --- DESCRIPTION | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 77336b5..7f7c7bd 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -93,7 +93,8 @@ Imports: utils, dbplyr (>= 2.3.0), duckdb, - stringr + stringr, + Matrix (>= 1.5.3) Suggests: zellkonverter, rmarkdown, From 49e18f90dd6dd74b7bd3e32cd0098265f0cb55e0 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 11 Jul 2023 17:11:42 +1000 Subject: [PATCH 06/85] Bump version --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 7f7c7bd..0eff9fa 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Type: Package Package: CuratedAtlasQueryR Title: Queries the Human Cell Atlas -Version: 0.99.4 +Version: 0.99.5 Authors@R: c( person( "Stefano", From 21eff294cdfbbb338a8a7c570f6f1f23f1b2ad8c Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 11 Jul 2023 17:44:49 +1000 Subject: [PATCH 07/85] Bust the cache --- .github/workflows/check-bioc.yml | 2 +- README.md | 391 +++++++++++++++++++------------ vignettes/Introduction.Rmd | 17 +- 3 files changed, 251 insertions(+), 159 deletions(-) diff --git a/.github/workflows/check-bioc.yml b/.github/workflows/check-bioc.yml index f313c76..aa77daf 100755 --- a/.github/workflows/check-bioc.yml +++ b/.github/workflows/check-bioc.yml @@ -38,7 +38,7 @@ env: run_covr: 'false' run_pkgdown: 'true' has_RUnit: 'false' - cache-version: 'cache-v1' + cache-version: 'cache-v2' jobs: build-check: diff --git a/README.md b/README.md index c4c8c58..6130c01 100644 --- a/README.md +++ b/README.md @@ -45,16 +45,16 @@ metadata #> # Database: DuckDB 0.7.1 [unknown@Linux 3.10.0-1160.88.1.el7.x86_64:R 4.2.1/:memory:] #> cell_ sample_ cell_…¹ cell_…² confi…³ cell_…⁴ cell_…⁵ cell_…⁶ sampl…⁷ _samp…⁸ #> -#> 1 8387… 7bd7b8… natura… immune… 5 cd8 tem gmp natura… 842ce7… Q59___… -#> 2 1768… 7bd7b8… natura… immune… 5 cd8 tem cd8 tcm natura… 842ce7… Q59___… -#> 3 6329… 7bd7b8… natura… immune… 5 cd8 tem clp termin… 842ce7… Q59___… -#> 4 5027… 7bd7b8… natura… immune… 5 cd8 tem clp natura… 842ce7… Q59___… -#> 5 7956… 7bd7b8… natura… immune… 5 cd8 tem clp natura… 842ce7… Q59___… -#> 6 4305… 7bd7b8… natura… immune… 5 cd8 tem clp termin… 842ce7… Q59___… -#> 7 2126… 933f96… natura… ilc 1 nk nk natura… c250bf… AML3__… -#> 8 3114… 933f96… natura… immune… 5 mait nk natura… c250bf… AML3__… -#> 9 1407… 933f96… natura… immune… 5 mait clp natura… c250bf… AML3__… -#> 10 2911… 933f96… natura… nk 5 nk clp natura… c250bf… AML3__… +#> 1 TTAT… 039c55… mature… immune… 5 gdt cd4 tem natura… 0c1d32… BPH340… +#> 2 GCTT… 039c55… mature… cd8 tem 3 cd8 tem cd8 tem effect… 0c1d32… BPH340… +#> 3 GTCA… 039c55… mature… immune… 5 cd8 tem cd8 tcm vd2 gd… 0c1d32… BPH340… +#> 4 AAGG… 039c55… mature… immune… 5 cd8 tem clp vd2 gd… 0c1d32… BPH340… +#> 5 ATTG… 039c55… mature… immune… 5 cd8 tem cd8 tcm vd2 gd… 0c1d32… BPH340… +#> 6 CTCG… 039c55… mature… immune… 5 cd8 tem cd8 tcm natura… 0c1d32… BPH340… +#> 7 CGTC… 07e649… mature… immune… 5 nk_cd5… clp natura… 3b5c6b… BPH340… +#> 8 ACGA… 176400… mature… immune… 5 nk pro… nk natura… 0ccffb… BPH511… +#> 9 TCAT… 176400… mature… immune… 5 cd4 pr… tregs t regu… 0ccffb… BPH511… +#> 10 GAGA… 176400… mature… immune… 5 nk pro… nk natura… 0ccffb… BPH511… #> # … with more rows, 46 more variables: assay , #> # assay_ontology_term_id , file_id_db , #> # cell_type_ontology_term_id , development_stage , @@ -71,20 +71,21 @@ The `metadata` variable can then be re-used for all subsequent queries. ``` r metadata |> dplyr::distinct(tissue, file_id) -#> # Source: SQL [10 x 2] +#> # Source: SQL [?? x 2] #> # Database: DuckDB 0.7.1 [unknown@Linux 3.10.0-1160.88.1.el7.x86_64:R 4.2.1/:memory:] -#> tissue file_id -#> -#> 1 bone marrow 1ff5cbda-4d41-4f50-8c7e-cbe4a90e38db -#> 2 lung parenchyma 6661ab3a-792a-4682-b58c-4afb98b2c016 -#> 3 respiratory airway 6661ab3a-792a-4682-b58c-4afb98b2c016 -#> 4 nose 6661ab3a-792a-4682-b58c-4afb98b2c016 -#> 5 renal pelvis dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 -#> 6 kidney dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 -#> 7 renal medulla dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 -#> 8 cortex of kidney dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 -#> 9 kidney blood vessel dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 -#> 10 lung a2796032-d015-40c4-b9db-835207e5bd5b +#> tissue file_id +#> +#> 1 transition zone of prostate d0a2b5d0-ded0-4ed0-af22-a6dde2799513 +#> 2 peripheral zone of prostate d0a2b5d0-ded0-4ed0-af22-a6dde2799513 +#> 3 heart left ventricle 5775c8d8-e37e-40cd-94f4-8e78b05ca331 +#> 4 lung f0691786-c20a-4727-8d34-9db28b418c4e +#> 5 blood f0691786-c20a-4727-8d34-9db28b418c4e +#> 6 blood 60ba353d-236a-4b91-a0a2-a74669c2b55e +#> 7 bone marrow 1ff5cbda-4d41-4f50-8c7e-cbe4a90e38db +#> 8 kidney 63523aa3-0d04-4fc6-ac59-5cadd3e73a14 +#> 9 renal medulla 63523aa3-0d04-4fc6-ac59-5cadd3e73a14 +#> 10 cortex of kidney 63523aa3-0d04-4fc6-ac59-5cadd3e73a14 +#> # … with more rows ``` ## Download single-cell RNA sequencing counts @@ -109,19 +110,19 @@ single_cell_counts = single_cell_counts #> # A SingleCellExperiment-tibble abstraction: 1,571 × 57 -#> # Features=36229 | Cells=1571 | Assays=counts +#> # [90mFeatures=36229 | Cells=1571 | Assays=counts[0m #> .cell sample_ cell_…¹ cell_…² confi…³ cell_…⁴ cell_…⁵ cell_…⁶ sampl…⁷ X_sam…⁸ #> -#> 1 AGCG… 11a7dc… CD4-po… cd4 th1 3 cd4 tcm cd8 t th1 10b339… Donor_… +#> 1 ACAC… 11a7dc… CD4-po… immune… 5 cd4 tcm plasma th1/th… 10b339… Donor_… #> 2 TCAG… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tem th1/th… 10b339… Donor_… -#> 3 TTTA… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tcm th17 10b339… Donor_… -#> 4 ACAC… 11a7dc… CD4-po… immune… 5 cd4 tcm plasma th1/th… 10b339… Donor_… -#> 5 CAAG… 11a7dc… CD4-po… immune… 1 cd4 tcm cd4 tcm mait 10b339… Donor_… -#> 6 CTGT… 14a078… CD4-po… cd4 th… 3 cd4 tcm cd4 tem th1/th… 8f71c5… VUHD85… -#> 7 ACGT… 14a078… CD4-po… treg 5 cd4 tcm tregs t regu… 8f71c5… VUHD85… -#> 8 CATA… 14a078… CD4-po… immune… 5 nk cd8 tem mait 8f71c5… VUHD85… -#> 9 ACTT… 14a078… CD4-po… mait 5 mait cd8 tem mait 8f71c5… VUHD85… -#> 10 TGCG… 14a078… CD4-po… cd4 th1 3 cd4 tcm cd4 tem th1 8f71c5… VUHD85… +#> 3 CAAG… 11a7dc… CD4-po… immune… 1 cd4 tcm cd4 tcm mait 10b339… Donor_… +#> 4 TTTA… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tcm th17 10b339… Donor_… +#> 5 AGCG… 11a7dc… CD4-po… cd4 th1 3 cd4 tcm cd8 t th1 10b339… Donor_… +#> 6 GTCA… 14a078… CD4-po… immune… 5 cd4 tcm cd4 tem mait 8f71c5… VUHD85… +#> 7 ACTT… 14a078… CD4-po… mait 5 mait cd8 tem mait 8f71c5… VUHD85… +#> 8 GATC… 14a078… CD4-po… immune… 5 cd4 tcm cd4 tem mait 8f71c5… VUHD85… +#> 9 TGTC… 14a078… CD4-po… termin… 3 cd4 tem cd8 tem termin… 8f71c5… VUHD85… +#> 10 GCGC… 14a078… CD4-po… cd4 th… 3 cd4 tcm cd4 t th17 8f71c5… VUHD85… #> # … with 1,561 more rows, 47 more variables: assay , #> # assay_ontology_term_id , file_id_db , #> # cell_type_ontology_term_id , development_stage , @@ -154,19 +155,19 @@ single_cell_counts = single_cell_counts #> # A SingleCellExperiment-tibble abstraction: 1,571 × 57 -#> # Features=36229 | Cells=1571 | Assays=cpm +#> # [90mFeatures=36229 | Cells=1571 | Assays=cpm[0m #> .cell sample_ cell_…¹ cell_…² confi…³ cell_…⁴ cell_…⁵ cell_…⁶ sampl…⁷ X_sam…⁸ #> -#> 1 AGCG… 11a7dc… CD4-po… cd4 th1 3 cd4 tcm cd8 t th1 10b339… Donor_… +#> 1 ACAC… 11a7dc… CD4-po… immune… 5 cd4 tcm plasma th1/th… 10b339… Donor_… #> 2 TCAG… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tem th1/th… 10b339… Donor_… -#> 3 TTTA… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tcm th17 10b339… Donor_… -#> 4 ACAC… 11a7dc… CD4-po… immune… 5 cd4 tcm plasma th1/th… 10b339… Donor_… -#> 5 CAAG… 11a7dc… CD4-po… immune… 1 cd4 tcm cd4 tcm mait 10b339… Donor_… -#> 6 CTGT… 14a078… CD4-po… cd4 th… 3 cd4 tcm cd4 tem th1/th… 8f71c5… VUHD85… -#> 7 ACGT… 14a078… CD4-po… treg 5 cd4 tcm tregs t regu… 8f71c5… VUHD85… -#> 8 CATA… 14a078… CD4-po… immune… 5 nk cd8 tem mait 8f71c5… VUHD85… -#> 9 ACTT… 14a078… CD4-po… mait 5 mait cd8 tem mait 8f71c5… VUHD85… -#> 10 TGCG… 14a078… CD4-po… cd4 th1 3 cd4 tcm cd4 tem th1 8f71c5… VUHD85… +#> 3 CAAG… 11a7dc… CD4-po… immune… 1 cd4 tcm cd4 tcm mait 10b339… Donor_… +#> 4 TTTA… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tcm th17 10b339… Donor_… +#> 5 AGCG… 11a7dc… CD4-po… cd4 th1 3 cd4 tcm cd8 t th1 10b339… Donor_… +#> 6 GTCA… 14a078… CD4-po… immune… 5 cd4 tcm cd4 tem mait 8f71c5… VUHD85… +#> 7 ACTT… 14a078… CD4-po… mait 5 mait cd8 tem mait 8f71c5… VUHD85… +#> 8 GATC… 14a078… CD4-po… immune… 5 cd4 tcm cd4 tem mait 8f71c5… VUHD85… +#> 9 TGTC… 14a078… CD4-po… termin… 3 cd4 tem cd8 tem termin… 8f71c5… VUHD85… +#> 10 GCGC… 14a078… CD4-po… cd4 th… 3 cd4 tcm cd4 t th17 8f71c5… VUHD85… #> # … with 1,561 more rows, 47 more variables: assay , #> # assay_ontology_term_id , file_id_db , #> # cell_type_ontology_term_id , development_stage , @@ -196,19 +197,19 @@ single_cell_counts = single_cell_counts #> # A SingleCellExperiment-tibble abstraction: 1,571 × 57 -#> # Features=1 | Cells=1571 | Assays=cpm +#> # [90mFeatures=1 | Cells=1571 | Assays=cpm[0m #> .cell sample_ cell_…¹ cell_…² confi…³ cell_…⁴ cell_…⁵ cell_…⁶ sampl…⁷ X_sam…⁸ #> -#> 1 AGCG… 11a7dc… CD4-po… cd4 th1 3 cd4 tcm cd8 t th1 10b339… Donor_… +#> 1 ACAC… 11a7dc… CD4-po… immune… 5 cd4 tcm plasma th1/th… 10b339… Donor_… #> 2 TCAG… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tem th1/th… 10b339… Donor_… -#> 3 TTTA… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tcm th17 10b339… Donor_… -#> 4 ACAC… 11a7dc… CD4-po… immune… 5 cd4 tcm plasma th1/th… 10b339… Donor_… -#> 5 CAAG… 11a7dc… CD4-po… immune… 1 cd4 tcm cd4 tcm mait 10b339… Donor_… -#> 6 CTGT… 14a078… CD4-po… cd4 th… 3 cd4 tcm cd4 tem th1/th… 8f71c5… VUHD85… -#> 7 ACGT… 14a078… CD4-po… treg 5 cd4 tcm tregs t regu… 8f71c5… VUHD85… -#> 8 CATA… 14a078… CD4-po… immune… 5 nk cd8 tem mait 8f71c5… VUHD85… -#> 9 ACTT… 14a078… CD4-po… mait 5 mait cd8 tem mait 8f71c5… VUHD85… -#> 10 TGCG… 14a078… CD4-po… cd4 th1 3 cd4 tcm cd4 tem th1 8f71c5… VUHD85… +#> 3 CAAG… 11a7dc… CD4-po… immune… 1 cd4 tcm cd4 tcm mait 10b339… Donor_… +#> 4 TTTA… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tcm th17 10b339… Donor_… +#> 5 AGCG… 11a7dc… CD4-po… cd4 th1 3 cd4 tcm cd8 t th1 10b339… Donor_… +#> 6 GTCA… 14a078… CD4-po… immune… 5 cd4 tcm cd4 tem mait 8f71c5… VUHD85… +#> 7 ACTT… 14a078… CD4-po… mait 5 mait cd8 tem mait 8f71c5… VUHD85… +#> 8 GATC… 14a078… CD4-po… immune… 5 cd4 tcm cd4 tem mait 8f71c5… VUHD85… +#> 9 TGTC… 14a078… CD4-po… termin… 3 cd4 tem cd8 tem termin… 8f71c5… VUHD85… +#> 10 GCGC… 14a078… CD4-po… cd4 th… 3 cd4 tcm cd4 t th17 8f71c5… VUHD85… #> # … with 1,561 more rows, 47 more variables: assay , #> # assay_ontology_term_id , file_id_db , #> # cell_type_ontology_term_id , development_stage , @@ -253,7 +254,7 @@ The returned `SingleCellExperiment` can be saved with two modalities, as ### Saving as RDS (fast saving, slow reading) -Saving as `.rds` has the advantage of being fast, andd the `.rds` file +Saving as `.rds` has the advantage of being fast, and the `.rds` file occupies very little disk space as it only stores the links to the files in your cache. @@ -288,35 +289,76 @@ single_cell_counts |> HDF5Array::saveHDF5SummarizedExperiment("single_cell_count We can gather all CD14 monocytes cells and plot the distribution of HLA-A across all tissues - #> ℹ Realising metadata. - #> ℹ Synchronising files - #> ℹ Downloading 0 files, totalling 0 GB - #> ℹ Reading files. - #> ℹ Compiling Single Cell Experiment. - #> Warning: Transformation introduced infinite values in continuous y-axis - #> Warning in min(x): no non-missing arguments to min; returning Inf - #> Warning in max(x): no non-missing arguments to max; returning -Inf +``` r +suppressPackageStartupMessages({ + library(ggplot2) +}) + +# Plots with styling +counts <- metadata |> + # Filter and subset + dplyr::filter(cell_type_harmonised == "cd14 mono") |> + dplyr::filter(file_id_db != "c5a05f23f9784a3be3bfa651198a48eb") |> + + # Get counts per million for HCA-A gene + get_single_cell_experiment(assays = "cpm", features = "HLA-A") |> + suppressMessages() |> + + # Add feature to table + tidySingleCellExperiment::join_features("HLA-A", shape = "wide") |> + + # Rank x axis + tibble::as_tibble() + +# Plot by disease +counts |> + dplyr::with_groups(disease, ~ .x |> dplyr::mutate(median_count = median(`HLA.A`, rm.na=TRUE))) |> + + # Plot + ggplot(aes(forcats::fct_reorder(disease, median_count,.desc = TRUE), `HLA.A`,color = file_id)) + + geom_jitter(shape=".") + + + # Style + guides(color="none") + + scale_y_log10() + + theme_bw() + + theme(axis.text.x = element_text(angle = 60, vjust = 1, hjust = 1)) + + xlab("Disease") + + ggtitle("HLA-A in CD14 monocytes by disease") +#> Warning: Transformation introduced infinite values in continuous y-axis +``` ![](/stornext/Home/data/allstaff/m/milton.m/HCAquery/README_files/figure-gfm/unnamed-chunk-14-1.png) - #> Warning: Transformation introduced infinite values in continuous y-axis - #> Warning in min(x): no non-missing arguments to min; returning Inf - #> Warning in max(x): no non-missing arguments to max; returning -Inf +With the full metadata file, this looks like: -![](/stornext/Home/data/allstaff/m/milton.m/HCAquery/README_files/figure-gfm/unnamed-chunk-14-2.png) + ``` r -library(tidySingleCellExperiment) -library(ggplot2) - +# Plot by tissue counts |> - ggplot(aes( disease, `HLA.A`,color = file_id)) + - geom_jitter(shape=".") + dplyr::with_groups(tissue_harmonised, ~ .x |> dplyr::mutate(median_count = median(`HLA.A`, rm.na=TRUE))) |> + + # Plot + ggplot(aes(forcats::fct_reorder(tissue_harmonised, median_count,.desc = TRUE), `HLA.A`,color = file_id)) + + geom_jitter(shape=".") + + + # Style + guides(color="none") + + scale_y_log10() + + theme_bw() + + theme(axis.text.x = element_text(angle = 60, vjust = 1, hjust = 1)) + + xlab("Tissue") + + ggtitle("HLA-A in CD14 monocytes by tissue") + + theme(legend.position = "none") +#> Warning: Transformation introduced infinite values in continuous y-axis ``` -![](/stornext/Home/data/allstaff/m/milton.m/HCAquery/README_files/figure-gfm/unnamed-chunk-15-1.png) +![](/stornext/Home/data/allstaff/m/milton.m/HCAquery/README_files/figure-gfm/unnamed-chunk-16-1.png) -![](man/figures/HLA_A_disease_plot.png) +With the full metadata file, this looks like: + + ``` r metadata |> @@ -326,21 +368,22 @@ metadata |> # Get counts per million for HCA-A gene get_single_cell_experiment(assays = "cpm", features = "HLA-A") |> + suppressMessages() |> - # Plot (styling code have been omitted) + # Plot tidySingleCellExperiment::join_features("HLA-A", shape = "wide") |> - ggplot(aes(tissue_harmonised, `HLA.A`,color = file_id)) + - geom_jitter(shape=".") -#> ℹ Realising metadata. -#> ℹ Synchronising files -#> ℹ Downloading 0 files, totalling 0 GB -#> ℹ Reading files. -#> ℹ Compiling Single Cell Experiment. + ggplot(aes(tissue_harmonised, `HLA.A`, color = file_id)) + + theme_bw() + + theme( + axis.text.x = element_text(angle = 60, vjust = 1, hjust = 1), + legend.position = "none" + ) + + geom_jitter(shape=".") + + xlab("Tissue") + + ggtitle("HLA-A in nk cells by tissue") ``` -![](/stornext/Home/data/allstaff/m/milton.m/HCAquery/README_files/figure-gfm/unnamed-chunk-17-1.png) - -![](man/figures/HLA_A_tissue_plot.png) +![](/stornext/Home/data/allstaff/m/milton.m/HCAquery/README_files/figure-gfm/unnamed-chunk-18-1.png) ## Obtain Unharmonised Metadata @@ -355,10 +398,13 @@ data frame. harmonised <- metadata |> dplyr::filter(tissue == "kidney blood vessel") unharmonised <- get_unharmonised_metadata(harmonised) unharmonised -#> # A tibble: 1 × 2 +#> # A tibble: 4 × 2 #> file_id unharmonised #> -#> 1 dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 +#> 1 63523aa3-0d04-4fc6-ac59-5cadd3e73a14 +#> 2 8fee7b82-178b-4c04-bf23-04689415690d +#> 3 dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 +#> 4 f7e94dbb-8638-4616-aaf9-16e2212c369f ``` Notice that the columns differ between each dataset’s data frame: @@ -366,25 +412,45 @@ Notice that the columns differ between each dataset’s data frame: ``` r dplyr::pull(unharmonised) |> head(2) #> [[1]] -#> # Source: SQL [?? x 14] +#> # Source: SQL [?? x 17] #> # Database: DuckDB 0.7.1 [unknown@Linux 3.10.0-1160.88.1.el7.x86_64:R 4.2.1/:memory:] #> cell_ file_id donor…¹ donor…² libra…³ mappe…⁴ sampl…⁵ suspe…⁶ suspe…⁷ autho…⁸ #> -#> 1 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… -#> 2 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… -#> 3 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… -#> 4 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… -#> 5 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… -#> 6 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… -#> 7 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… -#> 8 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… -#> 9 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… -#> 10 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… -#> # … with more rows, 4 more variables: reported_diseases , -#> # Experiment , Project , broad_celltype , and abbreviated +#> 1 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… +#> 2 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… +#> 3 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… +#> 4 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… +#> 5 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… +#> 6 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… +#> 7 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… +#> 8 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… +#> 9 4602… 63523a… 19 mon… 463181… 671785… GENCOD… 125234… cell c7485e… CD4 T … +#> 10 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… +#> # … with more rows, 7 more variables: cell_state , +#> # reported_diseases , Short_Sample , Project , +#> # Experiment , compartment , broad_celltype , and abbreviated #> # variable names ¹​donor_age, ²​donor_uuid, ³​library_uuid, #> # ⁴​mapped_reference_annotation, ⁵​sample_uuid, ⁶​suspension_type, #> # ⁷​suspension_uuid, ⁸​author_cell_type +#> +#> [[2]] +#> # Source: SQL [?? x 12] +#> # Database: DuckDB 0.7.1 [unknown@Linux 3.10.0-1160.88.1.el7.x86_64:R 4.2.1/:memory:] +#> cell_ file_id orig.…¹ nCoun…² nFeat…³ seura…⁴ Project donor…⁵ compa…⁶ broad…⁷ +#> +#> 1 1069 8fee7b… 4602ST… 16082 3997 25 Experi… Wilms3 non_PT Pelvic… +#> 2 1214 8fee7b… 4602ST… 1037 606 25 Experi… Wilms3 non_PT Pelvic… +#> 3 2583 8fee7b… 4602ST… 3028 1361 25 Experi… Wilms3 non_PT Pelvic… +#> 4 2655 8fee7b… 4602ST… 1605 859 25 Experi… Wilms3 non_PT Pelvic… +#> 5 3609 8fee7b… 4602ST… 1144 682 25 Experi… Wilms3 non_PT Pelvic… +#> 6 3624 8fee7b… 4602ST… 1874 963 25 Experi… Wilms3 non_PT Pelvic… +#> 7 3946 8fee7b… 4602ST… 1296 755 25 Experi… Wilms3 non_PT Pelvic… +#> 8 5163 8fee7b… 4602ST… 11417 3255 25 Experi… Wilms3 non_PT Pelvic… +#> 9 5446 8fee7b… 4602ST… 1769 946 19 Experi… Wilms2 lympho… CD4 T … +#> 10 6275 8fee7b… 4602ST… 3750 1559 25 Experi… Wilms3 non_PT Pelvic… +#> # … with more rows, 2 more variables: author_cell_type , Sample , and +#> # abbreviated variable names ¹​orig.ident, ²​nCount_RNA, ³​nFeature_RNA, +#> # ⁴​seurat_clusters, ⁵​donor_id, ⁶​compartment, ⁷​broad_celltype ``` # Cell metadata @@ -468,61 +534,82 @@ sessionInfo() #> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C #> #> attached base packages: -#> [1] stats4 stats graphics grDevices utils datasets methods -#> [8] base +#> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: -#> [1] tidySingleCellExperiment_1.6.3 SingleCellExperiment_1.18.1 -#> [3] SummarizedExperiment_1.26.1 Biobase_2.56.0 -#> [5] GenomicRanges_1.48.0 GenomeInfoDb_1.32.4 -#> [7] IRanges_2.30.1 S4Vectors_0.34.0 -#> [9] BiocGenerics_0.42.0 MatrixGenerics_1.8.1 -#> [11] matrixStats_0.63.0 ttservice_0.2.2 -#> [13] ggplot2_3.4.1 CuratedAtlasQueryR_0.99.1 +#> [1] ggplot2_3.4.1 CuratedAtlasQueryR_0.99.1 #> #> loaded via a namespace (and not attached): -#> [1] plyr_1.8.8 igraph_1.4.1 lazyeval_0.2.2 -#> [4] sp_1.5-1 splines_4.2.1 listenv_0.9.0 -#> [7] scattermore_0.8 digest_0.6.31 htmltools_0.5.4 -#> [10] fansi_1.0.3 magrittr_2.0.3 tensor_1.5 -#> [13] cluster_2.1.3 ROCR_1.0-11 globals_0.16.2 -#> [16] duckdb_0.7.1-1 spatstat.sparse_3.0-0 colorspace_2.0-3 -#> [19] blob_1.2.3 ggrepel_0.9.2 xfun_0.36 -#> [22] dplyr_1.1.0 RCurl_1.98-1.9 jsonlite_1.8.4 -#> [25] progressr_0.13.0 spatstat.data_3.0-0 survival_3.3-1 -#> [28] zoo_1.8-11 glue_1.6.2 polyclip_1.10-4 -#> [31] gtable_0.3.1 zlibbioc_1.42.0 XVector_0.36.0 -#> [34] leiden_0.4.3 DelayedArray_0.22.0 Rhdf5lib_1.18.2 -#> [37] future.apply_1.10.0 HDF5Array_1.24.2 abind_1.4-5 -#> [40] scales_1.2.1 DBI_1.1.3 spatstat.random_3.0-1 -#> [43] miniUI_0.1.1.1 Rcpp_1.0.10 viridisLite_0.4.1 -#> [46] xtable_1.8-4 reticulate_1.26 htmlwidgets_1.6.0 -#> [49] httr_1.4.4 RColorBrewer_1.1-3 ellipsis_0.3.2 -#> [52] Seurat_4.3.0 ica_1.0-3 farver_2.1.1 -#> [55] pkgconfig_2.0.3 dbplyr_2.3.0 sass_0.4.4 -#> [58] uwot_0.1.14 deldir_1.0-6 utf8_1.2.2 -#> [61] labeling_0.4.2 tidyselect_1.2.0 rlang_1.0.6 -#> [64] reshape2_1.4.4 later_1.3.0 munsell_0.5.0 -#> [67] tools_4.2.1 cachem_1.0.6 cli_3.6.0 -#> [70] generics_0.1.3 ggridges_0.5.4 evaluate_0.19 -#> [73] stringr_1.5.0 fastmap_1.1.0 yaml_2.3.6 -#> [76] goftest_1.2-3 knitr_1.42 fitdistrplus_1.1-8 -#> [79] purrr_1.0.1 RANN_2.6.1 pbapply_1.6-0 -#> [82] future_1.30.0 nlme_3.1-157 mime_0.12 -#> [85] compiler_4.2.1 rstudioapi_0.14 curl_4.3.3 -#> [88] plotly_4.10.1 png_0.1-8 spatstat.utils_3.0-1 -#> [91] tibble_3.1.8 bslib_0.4.2 stringi_1.7.12 -#> [94] highr_0.10 forcats_1.0.0 lattice_0.20-45 -#> [97] Matrix_1.5-3 vctrs_0.5.2 pillar_1.8.1 -#> [100] lifecycle_1.0.3 rhdf5filters_1.8.0 spatstat.geom_3.0-3 -#> [103] lmtest_0.9-40 jquerylib_0.1.4 RcppAnnoy_0.0.20 -#> [106] data.table_1.14.6 cowplot_1.1.1 bitops_1.0-7 -#> [109] irlba_2.3.5.1 httpuv_1.6.7 patchwork_1.1.2 -#> [112] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20 -#> [115] gridExtra_2.3 parallelly_1.33.0 codetools_0.2-18 -#> [118] assertthat_0.2.1 MASS_7.3-57 rhdf5_2.40.0 -#> [121] rprojroot_2.0.3 withr_2.5.0 SeuratObject_4.1.3 -#> [124] sctransform_0.3.5 GenomeInfoDbData_1.2.8 parallel_4.2.1 -#> [127] grid_4.2.1 tidyr_1.3.0 rmarkdown_2.20 -#> [130] Rtsne_0.16 spatstat.explore_3.0-5 shiny_1.7.4 +#> [1] plyr_1.8.8 igraph_1.4.1 +#> [3] lazyeval_0.2.2 sp_1.5-1 +#> [5] splines_4.2.1 listenv_0.9.0 +#> [7] scattermore_0.8 GenomeInfoDb_1.32.4 +#> [9] digest_0.6.31 htmltools_0.5.4 +#> [11] fansi_1.0.3 magrittr_2.0.3 +#> [13] tensor_1.5 cluster_2.1.3 +#> [15] ROCR_1.0-11 globals_0.16.2 +#> [17] matrixStats_0.63.0 duckdb_0.7.1-1 +#> [19] spatstat.sparse_3.0-0 colorspace_2.0-3 +#> [21] blob_1.2.3 ggrepel_0.9.2 +#> [23] ttservice_0.2.2 xfun_0.36 +#> [25] dplyr_1.1.0 RCurl_1.98-1.9 +#> [27] jsonlite_1.8.4 progressr_0.13.0 +#> [29] spatstat.data_3.0-0 survival_3.3-1 +#> [31] zoo_1.8-11 glue_1.6.2 +#> [33] polyclip_1.10-4 gtable_0.3.1 +#> [35] zlibbioc_1.42.0 XVector_0.36.0 +#> [37] leiden_0.4.3 DelayedArray_0.22.0 +#> [39] tidySingleCellExperiment_1.6.3 Rhdf5lib_1.18.2 +#> [41] future.apply_1.10.0 SingleCellExperiment_1.18.1 +#> [43] BiocGenerics_0.42.0 HDF5Array_1.24.2 +#> [45] abind_1.4-5 scales_1.2.1 +#> [47] DBI_1.1.3 spatstat.random_3.0-1 +#> [49] miniUI_0.1.1.1 Rcpp_1.0.10 +#> [51] viridisLite_0.4.1 xtable_1.8-4 +#> [53] reticulate_1.26 stats4_4.2.1 +#> [55] htmlwidgets_1.6.0 httr_1.4.4 +#> [57] RColorBrewer_1.1-3 ellipsis_0.3.2 +#> [59] Seurat_4.3.0 ica_1.0-3 +#> [61] farver_2.1.1 pkgconfig_2.0.3 +#> [63] dbplyr_2.3.0 sass_0.4.4 +#> [65] uwot_0.1.14 deldir_1.0-6 +#> [67] utf8_1.2.2 labeling_0.4.2 +#> [69] tidyselect_1.2.0 rlang_1.0.6 +#> [71] reshape2_1.4.4 later_1.3.0 +#> [73] munsell_0.5.0 tools_4.2.1 +#> [75] cachem_1.0.6 cli_3.6.0 +#> [77] generics_0.1.3 ggridges_0.5.4 +#> [79] evaluate_0.19 stringr_1.5.0 +#> [81] fastmap_1.1.0 yaml_2.3.6 +#> [83] goftest_1.2-3 knitr_1.42 +#> [85] fitdistrplus_1.1-8 purrr_1.0.1 +#> [87] RANN_2.6.1 pbapply_1.6-0 +#> [89] future_1.30.0 nlme_3.1-157 +#> [91] mime_0.12 compiler_4.2.1 +#> [93] rstudioapi_0.14 plotly_4.10.1 +#> [95] png_0.1-8 spatstat.utils_3.0-1 +#> [97] tibble_3.1.8 bslib_0.4.2 +#> [99] stringi_1.7.12 highr_0.10 +#> [101] forcats_1.0.0 lattice_0.20-45 +#> [103] Matrix_1.5-3 vctrs_0.5.2 +#> [105] pillar_1.8.1 lifecycle_1.0.3 +#> [107] rhdf5filters_1.8.0 spatstat.geom_3.0-3 +#> [109] lmtest_0.9-40 jquerylib_0.1.4 +#> [111] RcppAnnoy_0.0.20 data.table_1.14.6 +#> [113] cowplot_1.1.1 bitops_1.0-7 +#> [115] irlba_2.3.5.1 httpuv_1.6.7 +#> [117] patchwork_1.1.2 GenomicRanges_1.48.0 +#> [119] R6_2.5.1 promises_1.2.0.1 +#> [121] KernSmooth_2.23-20 gridExtra_2.3 +#> [123] IRanges_2.30.1 parallelly_1.33.0 +#> [125] codetools_0.2-18 assertthat_0.2.1 +#> [127] MASS_7.3-57 rhdf5_2.40.0 +#> [129] SummarizedExperiment_1.26.1 rprojroot_2.0.3 +#> [131] withr_2.5.0 SeuratObject_4.1.3 +#> [133] sctransform_0.3.5 S4Vectors_0.34.0 +#> [135] GenomeInfoDbData_1.2.8 parallel_4.2.1 +#> [137] grid_4.2.1 tidyr_1.3.0 +#> [139] rmarkdown_2.20 MatrixGenerics_1.8.1 +#> [141] Rtsne_0.16 spatstat.explore_3.0-5 +#> [143] Biobase_2.56.0 shiny_1.7.4 ``` diff --git a/vignettes/Introduction.Rmd b/vignettes/Introduction.Rmd index dbba7f5..ceca9a1 100644 --- a/vignettes/Introduction.Rmd +++ b/vignettes/Introduction.Rmd @@ -14,7 +14,7 @@ knit: > x, output_format = "html_document", params = list( - demo_metadata = TRUE + demo_metadata = FALSE ) ) rmarkdown::render( @@ -189,7 +189,7 @@ The returned `SingleCellExperiment` can be saved with two modalities, as `.rds` ### Saving as RDS (fast saving, slow reading) -Saving as `.rds` has the advantage of being fast, andd the `.rds` file occupies very little disk space as it only stores the links to the files in your cache. +Saving as `.rds` has the advantage of being fast, and the `.rds` file occupies very little disk space as it only stores the links to the files in your cache. However it has the disadvantage that for big `SingleCellExperiment` objects, which merge a lot of HDF5 from your `get_single_cell_experiment`, the display and manipulation is going to be slow. In addition, an `.rds` saved in this way is not portable: you will not be able @@ -215,7 +215,7 @@ single_cell_counts |> HDF5Array::saveHDF5SummarizedExperiment("single_cell_count We can gather all CD14 monocytes cells and plot the distribution of HLA-A across all tissues -```{r, results='hide'} +```{r, echo=TRUE} suppressPackageStartupMessages({ library(ggplot2) }) @@ -253,11 +253,13 @@ counts |> ggtitle("HLA-A in CD14 monocytes by disease") ``` -```{r echo=FALSE} +With the full metadata file, this looks like: + +```{r echo=FALSE, out.width="100%"} find_figure("HLA_A_disease_plot.png") |> knitr::include_graphics() ``` -```{r, results='hide'} +```{r} # Plot by tissue counts |> dplyr::with_groups(tissue_harmonised, ~ .x |> dplyr::mutate(median_count = median(`HLA.A`, rm.na=TRUE))) |> @@ -275,7 +277,10 @@ counts |> ggtitle("HLA-A in CD14 monocytes by tissue") + theme(legend.position = "none") ``` -```{r echo=FALSE} + +With the full metadata file, this looks like: + +```{r echo=FALSE, out.width="100%"} find_figure("HLA_A_tissue_plot.png") |> knitr::include_graphics() ``` From 7d0f3f99525cd7c39a02ad8a18e58ec75f0566f2 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 12 Jul 2023 10:37:05 +1000 Subject: [PATCH 08/85] Undo readme changes, force Matrix upgrade --- .github/workflows/check-bioc.yml | 7 + README.md | 391 ++++++++++++------------------- vignettes/Introduction.Rmd | 17 +- 3 files changed, 165 insertions(+), 250 deletions(-) diff --git a/.github/workflows/check-bioc.yml b/.github/workflows/check-bioc.yml index aa77daf..1d32f97 100755 --- a/.github/workflows/check-bioc.yml +++ b/.github/workflows/check-bioc.yml @@ -181,6 +181,13 @@ jobs: remotes::install_cran("rcmdcheck") BiocManager::install("BiocCheck") shell: Rscript {0} + + - name: Fix Matrix on MacOS + if: matrix.config.os == 'macOS-latest' + run: | + print(packageVersion("Matrix")) + install.packages("Matrix") + shell: Rscript {0} - name: Install BiocGenerics if: env.has_RUnit == 'true' diff --git a/README.md b/README.md index 6130c01..c4c8c58 100644 --- a/README.md +++ b/README.md @@ -45,16 +45,16 @@ metadata #> # Database: DuckDB 0.7.1 [unknown@Linux 3.10.0-1160.88.1.el7.x86_64:R 4.2.1/:memory:] #> cell_ sample_ cell_…¹ cell_…² confi…³ cell_…⁴ cell_…⁵ cell_…⁶ sampl…⁷ _samp…⁸ #> -#> 1 TTAT… 039c55… mature… immune… 5 gdt cd4 tem natura… 0c1d32… BPH340… -#> 2 GCTT… 039c55… mature… cd8 tem 3 cd8 tem cd8 tem effect… 0c1d32… BPH340… -#> 3 GTCA… 039c55… mature… immune… 5 cd8 tem cd8 tcm vd2 gd… 0c1d32… BPH340… -#> 4 AAGG… 039c55… mature… immune… 5 cd8 tem clp vd2 gd… 0c1d32… BPH340… -#> 5 ATTG… 039c55… mature… immune… 5 cd8 tem cd8 tcm vd2 gd… 0c1d32… BPH340… -#> 6 CTCG… 039c55… mature… immune… 5 cd8 tem cd8 tcm natura… 0c1d32… BPH340… -#> 7 CGTC… 07e649… mature… immune… 5 nk_cd5… clp natura… 3b5c6b… BPH340… -#> 8 ACGA… 176400… mature… immune… 5 nk pro… nk natura… 0ccffb… BPH511… -#> 9 TCAT… 176400… mature… immune… 5 cd4 pr… tregs t regu… 0ccffb… BPH511… -#> 10 GAGA… 176400… mature… immune… 5 nk pro… nk natura… 0ccffb… BPH511… +#> 1 8387… 7bd7b8… natura… immune… 5 cd8 tem gmp natura… 842ce7… Q59___… +#> 2 1768… 7bd7b8… natura… immune… 5 cd8 tem cd8 tcm natura… 842ce7… Q59___… +#> 3 6329… 7bd7b8… natura… immune… 5 cd8 tem clp termin… 842ce7… Q59___… +#> 4 5027… 7bd7b8… natura… immune… 5 cd8 tem clp natura… 842ce7… Q59___… +#> 5 7956… 7bd7b8… natura… immune… 5 cd8 tem clp natura… 842ce7… Q59___… +#> 6 4305… 7bd7b8… natura… immune… 5 cd8 tem clp termin… 842ce7… Q59___… +#> 7 2126… 933f96… natura… ilc 1 nk nk natura… c250bf… AML3__… +#> 8 3114… 933f96… natura… immune… 5 mait nk natura… c250bf… AML3__… +#> 9 1407… 933f96… natura… immune… 5 mait clp natura… c250bf… AML3__… +#> 10 2911… 933f96… natura… nk 5 nk clp natura… c250bf… AML3__… #> # … with more rows, 46 more variables: assay , #> # assay_ontology_term_id , file_id_db , #> # cell_type_ontology_term_id , development_stage , @@ -71,21 +71,20 @@ The `metadata` variable can then be re-used for all subsequent queries. ``` r metadata |> dplyr::distinct(tissue, file_id) -#> # Source: SQL [?? x 2] +#> # Source: SQL [10 x 2] #> # Database: DuckDB 0.7.1 [unknown@Linux 3.10.0-1160.88.1.el7.x86_64:R 4.2.1/:memory:] -#> tissue file_id -#> -#> 1 transition zone of prostate d0a2b5d0-ded0-4ed0-af22-a6dde2799513 -#> 2 peripheral zone of prostate d0a2b5d0-ded0-4ed0-af22-a6dde2799513 -#> 3 heart left ventricle 5775c8d8-e37e-40cd-94f4-8e78b05ca331 -#> 4 lung f0691786-c20a-4727-8d34-9db28b418c4e -#> 5 blood f0691786-c20a-4727-8d34-9db28b418c4e -#> 6 blood 60ba353d-236a-4b91-a0a2-a74669c2b55e -#> 7 bone marrow 1ff5cbda-4d41-4f50-8c7e-cbe4a90e38db -#> 8 kidney 63523aa3-0d04-4fc6-ac59-5cadd3e73a14 -#> 9 renal medulla 63523aa3-0d04-4fc6-ac59-5cadd3e73a14 -#> 10 cortex of kidney 63523aa3-0d04-4fc6-ac59-5cadd3e73a14 -#> # … with more rows +#> tissue file_id +#> +#> 1 bone marrow 1ff5cbda-4d41-4f50-8c7e-cbe4a90e38db +#> 2 lung parenchyma 6661ab3a-792a-4682-b58c-4afb98b2c016 +#> 3 respiratory airway 6661ab3a-792a-4682-b58c-4afb98b2c016 +#> 4 nose 6661ab3a-792a-4682-b58c-4afb98b2c016 +#> 5 renal pelvis dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 +#> 6 kidney dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 +#> 7 renal medulla dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 +#> 8 cortex of kidney dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 +#> 9 kidney blood vessel dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 +#> 10 lung a2796032-d015-40c4-b9db-835207e5bd5b ``` ## Download single-cell RNA sequencing counts @@ -110,19 +109,19 @@ single_cell_counts = single_cell_counts #> # A SingleCellExperiment-tibble abstraction: 1,571 × 57 -#> # [90mFeatures=36229 | Cells=1571 | Assays=counts[0m +#> # Features=36229 | Cells=1571 | Assays=counts #> .cell sample_ cell_…¹ cell_…² confi…³ cell_…⁴ cell_…⁵ cell_…⁶ sampl…⁷ X_sam…⁸ #> -#> 1 ACAC… 11a7dc… CD4-po… immune… 5 cd4 tcm plasma th1/th… 10b339… Donor_… +#> 1 AGCG… 11a7dc… CD4-po… cd4 th1 3 cd4 tcm cd8 t th1 10b339… Donor_… #> 2 TCAG… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tem th1/th… 10b339… Donor_… -#> 3 CAAG… 11a7dc… CD4-po… immune… 1 cd4 tcm cd4 tcm mait 10b339… Donor_… -#> 4 TTTA… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tcm th17 10b339… Donor_… -#> 5 AGCG… 11a7dc… CD4-po… cd4 th1 3 cd4 tcm cd8 t th1 10b339… Donor_… -#> 6 GTCA… 14a078… CD4-po… immune… 5 cd4 tcm cd4 tem mait 8f71c5… VUHD85… -#> 7 ACTT… 14a078… CD4-po… mait 5 mait cd8 tem mait 8f71c5… VUHD85… -#> 8 GATC… 14a078… CD4-po… immune… 5 cd4 tcm cd4 tem mait 8f71c5… VUHD85… -#> 9 TGTC… 14a078… CD4-po… termin… 3 cd4 tem cd8 tem termin… 8f71c5… VUHD85… -#> 10 GCGC… 14a078… CD4-po… cd4 th… 3 cd4 tcm cd4 t th17 8f71c5… VUHD85… +#> 3 TTTA… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tcm th17 10b339… Donor_… +#> 4 ACAC… 11a7dc… CD4-po… immune… 5 cd4 tcm plasma th1/th… 10b339… Donor_… +#> 5 CAAG… 11a7dc… CD4-po… immune… 1 cd4 tcm cd4 tcm mait 10b339… Donor_… +#> 6 CTGT… 14a078… CD4-po… cd4 th… 3 cd4 tcm cd4 tem th1/th… 8f71c5… VUHD85… +#> 7 ACGT… 14a078… CD4-po… treg 5 cd4 tcm tregs t regu… 8f71c5… VUHD85… +#> 8 CATA… 14a078… CD4-po… immune… 5 nk cd8 tem mait 8f71c5… VUHD85… +#> 9 ACTT… 14a078… CD4-po… mait 5 mait cd8 tem mait 8f71c5… VUHD85… +#> 10 TGCG… 14a078… CD4-po… cd4 th1 3 cd4 tcm cd4 tem th1 8f71c5… VUHD85… #> # … with 1,561 more rows, 47 more variables: assay , #> # assay_ontology_term_id , file_id_db , #> # cell_type_ontology_term_id , development_stage , @@ -155,19 +154,19 @@ single_cell_counts = single_cell_counts #> # A SingleCellExperiment-tibble abstraction: 1,571 × 57 -#> # [90mFeatures=36229 | Cells=1571 | Assays=cpm[0m +#> # Features=36229 | Cells=1571 | Assays=cpm #> .cell sample_ cell_…¹ cell_…² confi…³ cell_…⁴ cell_…⁵ cell_…⁶ sampl…⁷ X_sam…⁸ #> -#> 1 ACAC… 11a7dc… CD4-po… immune… 5 cd4 tcm plasma th1/th… 10b339… Donor_… +#> 1 AGCG… 11a7dc… CD4-po… cd4 th1 3 cd4 tcm cd8 t th1 10b339… Donor_… #> 2 TCAG… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tem th1/th… 10b339… Donor_… -#> 3 CAAG… 11a7dc… CD4-po… immune… 1 cd4 tcm cd4 tcm mait 10b339… Donor_… -#> 4 TTTA… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tcm th17 10b339… Donor_… -#> 5 AGCG… 11a7dc… CD4-po… cd4 th1 3 cd4 tcm cd8 t th1 10b339… Donor_… -#> 6 GTCA… 14a078… CD4-po… immune… 5 cd4 tcm cd4 tem mait 8f71c5… VUHD85… -#> 7 ACTT… 14a078… CD4-po… mait 5 mait cd8 tem mait 8f71c5… VUHD85… -#> 8 GATC… 14a078… CD4-po… immune… 5 cd4 tcm cd4 tem mait 8f71c5… VUHD85… -#> 9 TGTC… 14a078… CD4-po… termin… 3 cd4 tem cd8 tem termin… 8f71c5… VUHD85… -#> 10 GCGC… 14a078… CD4-po… cd4 th… 3 cd4 tcm cd4 t th17 8f71c5… VUHD85… +#> 3 TTTA… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tcm th17 10b339… Donor_… +#> 4 ACAC… 11a7dc… CD4-po… immune… 5 cd4 tcm plasma th1/th… 10b339… Donor_… +#> 5 CAAG… 11a7dc… CD4-po… immune… 1 cd4 tcm cd4 tcm mait 10b339… Donor_… +#> 6 CTGT… 14a078… CD4-po… cd4 th… 3 cd4 tcm cd4 tem th1/th… 8f71c5… VUHD85… +#> 7 ACGT… 14a078… CD4-po… treg 5 cd4 tcm tregs t regu… 8f71c5… VUHD85… +#> 8 CATA… 14a078… CD4-po… immune… 5 nk cd8 tem mait 8f71c5… VUHD85… +#> 9 ACTT… 14a078… CD4-po… mait 5 mait cd8 tem mait 8f71c5… VUHD85… +#> 10 TGCG… 14a078… CD4-po… cd4 th1 3 cd4 tcm cd4 tem th1 8f71c5… VUHD85… #> # … with 1,561 more rows, 47 more variables: assay , #> # assay_ontology_term_id , file_id_db , #> # cell_type_ontology_term_id , development_stage , @@ -197,19 +196,19 @@ single_cell_counts = single_cell_counts #> # A SingleCellExperiment-tibble abstraction: 1,571 × 57 -#> # [90mFeatures=1 | Cells=1571 | Assays=cpm[0m +#> # Features=1 | Cells=1571 | Assays=cpm #> .cell sample_ cell_…¹ cell_…² confi…³ cell_…⁴ cell_…⁵ cell_…⁶ sampl…⁷ X_sam…⁸ #> -#> 1 ACAC… 11a7dc… CD4-po… immune… 5 cd4 tcm plasma th1/th… 10b339… Donor_… +#> 1 AGCG… 11a7dc… CD4-po… cd4 th1 3 cd4 tcm cd8 t th1 10b339… Donor_… #> 2 TCAG… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tem th1/th… 10b339… Donor_… -#> 3 CAAG… 11a7dc… CD4-po… immune… 1 cd4 tcm cd4 tcm mait 10b339… Donor_… -#> 4 TTTA… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tcm th17 10b339… Donor_… -#> 5 AGCG… 11a7dc… CD4-po… cd4 th1 3 cd4 tcm cd8 t th1 10b339… Donor_… -#> 6 GTCA… 14a078… CD4-po… immune… 5 cd4 tcm cd4 tem mait 8f71c5… VUHD85… -#> 7 ACTT… 14a078… CD4-po… mait 5 mait cd8 tem mait 8f71c5… VUHD85… -#> 8 GATC… 14a078… CD4-po… immune… 5 cd4 tcm cd4 tem mait 8f71c5… VUHD85… -#> 9 TGTC… 14a078… CD4-po… termin… 3 cd4 tem cd8 tem termin… 8f71c5… VUHD85… -#> 10 GCGC… 14a078… CD4-po… cd4 th… 3 cd4 tcm cd4 t th17 8f71c5… VUHD85… +#> 3 TTTA… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tcm th17 10b339… Donor_… +#> 4 ACAC… 11a7dc… CD4-po… immune… 5 cd4 tcm plasma th1/th… 10b339… Donor_… +#> 5 CAAG… 11a7dc… CD4-po… immune… 1 cd4 tcm cd4 tcm mait 10b339… Donor_… +#> 6 CTGT… 14a078… CD4-po… cd4 th… 3 cd4 tcm cd4 tem th1/th… 8f71c5… VUHD85… +#> 7 ACGT… 14a078… CD4-po… treg 5 cd4 tcm tregs t regu… 8f71c5… VUHD85… +#> 8 CATA… 14a078… CD4-po… immune… 5 nk cd8 tem mait 8f71c5… VUHD85… +#> 9 ACTT… 14a078… CD4-po… mait 5 mait cd8 tem mait 8f71c5… VUHD85… +#> 10 TGCG… 14a078… CD4-po… cd4 th1 3 cd4 tcm cd4 tem th1 8f71c5… VUHD85… #> # … with 1,561 more rows, 47 more variables: assay , #> # assay_ontology_term_id , file_id_db , #> # cell_type_ontology_term_id , development_stage , @@ -254,7 +253,7 @@ The returned `SingleCellExperiment` can be saved with two modalities, as ### Saving as RDS (fast saving, slow reading) -Saving as `.rds` has the advantage of being fast, and the `.rds` file +Saving as `.rds` has the advantage of being fast, andd the `.rds` file occupies very little disk space as it only stores the links to the files in your cache. @@ -289,76 +288,35 @@ single_cell_counts |> HDF5Array::saveHDF5SummarizedExperiment("single_cell_count We can gather all CD14 monocytes cells and plot the distribution of HLA-A across all tissues -``` r -suppressPackageStartupMessages({ - library(ggplot2) -}) - -# Plots with styling -counts <- metadata |> - # Filter and subset - dplyr::filter(cell_type_harmonised == "cd14 mono") |> - dplyr::filter(file_id_db != "c5a05f23f9784a3be3bfa651198a48eb") |> - - # Get counts per million for HCA-A gene - get_single_cell_experiment(assays = "cpm", features = "HLA-A") |> - suppressMessages() |> - - # Add feature to table - tidySingleCellExperiment::join_features("HLA-A", shape = "wide") |> - - # Rank x axis - tibble::as_tibble() - -# Plot by disease -counts |> - dplyr::with_groups(disease, ~ .x |> dplyr::mutate(median_count = median(`HLA.A`, rm.na=TRUE))) |> - - # Plot - ggplot(aes(forcats::fct_reorder(disease, median_count,.desc = TRUE), `HLA.A`,color = file_id)) + - geom_jitter(shape=".") + - - # Style - guides(color="none") + - scale_y_log10() + - theme_bw() + - theme(axis.text.x = element_text(angle = 60, vjust = 1, hjust = 1)) + - xlab("Disease") + - ggtitle("HLA-A in CD14 monocytes by disease") -#> Warning: Transformation introduced infinite values in continuous y-axis -``` + #> ℹ Realising metadata. + #> ℹ Synchronising files + #> ℹ Downloading 0 files, totalling 0 GB + #> ℹ Reading files. + #> ℹ Compiling Single Cell Experiment. + #> Warning: Transformation introduced infinite values in continuous y-axis + #> Warning in min(x): no non-missing arguments to min; returning Inf + #> Warning in max(x): no non-missing arguments to max; returning -Inf ![](/stornext/Home/data/allstaff/m/milton.m/HCAquery/README_files/figure-gfm/unnamed-chunk-14-1.png) -With the full metadata file, this looks like: + #> Warning: Transformation introduced infinite values in continuous y-axis + #> Warning in min(x): no non-missing arguments to min; returning Inf + #> Warning in max(x): no non-missing arguments to max; returning -Inf - +![](/stornext/Home/data/allstaff/m/milton.m/HCAquery/README_files/figure-gfm/unnamed-chunk-14-2.png) ``` r -# Plot by tissue +library(tidySingleCellExperiment) +library(ggplot2) + counts |> - dplyr::with_groups(tissue_harmonised, ~ .x |> dplyr::mutate(median_count = median(`HLA.A`, rm.na=TRUE))) |> - - # Plot - ggplot(aes(forcats::fct_reorder(tissue_harmonised, median_count,.desc = TRUE), `HLA.A`,color = file_id)) + - geom_jitter(shape=".") + - - # Style - guides(color="none") + - scale_y_log10() + - theme_bw() + - theme(axis.text.x = element_text(angle = 60, vjust = 1, hjust = 1)) + - xlab("Tissue") + - ggtitle("HLA-A in CD14 monocytes by tissue") + - theme(legend.position = "none") -#> Warning: Transformation introduced infinite values in continuous y-axis + ggplot(aes( disease, `HLA.A`,color = file_id)) + + geom_jitter(shape=".") ``` -![](/stornext/Home/data/allstaff/m/milton.m/HCAquery/README_files/figure-gfm/unnamed-chunk-16-1.png) +![](/stornext/Home/data/allstaff/m/milton.m/HCAquery/README_files/figure-gfm/unnamed-chunk-15-1.png) -With the full metadata file, this looks like: - - +![](man/figures/HLA_A_disease_plot.png) ``` r metadata |> @@ -368,22 +326,21 @@ metadata |> # Get counts per million for HCA-A gene get_single_cell_experiment(assays = "cpm", features = "HLA-A") |> - suppressMessages() |> - # Plot + # Plot (styling code have been omitted) tidySingleCellExperiment::join_features("HLA-A", shape = "wide") |> - ggplot(aes(tissue_harmonised, `HLA.A`, color = file_id)) + - theme_bw() + - theme( - axis.text.x = element_text(angle = 60, vjust = 1, hjust = 1), - legend.position = "none" - ) + - geom_jitter(shape=".") + - xlab("Tissue") + - ggtitle("HLA-A in nk cells by tissue") + ggplot(aes(tissue_harmonised, `HLA.A`,color = file_id)) + + geom_jitter(shape=".") +#> ℹ Realising metadata. +#> ℹ Synchronising files +#> ℹ Downloading 0 files, totalling 0 GB +#> ℹ Reading files. +#> ℹ Compiling Single Cell Experiment. ``` -![](/stornext/Home/data/allstaff/m/milton.m/HCAquery/README_files/figure-gfm/unnamed-chunk-18-1.png) +![](/stornext/Home/data/allstaff/m/milton.m/HCAquery/README_files/figure-gfm/unnamed-chunk-17-1.png) + +![](man/figures/HLA_A_tissue_plot.png) ## Obtain Unharmonised Metadata @@ -398,13 +355,10 @@ data frame. harmonised <- metadata |> dplyr::filter(tissue == "kidney blood vessel") unharmonised <- get_unharmonised_metadata(harmonised) unharmonised -#> # A tibble: 4 × 2 +#> # A tibble: 1 × 2 #> file_id unharmonised #> -#> 1 63523aa3-0d04-4fc6-ac59-5cadd3e73a14 -#> 2 8fee7b82-178b-4c04-bf23-04689415690d -#> 3 dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 -#> 4 f7e94dbb-8638-4616-aaf9-16e2212c369f +#> 1 dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 ``` Notice that the columns differ between each dataset’s data frame: @@ -412,45 +366,25 @@ Notice that the columns differ between each dataset’s data frame: ``` r dplyr::pull(unharmonised) |> head(2) #> [[1]] -#> # Source: SQL [?? x 17] +#> # Source: SQL [?? x 14] #> # Database: DuckDB 0.7.1 [unknown@Linux 3.10.0-1160.88.1.el7.x86_64:R 4.2.1/:memory:] #> cell_ file_id donor…¹ donor…² libra…³ mappe…⁴ sampl…⁵ suspe…⁶ suspe…⁷ autho…⁸ #> -#> 1 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… -#> 2 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… -#> 3 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… -#> 4 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… -#> 5 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… -#> 6 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… -#> 7 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… -#> 8 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… -#> 9 4602… 63523a… 19 mon… 463181… 671785… GENCOD… 125234… cell c7485e… CD4 T … -#> 10 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… -#> # … with more rows, 7 more variables: cell_state , -#> # reported_diseases , Short_Sample , Project , -#> # Experiment , compartment , broad_celltype , and abbreviated +#> 1 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… +#> 2 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… +#> 3 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… +#> 4 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… +#> 5 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… +#> 6 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… +#> 7 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… +#> 8 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… +#> 9 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… +#> 10 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… +#> # … with more rows, 4 more variables: reported_diseases , +#> # Experiment , Project , broad_celltype , and abbreviated #> # variable names ¹​donor_age, ²​donor_uuid, ³​library_uuid, #> # ⁴​mapped_reference_annotation, ⁵​sample_uuid, ⁶​suspension_type, #> # ⁷​suspension_uuid, ⁸​author_cell_type -#> -#> [[2]] -#> # Source: SQL [?? x 12] -#> # Database: DuckDB 0.7.1 [unknown@Linux 3.10.0-1160.88.1.el7.x86_64:R 4.2.1/:memory:] -#> cell_ file_id orig.…¹ nCoun…² nFeat…³ seura…⁴ Project donor…⁵ compa…⁶ broad…⁷ -#> -#> 1 1069 8fee7b… 4602ST… 16082 3997 25 Experi… Wilms3 non_PT Pelvic… -#> 2 1214 8fee7b… 4602ST… 1037 606 25 Experi… Wilms3 non_PT Pelvic… -#> 3 2583 8fee7b… 4602ST… 3028 1361 25 Experi… Wilms3 non_PT Pelvic… -#> 4 2655 8fee7b… 4602ST… 1605 859 25 Experi… Wilms3 non_PT Pelvic… -#> 5 3609 8fee7b… 4602ST… 1144 682 25 Experi… Wilms3 non_PT Pelvic… -#> 6 3624 8fee7b… 4602ST… 1874 963 25 Experi… Wilms3 non_PT Pelvic… -#> 7 3946 8fee7b… 4602ST… 1296 755 25 Experi… Wilms3 non_PT Pelvic… -#> 8 5163 8fee7b… 4602ST… 11417 3255 25 Experi… Wilms3 non_PT Pelvic… -#> 9 5446 8fee7b… 4602ST… 1769 946 19 Experi… Wilms2 lympho… CD4 T … -#> 10 6275 8fee7b… 4602ST… 3750 1559 25 Experi… Wilms3 non_PT Pelvic… -#> # … with more rows, 2 more variables: author_cell_type , Sample , and -#> # abbreviated variable names ¹​orig.ident, ²​nCount_RNA, ³​nFeature_RNA, -#> # ⁴​seurat_clusters, ⁵​donor_id, ⁶​compartment, ⁷​broad_celltype ``` # Cell metadata @@ -534,82 +468,61 @@ sessionInfo() #> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C #> #> attached base packages: -#> [1] stats graphics grDevices utils datasets methods base +#> [1] stats4 stats graphics grDevices utils datasets methods +#> [8] base #> #> other attached packages: -#> [1] ggplot2_3.4.1 CuratedAtlasQueryR_0.99.1 +#> [1] tidySingleCellExperiment_1.6.3 SingleCellExperiment_1.18.1 +#> [3] SummarizedExperiment_1.26.1 Biobase_2.56.0 +#> [5] GenomicRanges_1.48.0 GenomeInfoDb_1.32.4 +#> [7] IRanges_2.30.1 S4Vectors_0.34.0 +#> [9] BiocGenerics_0.42.0 MatrixGenerics_1.8.1 +#> [11] matrixStats_0.63.0 ttservice_0.2.2 +#> [13] ggplot2_3.4.1 CuratedAtlasQueryR_0.99.1 #> #> loaded via a namespace (and not attached): -#> [1] plyr_1.8.8 igraph_1.4.1 -#> [3] lazyeval_0.2.2 sp_1.5-1 -#> [5] splines_4.2.1 listenv_0.9.0 -#> [7] scattermore_0.8 GenomeInfoDb_1.32.4 -#> [9] digest_0.6.31 htmltools_0.5.4 -#> [11] fansi_1.0.3 magrittr_2.0.3 -#> [13] tensor_1.5 cluster_2.1.3 -#> [15] ROCR_1.0-11 globals_0.16.2 -#> [17] matrixStats_0.63.0 duckdb_0.7.1-1 -#> [19] spatstat.sparse_3.0-0 colorspace_2.0-3 -#> [21] blob_1.2.3 ggrepel_0.9.2 -#> [23] ttservice_0.2.2 xfun_0.36 -#> [25] dplyr_1.1.0 RCurl_1.98-1.9 -#> [27] jsonlite_1.8.4 progressr_0.13.0 -#> [29] spatstat.data_3.0-0 survival_3.3-1 -#> [31] zoo_1.8-11 glue_1.6.2 -#> [33] polyclip_1.10-4 gtable_0.3.1 -#> [35] zlibbioc_1.42.0 XVector_0.36.0 -#> [37] leiden_0.4.3 DelayedArray_0.22.0 -#> [39] tidySingleCellExperiment_1.6.3 Rhdf5lib_1.18.2 -#> [41] future.apply_1.10.0 SingleCellExperiment_1.18.1 -#> [43] BiocGenerics_0.42.0 HDF5Array_1.24.2 -#> [45] abind_1.4-5 scales_1.2.1 -#> [47] DBI_1.1.3 spatstat.random_3.0-1 -#> [49] miniUI_0.1.1.1 Rcpp_1.0.10 -#> [51] viridisLite_0.4.1 xtable_1.8-4 -#> [53] reticulate_1.26 stats4_4.2.1 -#> [55] htmlwidgets_1.6.0 httr_1.4.4 -#> [57] RColorBrewer_1.1-3 ellipsis_0.3.2 -#> [59] Seurat_4.3.0 ica_1.0-3 -#> [61] farver_2.1.1 pkgconfig_2.0.3 -#> [63] dbplyr_2.3.0 sass_0.4.4 -#> [65] uwot_0.1.14 deldir_1.0-6 -#> [67] utf8_1.2.2 labeling_0.4.2 -#> [69] tidyselect_1.2.0 rlang_1.0.6 -#> [71] reshape2_1.4.4 later_1.3.0 -#> [73] munsell_0.5.0 tools_4.2.1 -#> [75] cachem_1.0.6 cli_3.6.0 -#> [77] generics_0.1.3 ggridges_0.5.4 -#> [79] evaluate_0.19 stringr_1.5.0 -#> [81] fastmap_1.1.0 yaml_2.3.6 -#> [83] goftest_1.2-3 knitr_1.42 -#> [85] fitdistrplus_1.1-8 purrr_1.0.1 -#> [87] RANN_2.6.1 pbapply_1.6-0 -#> [89] future_1.30.0 nlme_3.1-157 -#> [91] mime_0.12 compiler_4.2.1 -#> [93] rstudioapi_0.14 plotly_4.10.1 -#> [95] png_0.1-8 spatstat.utils_3.0-1 -#> [97] tibble_3.1.8 bslib_0.4.2 -#> [99] stringi_1.7.12 highr_0.10 -#> [101] forcats_1.0.0 lattice_0.20-45 -#> [103] Matrix_1.5-3 vctrs_0.5.2 -#> [105] pillar_1.8.1 lifecycle_1.0.3 -#> [107] rhdf5filters_1.8.0 spatstat.geom_3.0-3 -#> [109] lmtest_0.9-40 jquerylib_0.1.4 -#> [111] RcppAnnoy_0.0.20 data.table_1.14.6 -#> [113] cowplot_1.1.1 bitops_1.0-7 -#> [115] irlba_2.3.5.1 httpuv_1.6.7 -#> [117] patchwork_1.1.2 GenomicRanges_1.48.0 -#> [119] R6_2.5.1 promises_1.2.0.1 -#> [121] KernSmooth_2.23-20 gridExtra_2.3 -#> [123] IRanges_2.30.1 parallelly_1.33.0 -#> [125] codetools_0.2-18 assertthat_0.2.1 -#> [127] MASS_7.3-57 rhdf5_2.40.0 -#> [129] SummarizedExperiment_1.26.1 rprojroot_2.0.3 -#> [131] withr_2.5.0 SeuratObject_4.1.3 -#> [133] sctransform_0.3.5 S4Vectors_0.34.0 -#> [135] GenomeInfoDbData_1.2.8 parallel_4.2.1 -#> [137] grid_4.2.1 tidyr_1.3.0 -#> [139] rmarkdown_2.20 MatrixGenerics_1.8.1 -#> [141] Rtsne_0.16 spatstat.explore_3.0-5 -#> [143] Biobase_2.56.0 shiny_1.7.4 +#> [1] plyr_1.8.8 igraph_1.4.1 lazyeval_0.2.2 +#> [4] sp_1.5-1 splines_4.2.1 listenv_0.9.0 +#> [7] scattermore_0.8 digest_0.6.31 htmltools_0.5.4 +#> [10] fansi_1.0.3 magrittr_2.0.3 tensor_1.5 +#> [13] cluster_2.1.3 ROCR_1.0-11 globals_0.16.2 +#> [16] duckdb_0.7.1-1 spatstat.sparse_3.0-0 colorspace_2.0-3 +#> [19] blob_1.2.3 ggrepel_0.9.2 xfun_0.36 +#> [22] dplyr_1.1.0 RCurl_1.98-1.9 jsonlite_1.8.4 +#> [25] progressr_0.13.0 spatstat.data_3.0-0 survival_3.3-1 +#> [28] zoo_1.8-11 glue_1.6.2 polyclip_1.10-4 +#> [31] gtable_0.3.1 zlibbioc_1.42.0 XVector_0.36.0 +#> [34] leiden_0.4.3 DelayedArray_0.22.0 Rhdf5lib_1.18.2 +#> [37] future.apply_1.10.0 HDF5Array_1.24.2 abind_1.4-5 +#> [40] scales_1.2.1 DBI_1.1.3 spatstat.random_3.0-1 +#> [43] miniUI_0.1.1.1 Rcpp_1.0.10 viridisLite_0.4.1 +#> [46] xtable_1.8-4 reticulate_1.26 htmlwidgets_1.6.0 +#> [49] httr_1.4.4 RColorBrewer_1.1-3 ellipsis_0.3.2 +#> [52] Seurat_4.3.0 ica_1.0-3 farver_2.1.1 +#> [55] pkgconfig_2.0.3 dbplyr_2.3.0 sass_0.4.4 +#> [58] uwot_0.1.14 deldir_1.0-6 utf8_1.2.2 +#> [61] labeling_0.4.2 tidyselect_1.2.0 rlang_1.0.6 +#> [64] reshape2_1.4.4 later_1.3.0 munsell_0.5.0 +#> [67] tools_4.2.1 cachem_1.0.6 cli_3.6.0 +#> [70] generics_0.1.3 ggridges_0.5.4 evaluate_0.19 +#> [73] stringr_1.5.0 fastmap_1.1.0 yaml_2.3.6 +#> [76] goftest_1.2-3 knitr_1.42 fitdistrplus_1.1-8 +#> [79] purrr_1.0.1 RANN_2.6.1 pbapply_1.6-0 +#> [82] future_1.30.0 nlme_3.1-157 mime_0.12 +#> [85] compiler_4.2.1 rstudioapi_0.14 curl_4.3.3 +#> [88] plotly_4.10.1 png_0.1-8 spatstat.utils_3.0-1 +#> [91] tibble_3.1.8 bslib_0.4.2 stringi_1.7.12 +#> [94] highr_0.10 forcats_1.0.0 lattice_0.20-45 +#> [97] Matrix_1.5-3 vctrs_0.5.2 pillar_1.8.1 +#> [100] lifecycle_1.0.3 rhdf5filters_1.8.0 spatstat.geom_3.0-3 +#> [103] lmtest_0.9-40 jquerylib_0.1.4 RcppAnnoy_0.0.20 +#> [106] data.table_1.14.6 cowplot_1.1.1 bitops_1.0-7 +#> [109] irlba_2.3.5.1 httpuv_1.6.7 patchwork_1.1.2 +#> [112] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20 +#> [115] gridExtra_2.3 parallelly_1.33.0 codetools_0.2-18 +#> [118] assertthat_0.2.1 MASS_7.3-57 rhdf5_2.40.0 +#> [121] rprojroot_2.0.3 withr_2.5.0 SeuratObject_4.1.3 +#> [124] sctransform_0.3.5 GenomeInfoDbData_1.2.8 parallel_4.2.1 +#> [127] grid_4.2.1 tidyr_1.3.0 rmarkdown_2.20 +#> [130] Rtsne_0.16 spatstat.explore_3.0-5 shiny_1.7.4 ``` diff --git a/vignettes/Introduction.Rmd b/vignettes/Introduction.Rmd index ceca9a1..dbba7f5 100644 --- a/vignettes/Introduction.Rmd +++ b/vignettes/Introduction.Rmd @@ -14,7 +14,7 @@ knit: > x, output_format = "html_document", params = list( - demo_metadata = FALSE + demo_metadata = TRUE ) ) rmarkdown::render( @@ -189,7 +189,7 @@ The returned `SingleCellExperiment` can be saved with two modalities, as `.rds` ### Saving as RDS (fast saving, slow reading) -Saving as `.rds` has the advantage of being fast, and the `.rds` file occupies very little disk space as it only stores the links to the files in your cache. +Saving as `.rds` has the advantage of being fast, andd the `.rds` file occupies very little disk space as it only stores the links to the files in your cache. However it has the disadvantage that for big `SingleCellExperiment` objects, which merge a lot of HDF5 from your `get_single_cell_experiment`, the display and manipulation is going to be slow. In addition, an `.rds` saved in this way is not portable: you will not be able @@ -215,7 +215,7 @@ single_cell_counts |> HDF5Array::saveHDF5SummarizedExperiment("single_cell_count We can gather all CD14 monocytes cells and plot the distribution of HLA-A across all tissues -```{r, echo=TRUE} +```{r, results='hide'} suppressPackageStartupMessages({ library(ggplot2) }) @@ -253,13 +253,11 @@ counts |> ggtitle("HLA-A in CD14 monocytes by disease") ``` -With the full metadata file, this looks like: - -```{r echo=FALSE, out.width="100%"} +```{r echo=FALSE} find_figure("HLA_A_disease_plot.png") |> knitr::include_graphics() ``` -```{r} +```{r, results='hide'} # Plot by tissue counts |> dplyr::with_groups(tissue_harmonised, ~ .x |> dplyr::mutate(median_count = median(`HLA.A`, rm.na=TRUE))) |> @@ -277,10 +275,7 @@ counts |> ggtitle("HLA-A in CD14 monocytes by tissue") + theme(legend.position = "none") ``` - -With the full metadata file, this looks like: - -```{r echo=FALSE, out.width="100%"} +```{r echo=FALSE} find_figure("HLA_A_tissue_plot.png") |> knitr::include_graphics() ``` From ddbdc3c236efceadd24d5053adcc0fe7ee4d79a4 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 12 Jul 2023 11:26:13 +1000 Subject: [PATCH 09/85] Reinstall Seurat from source --- .github/workflows/check-bioc.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/check-bioc.yml b/.github/workflows/check-bioc.yml index 1d32f97..27b36bf 100755 --- a/.github/workflows/check-bioc.yml +++ b/.github/workflows/check-bioc.yml @@ -185,8 +185,7 @@ jobs: - name: Fix Matrix on MacOS if: matrix.config.os == 'macOS-latest' run: | - print(packageVersion("Matrix")) - install.packages("Matrix") + install.packages(c("Seurat", "SeuratObject"), type="source") shell: Rscript {0} - name: Install BiocGenerics From e915a751e4ea7263463c49c025229618408b5fb4 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 12 Jul 2023 12:01:32 +1000 Subject: [PATCH 10/85] Revert DESCRIPTION change --- DESCRIPTION | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 0eff9fa..77336b5 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Type: Package Package: CuratedAtlasQueryR Title: Queries the Human Cell Atlas -Version: 0.99.5 +Version: 0.99.4 Authors@R: c( person( "Stefano", @@ -93,8 +93,7 @@ Imports: utils, dbplyr (>= 2.3.0), duckdb, - stringr, - Matrix (>= 1.5.3) + stringr Suggests: zellkonverter, rmarkdown, From 2246216c4feeb030dfdeeaec21add11f503bb398 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 12 Jul 2023 12:13:14 +1000 Subject: [PATCH 11/85] Explain the workflow hack --- .github/workflows/check-bioc.yml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/.github/workflows/check-bioc.yml b/.github/workflows/check-bioc.yml index 27b36bf..fa3d49e 100755 --- a/.github/workflows/check-bioc.yml +++ b/.github/workflows/check-bioc.yml @@ -182,7 +182,10 @@ jobs: BiocManager::install("BiocCheck") shell: Rscript {0} - - name: Fix Matrix on MacOS + # This is a hack to fix this bug: https://stackoverflow.com/q/73700130/2148718 + # Basically the Seurat and SeuratObject binary packages are compiled against an + # older version of Matrix, which causes an error we can fix by re-compiling from source + - name: Fix Matrix on MacOS. if: matrix.config.os == 'macOS-latest' run: | install.packages(c("Seurat", "SeuratObject"), type="source") From b9c8f3dd27b49fc601bcd050bc159ef6cec45188 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 12 Jul 2023 13:07:21 +1000 Subject: [PATCH 12/85] Bump version --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 77336b5..4d10ff5 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Type: Package Package: CuratedAtlasQueryR Title: Queries the Human Cell Atlas -Version: 0.99.4 +Version: 0.99.5 Authors@R: c( person( "Stefano", From 1680b2c8ac00bdf8e5810ca2f70b9dabb6f2175b Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 12 Jul 2023 13:25:21 +1000 Subject: [PATCH 13/85] Don't document COUNTS_URL --- R/counts.R | 1 + man/COUNTS_URL.Rd | 16 ---------------- 2 files changed, 1 insertion(+), 16 deletions(-) delete mode 100644 man/COUNTS_URL.Rd diff --git a/R/counts.R b/R/counts.R index 6736001..f8c9a93 100644 --- a/R/counts.R +++ b/R/counts.R @@ -14,6 +14,7 @@ assay_map <- c( ) #' Base URL pointing to the count data at the current version +#' @noRd COUNTS_URL <- single_line_str( "https://object-store.rc.nectar.org.au/v1/ AUTH_06d6e008e3e642da99d806ba3ea629c5/cellxgene-0.2.1-hdf5" diff --git a/man/COUNTS_URL.Rd b/man/COUNTS_URL.Rd deleted file mode 100644 index 70a92c0..0000000 --- a/man/COUNTS_URL.Rd +++ /dev/null @@ -1,16 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/counts.R -\docType{data} -\name{COUNTS_URL} -\alias{COUNTS_URL} -\title{Base URL pointing to the count data at the current version} -\format{ -An object of class \code{character} of length 1. -} -\usage{ -COUNTS_URL -} -\description{ -Base URL pointing to the count data at the current version -} -\keyword{datasets} From 3671b812deafed5a39d75505be712f23ff9ea7f2 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Wed, 12 Jul 2023 13:26:31 +1000 Subject: [PATCH 14/85] Bump version --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 4d10ff5..6b6b6f6 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Type: Package Package: CuratedAtlasQueryR Title: Queries the Human Cell Atlas -Version: 0.99.5 +Version: 0.99.6 Authors@R: c( person( "Stefano", From 21480068a3ee6330602535550e14d5e918ebc894 Mon Sep 17 00:00:00 2001 From: Stefano Mangiola Date: Tue, 17 Oct 2023 03:52:53 +1100 Subject: [PATCH 15/85] Update check-bioc.yml --- .github/workflows/check-bioc.yml | 173 ++++++++++++++++++++----------- 1 file changed, 113 insertions(+), 60 deletions(-) diff --git a/.github/workflows/check-bioc.yml b/.github/workflows/check-bioc.yml index fa3d49e..ba15358 100755 --- a/.github/workflows/check-bioc.yml +++ b/.github/workflows/check-bioc.yml @@ -38,7 +38,8 @@ env: run_covr: 'false' run_pkgdown: 'true' has_RUnit: 'false' - cache-version: 'cache-v2' + cache-version: 'cache-v1' + run_docker: 'false' jobs: build-check: @@ -48,12 +49,14 @@ jobs: ## Environment variables unique to this job. strategy: - fail-fast: true + fail-fast: false matrix: config: - - { os: ubuntu-latest, r: '4.2', bioc: '3.16', cont: "bioconductor/bioconductor_docker:RELEASE_3_16", rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest" } - - { os: macOS-latest, r: '4.2', bioc: '3.16'} - - { os: windows-latest, r: '4.2', bioc: '3.16'} + - { os: ubuntu-latest, r: '4.3', bioc: '3.18', cont: "bioconductor/bioconductor_docker:devel", rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest" } + - { os: macOS-latest, r: '4.3', bioc: '3.18'} + - { os: windows-latest, r: '4.3', bioc: '3.18'} + ## Check https://github.com/r-lib/actions/tree/master/examples + ## for examples using the http-user-agent env: R_REMOTES_NO_ERRORS_FROM_WARNINGS: true RSPM: ${{ matrix.config.rspm }} @@ -76,7 +79,7 @@ jobs: ## https://github.com/r-lib/actions/blob/master/examples/check-standard.yaml ## If they update their steps, we will also need to update ours. - name: Checkout Repository - uses: actions/checkout@v2 + uses: actions/checkout@v3 ## R is already included in the Bioconductor docker images - name: Setup R from r-lib @@ -84,6 +87,7 @@ jobs: uses: r-lib/actions/setup-r@v2 with: r-version: ${{ matrix.config.r }} + http-user-agent: ${{ matrix.config.http-user-agent }} ## pandoc is already included in the Bioconductor docker images - name: Setup pandoc from r-lib @@ -96,26 +100,28 @@ jobs: saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2) shell: Rscript {0} - - name: Cache R packages + - name: Restore R package cache if: "!contains(github.event.head_commit.message, '/nocache') && runner.os != 'Linux'" - uses: actions/cache@v2 + uses: actions/cache@v3 with: path: ${{ env.R_LIBS_USER }} - key: "${{ env.cache-version }}-${{ runner.os }}-biocversion-RELEASE_${{ matrix.config.bioc }}r-{{ matrix.config.r }}" + key: ${{ env.cache-version }}-${{ runner.os }}-biocversion-devel-r-4.3-${{ hashFiles('.github/depends.Rds') }} + restore-keys: ${{ env.cache-version }}-${{ runner.os }}-biocversion-devel-r-4.3- - name: Cache R packages on Linux if: "!contains(github.event.head_commit.message, '/nocache') && runner.os == 'Linux' " - uses: actions/cache@v2 + uses: actions/cache@v3 with: path: /home/runner/work/_temp/Library - key: "${{ env.cache-version }}-${{ runner.os }}-biocversion-RELEASE_${{ matrix.config.bioc }}r-{{ matrix.config.r }}" + key: ${{ env.cache-version }}-${{ runner.os }}-biocversion-devel-r-4.3-${{ hashFiles('.github/depends.Rds') }} + restore-keys: ${{ env.cache-version }}-${{ runner.os }}-biocversion-devel-r-4.3- - - name: Install Linux system dependencies - if: runner.os == 'Linux' - run: | - sysreqs=$(Rscript -e 'cat("apt-get update -y && apt-get install -y", paste(gsub("apt-get install -y ", "", remotes::system_requirements("ubuntu", "20.04")), collapse = " "))') - echo $sysreqs - sudo -s eval "$sysreqs" + # - name: Install Linux system dependencies + # if: runner.os == 'Linux' + # run: | + # sysreqs=$(Rscript -e 'cat("apt-get update -y && apt-get install -y", paste(gsub("apt-get install -y ", "", remotes::system_requirements("ubuntu", "20.04")), collapse = " "))') + # echo $sysreqs + # sudo -s eval "$sysreqs" - name: Install macOS system dependencies if: matrix.config.os == 'macOS-latest' @@ -133,10 +139,9 @@ jobs: ## For installing usethis's dependency gert brew install libgit2 - - ## To fix x11/cairo error with tidyHeatmap/Complexheatmap here https://github.com/stemangiola/tidybulk/runs/1388237421?check_suite_focus=true#step:14:2134 - ## Suggested here https://stackoverflow.com/questions/63648591/how-to-install-x11-before-testing-with-github-actions-for-macos - brew install --cask xquartz + + ## Required for tcltk + brew install xquartz --cask - name: Install Windows system dependencies if: runner.os == 'Windows' @@ -147,12 +152,12 @@ jobs: - name: Install BiocManager run: | message(paste('****', Sys.time(), 'installing BiocManager ****')) - remotes::install_github("Bioconductor/BiocManager") + remotes::install_cran("BiocManager") shell: Rscript {0} - name: Set BiocVersion run: | - BiocManager::install(version = "${{ matrix.config.bioc }}", ask = FALSE) + BiocManager::install(version = "${{ matrix.config.bioc }}", ask = FALSE, force = TRUE) shell: Rscript {0} - name: Install dependencies pass 1 @@ -164,6 +169,10 @@ jobs: ## https://github.com/r-lib/remotes/issues/296 ## Ideally, all dependencies should get installed in the first pass. + ## For running the checks + message(paste('****', Sys.time(), 'installing rcmdcheck and BiocCheck ****')) + install.packages(c("rcmdcheck", "BiocCheck"), repos = BiocManager::repositories()) + ## Pass #1 at installing dependencies message(paste('****', Sys.time(), 'pass number 1 at installing dependencies: local dependencies ****')) remotes::install_local(dependencies = TRUE, repos = BiocManager::repositories(), build_vignettes = FALSE, upgrade = TRUE) @@ -174,21 +183,7 @@ jobs: run: | ## Pass #2 at installing dependencies message(paste('****', Sys.time(), 'pass number 2 at installing dependencies: any remaining dependencies ****')) - remotes::install_local(dependencies = TRUE, repos = BiocManager::repositories(), build_vignettes = FALSE, upgrade = TRUE) - - ## For running the checks - message(paste('****', Sys.time(), 'installing rcmdcheck and BiocCheck ****')) - remotes::install_cran("rcmdcheck") - BiocManager::install("BiocCheck") - shell: Rscript {0} - - # This is a hack to fix this bug: https://stackoverflow.com/q/73700130/2148718 - # Basically the Seurat and SeuratObject binary packages are compiled against an - # older version of Matrix, which causes an error we can fix by re-compiling from source - - name: Fix Matrix on MacOS. - if: matrix.config.os == 'macOS-latest' - run: | - install.packages(c("Seurat", "SeuratObject"), type="source") + remotes::install_local(dependencies = TRUE, repos = BiocManager::repositories(), build_vignettes = TRUE, upgrade = TRUE, force = TRUE) shell: Rscript {0} - name: Install BiocGenerics @@ -199,15 +194,15 @@ jobs: shell: Rscript {0} - name: Install covr - if: github.ref == 'refs/heads/master' && env.run_covr == 'true' && runner.os == 'Linux' + if: github.ref == 'refs/heads/devel' && env.run_covr == 'true' && runner.os == 'Linux' run: | remotes::install_cran("covr") shell: Rscript {0} - name: Install pkgdown - if: github.ref == 'refs/heads/master' && env.run_pkgdown == 'true' && runner.os == 'Linux' + if: github.ref == 'refs/heads/devel' && env.run_pkgdown == 'true' && runner.os == 'Linux' run: | - remotes::install_cran("pkgdown") + remotes::install_github("r-lib/pkgdown") shell: Rscript {0} - name: Session info @@ -220,10 +215,12 @@ jobs: - name: Run CMD check env: _R_CHECK_CRAN_INCOMING_: false + DISPLAY: 99.0 run: | + options(crayon.enabled = TRUE) rcmdcheck::rcmdcheck( - args = c("--no-manual", "--timings"), - build_args = c("--no-manual", "--no-resave-data"), + args = c("--no-manual", "--no-vignettes", "--timings"), + build_args = c("--no-manual", "--keep-empty-dirs", "--no-resave-data"), error_on = "warning", check_dir = "check" ) @@ -241,45 +238,101 @@ jobs: shell: Rscript {0} - name: Run BiocCheck + env: + DISPLAY: 99.0 run: | BiocCheck::BiocCheck( dir('check', 'tar.gz$', full.names = TRUE), `quit-with-status` = TRUE, `no-check-R-ver` = TRUE, - `no-check-bioc-help` = TRUE, - `new-package` = TRUE + `no-check-bioc-help` = TRUE ) shell: Rscript {0} - name: Test coverage - if: github.ref == 'refs/heads/master' && env.run_covr == 'true' && runner.os == 'Linux' + if: github.ref == 'refs/heads/devel' && env.run_covr == 'true' && runner.os == 'Linux' run: | covr::codecov() shell: Rscript {0} - name: Install package - if: github.ref == 'refs/heads/master' && env.run_pkgdown == 'true' && runner.os == 'Linux' + if: github.ref == 'refs/heads/devel' && env.run_pkgdown == 'true' && runner.os == 'Linux' run: R CMD INSTALL . - - name: Deploy package - if: github.ref == 'refs/heads/master' && env.run_pkgdown == 'true' && runner.os == 'Linux' - run: | - ## Temporary workaround for https://github.com/actions/checkout/issues/766 - git config --global --add safe.directory "$GITHUB_WORKSPACE" - - git config --local user.email "actions@github.com" - git config --local user.name "GitHub Actions" - Rscript -e "pkgdown::deploy_to_branch(new_process = FALSE)" - shell: bash {0} + - name: Build pkgdown site + if: github.ref == 'refs/heads/devel' && env.run_pkgdown == 'true' && runner.os == 'Linux' + run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE) + shell: Rscript {0} ## Note that you need to run pkgdown::deploy_to_branch(new_process = FALSE) ## at least one locally before this will work. This creates the gh-pages ## branch (erasing anything you haven't version controlled!) and ## makes the git history recognizable by pkgdown. + - name: Install deploy dependencies + if: github.ref == 'refs/heads/devel' && env.run_pkgdown == 'true' && runner.os == 'Linux' + run: | + apt-get update && apt-get -y install rsync + + - name: Deploy pkgdown site to GitHub pages 🚀 + if: github.ref == 'refs/heads/devel' && env.run_pkgdown == 'true' && runner.os == 'Linux' + uses: JamesIves/github-pages-deploy-action@releases/v4 + with: + clean: false + branch: gh-pages + folder: docs + - name: Upload check results if: failure() - uses: actions/upload-artifact@v2 + uses: actions/upload-artifact@master with: - name: ${{ runner.os }}-biocversion-RELEASE_3_15-r-4.2-results + name: ${{ runner.os }}-biocversion-devel-r-4.3-results path: check - \ No newline at end of file + + + ## Code adapted from + ## https://github.com/waldronlab/cBioPortalData/blob/e0440a4445f0cc731e426363a76faa22ee5e0f9d/.github/workflows/devel_check_dock.yml#L65-L92 + docker-build-and-push: + runs-on: ubuntu-latest + needs: build-check + steps: + - name: Checkout Repository + if: "!contains(github.event.head_commit.message, '/nodocker') && env.run_docker == 'true' && github.ref == 'refs/heads/devel'" + uses: actions/checkout@v3 + + - name: Register repo name + if: "!contains(github.event.head_commit.message, '/nodocker') && env.run_docker == 'true' && github.ref == 'refs/heads/devel'" + id: reg_repo_name + run: | + echo CONT_IMG_NAME=$(echo ${{ github.event.repository.name }} | tr '[:upper:]' '[:lower:]') >> $GITHUB_ENV + + - name: Set up QEMU + if: "!contains(github.event.head_commit.message, '/nodocker') && env.run_docker == 'true' && github.ref == 'refs/heads/devel'" + uses: docker/setup-qemu-action@v2 + + - name: Set up Docker Buildx + if: "!contains(github.event.head_commit.message, '/nodocker') && env.run_docker == 'true' && github.ref == 'refs/heads/devel'" + uses: docker/setup-buildx-action@v2 + + - name: Login to Docker Hub + if: "!contains(github.event.head_commit.message, '/nodocker') && env.run_docker == 'true' && github.ref == 'refs/heads/devel'" + uses: docker/login-action@v2 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_TOKEN }} + ## Note that DOCKERHUB_TOKEN is really a token for your dockerhub + ## account, not your actual dockerhub account password. You can get it + ## from https://hub.docker.com/settings/security. + ## Check https://github.com/docker/build-push-action/tree/v4.0.0 + ## for more details. + ## Alternatively, try checking + ## https://seandavi.github.io/BuildABiocWorkshop/articles/HOWTO_BUILD_WORKSHOP.html. + + - name: Build and Push Docker + if: "!contains(github.event.head_commit.message, '/nodocker') && env.run_docker == 'true' && github.ref == 'refs/heads/devel' && success()" + uses: docker/build-push-action@v4 + with: + context: . + push: true + tags: > + ${{ secrets.DOCKERHUB_USERNAME }}/${{ env.CONT_IMG_NAME }}:latest, + ${{ secrets.DOCKERHUB_USERNAME }}/${{ env.CONT_IMG_NAME }}:devel From 15a0dd3604b01934a5d284fd4aad49390dfcd4a4 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Tue, 17 Oct 2023 17:41:09 +1100 Subject: [PATCH 16/85] Don't bioccheck for a new package --- .github/workflows/check-bioc.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/check-bioc.yml b/.github/workflows/check-bioc.yml index fa3d49e..2a0c0ce 100755 --- a/.github/workflows/check-bioc.yml +++ b/.github/workflows/check-bioc.yml @@ -247,7 +247,6 @@ jobs: `quit-with-status` = TRUE, `no-check-R-ver` = TRUE, `no-check-bioc-help` = TRUE, - `new-package` = TRUE ) shell: Rscript {0} From 7684dc6b96a91c8549204db665038a5ae7f9b9f9 Mon Sep 17 00:00:00 2001 From: J Wokaty Date: Tue, 24 Oct 2023 11:50:09 -0400 Subject: [PATCH 17/85] bump x.y.z version to even y prior to creation of RELEASE_3_18 branch --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 6b6b6f6..d874a3a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Type: Package Package: CuratedAtlasQueryR Title: Queries the Human Cell Atlas -Version: 0.99.6 +Version: 1.0.0 Authors@R: c( person( "Stefano", From 85f6c77cdc7afbb4a62c39a479c86732619bf6bf Mon Sep 17 00:00:00 2001 From: J Wokaty Date: Tue, 24 Oct 2023 11:50:09 -0400 Subject: [PATCH 18/85] bump x.y.z version to odd y following creation of RELEASE_3_18 branch --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index d874a3a..e0799c1 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Type: Package Package: CuratedAtlasQueryR Title: Queries the Human Cell Atlas -Version: 1.0.0 +Version: 1.1.0 Authors@R: c( person( "Stefano", From 1103a4b0c531bbdb5d57937976e5404743e01ede Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 16 Nov 2023 13:45:44 +1100 Subject: [PATCH 19/85] Fix "Binder Error: Referenced table not found!" --- R/metadata.R | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/R/metadata.R b/R/metadata.R index ba5d8c1..5f87f85 100644 --- a/R/metadata.R +++ b/R/metadata.R @@ -64,9 +64,11 @@ SAMPLE_DATABASE_URL <- single_line_str( #' #' @importFrom DBI dbConnect #' @importFrom duckdb duckdb +#' @importFrom dbplyr sql #' @importFrom dplyr tbl #' @importFrom httr progress #' @importFrom cli cli_alert_info hash_sha256 +#' @importFrom glue glue #' #' @details #' @@ -162,10 +164,13 @@ get_metadata <- function( progress(type = "down", con = stderr()) ) } - + + # Since dbplyr 2.4.0, raw file paths aren't handled very well + # See: https://github.com/duckdb/duckdb-r/issues/38 + select <- glue("FROM read_parquet('{db_path}')") |> sql() table <- duckdb() |> dbConnect(drv = _, read_only = TRUE) |> - tbl(db_path) + tbl(select) cache$metadata_table[[hash]] <- table table } From 6ddfb2290781a72351665349d7554d22ae9c0a24 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 16 Nov 2023 13:58:39 +1100 Subject: [PATCH 20/85] Rebuild documentation --- NAMESPACE | 1 + 1 file changed, 1 insertion(+) diff --git a/NAMESPACE b/NAMESPACE index b49dce3..eef68d0 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -30,6 +30,7 @@ importFrom(cli,cli_alert_success) importFrom(cli,cli_alert_warning) importFrom(cli,hash_sha256) importFrom(dbplyr,remote_con) +importFrom(dbplyr,sql) importFrom(dplyr,as_tibble) importFrom(dplyr,collect) importFrom(dplyr,filter) From 0df081c8fb4c5b6403903f368d329204a7ae728f Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 16 Nov 2023 16:46:20 +1100 Subject: [PATCH 21/85] Bump bioc version for R 4.3 --- .github/workflows/check-bioc.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/check-bioc.yml b/.github/workflows/check-bioc.yml index ba15358..07689b5 100755 --- a/.github/workflows/check-bioc.yml +++ b/.github/workflows/check-bioc.yml @@ -52,9 +52,9 @@ jobs: fail-fast: false matrix: config: - - { os: ubuntu-latest, r: '4.3', bioc: '3.18', cont: "bioconductor/bioconductor_docker:devel", rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest" } - - { os: macOS-latest, r: '4.3', bioc: '3.18'} - - { os: windows-latest, r: '4.3', bioc: '3.18'} + - { os: ubuntu-latest, r: '4.3', bioc: '3.19', cont: "bioconductor/bioconductor_docker:devel", rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest" } + - { os: macOS-latest, r: '4.3', bioc: '3.19'} + - { os: windows-latest, r: '4.3', bioc: '3.19'} ## Check https://github.com/r-lib/actions/tree/master/examples ## for examples using the http-user-agent env: From 0c8c59d1d7f8869ac9b2fe15a4f015d18ad78831 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 16 Nov 2023 16:54:09 +1100 Subject: [PATCH 22/85] Mix of bioc versions --- .github/workflows/check-bioc.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/check-bioc.yml b/.github/workflows/check-bioc.yml index 07689b5..5972d4b 100755 --- a/.github/workflows/check-bioc.yml +++ b/.github/workflows/check-bioc.yml @@ -53,8 +53,8 @@ jobs: matrix: config: - { os: ubuntu-latest, r: '4.3', bioc: '3.19', cont: "bioconductor/bioconductor_docker:devel", rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest" } - - { os: macOS-latest, r: '4.3', bioc: '3.19'} - - { os: windows-latest, r: '4.3', bioc: '3.19'} + - { os: macOS-latest, r: '4.3', bioc: '3.18'} + - { os: windows-latest, r: '4.3', bioc: '3.18'} ## Check https://github.com/r-lib/actions/tree/master/examples ## for examples using the http-user-agent env: From fe0f6170a9f159fd43df824df66e3978ce0cf1dc Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 20 Nov 2023 15:20:02 +1100 Subject: [PATCH 23/85] /nocache From 81b4e346068adcba75a22eb2eecaa7fc3e8aa9d5 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 20 Nov 2023 15:42:30 +1100 Subject: [PATCH 24/85] Refactor read_parquet into separate function, and use it for unharmonised metadata --- R/metadata.R | 6 +----- R/unharmonised.R | 3 ++- R/utils.R | 17 +++++++++++++++++ 3 files changed, 20 insertions(+), 6 deletions(-) diff --git a/R/metadata.R b/R/metadata.R index 5f87f85..94d7e23 100644 --- a/R/metadata.R +++ b/R/metadata.R @@ -64,7 +64,6 @@ SAMPLE_DATABASE_URL <- single_line_str( #' #' @importFrom DBI dbConnect #' @importFrom duckdb duckdb -#' @importFrom dbplyr sql #' @importFrom dplyr tbl #' @importFrom httr progress #' @importFrom cli cli_alert_info hash_sha256 @@ -165,12 +164,9 @@ get_metadata <- function( ) } - # Since dbplyr 2.4.0, raw file paths aren't handled very well - # See: https://github.com/duckdb/duckdb-r/issues/38 - select <- glue("FROM read_parquet('{db_path}')") |> sql() table <- duckdb() |> dbConnect(drv = _, read_only = TRUE) |> - tbl(select) + read_parquet(db_path) cache$metadata_table[[hash]] <- table table } diff --git a/R/unharmonised.R b/R/unharmonised.R index 9e99586..7fa2730 100644 --- a/R/unharmonised.R +++ b/R/unharmonised.R @@ -64,7 +64,8 @@ get_unharmonised_dataset <- function( local_path, progress(type = "down", con = stderr()) ) - tbl(conn, local_path) |> + + read_parquet(conn, local_path) |> filter(.data$cell_ %in% cells) } diff --git a/R/utils.R b/R/utils.R index 0e455a1..b551aa8 100644 --- a/R/utils.R +++ b/R/utils.R @@ -80,3 +80,20 @@ sync_remote_file <- function(full_url, output_file, ...) { } invisible(NULL) } + +#' Returns a tibble from a parquet file path +#' +#' Since dbplyr 2.4.0, raw file paths aren't handled very well +#' See: https://github.com/duckdb/duckdb-r/issues/38 +#' Hence the need for this method +#' @importFrom glue glue +#' @importFrom dplyr tbl +#' @importFrom dbplyr sql +#' @return An SQL data frame +#' @keywords internal +read_parquet <- function(conn, path){ + from_clause <- glue("FROM read_parquet('{path}')") |> sql() + tbl(conn, from_clause) +} + + From ab447b34063da6388d727eb4f3e4dc7c814f089c Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Mon, 20 Nov 2023 16:04:10 +1100 Subject: [PATCH 25/85] Add new man page --- man/read_parquet.Rd | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) create mode 100644 man/read_parquet.Rd diff --git a/man/read_parquet.Rd b/man/read_parquet.Rd new file mode 100644 index 0000000..fe0cfe1 --- /dev/null +++ b/man/read_parquet.Rd @@ -0,0 +1,17 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/utils.R +\name{read_parquet} +\alias{read_parquet} +\title{Returns a tibble from a parquet file path} +\usage{ +read_parquet(conn, path) +} +\value{ +An SQL data frame +} +\description{ +Since dbplyr 2.4.0, raw file paths aren't handled very well +See: https://github.com/duckdb/duckdb-r/issues/38 +Hence the need for this method +} +\keyword{internal} From fe5c7388b97c91f14beaa98bc6904f4474a2e69e Mon Sep 17 00:00:00 2001 From: Stefano Mangiola Date: Sun, 26 Nov 2023 10:56:49 +1100 Subject: [PATCH 26/85] Update DESCRIPTION --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 6b6b6f6..da0cc1a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Type: Package Package: CuratedAtlasQueryR Title: Queries the Human Cell Atlas -Version: 0.99.6 +Version: 0.99.7 Authors@R: c( person( "Stefano", From 338c973250332cdbde80ee6ca56cc6c7951152d1 Mon Sep 17 00:00:00 2001 From: Michael Milton Date: Thu, 7 Dec 2023 10:32:19 +1100 Subject: [PATCH 27/85] Bump patch version --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index e0799c1..6e7b33f 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Type: Package Package: CuratedAtlasQueryR Title: Queries the Human Cell Atlas -Version: 1.1.0 +Version: 1.1.1 Authors@R: c( person( "Stefano", From afb0e56fcf6d88ce5dfe552e65bbaeb0f6554786 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Tue, 20 Feb 2024 13:38:23 +1100 Subject: [PATCH 28/85] update and testthat get_metadata function to allow accepting multiple vectors --- R/metadata.R | 18 ++++++++++++------ R/utils.R | 3 ++- tests/testthat/test-query.R | 12 ++++++++++++ 3 files changed, 26 insertions(+), 7 deletions(-) diff --git a/R/metadata.R b/R/metadata.R index 94d7e23..3a372ae 100644 --- a/R/metadata.R +++ b/R/metadata.R @@ -14,11 +14,16 @@ cache <- rlang::env( #' @return A character scalar consisting of the URL #' @examples #' get_metadata(remote_url = DATABASE_URL) -DATABASE_URL <- single_line_str( - "https://object-store.rc.nectar.org.au/v1/ - AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata/metadata.0.2.3.parquet" -) + +DATABASE_URL <- function(databases = c("metadata.0.2.3.parquet", "fibrosis.0.2.3.parquet")) { + glue::glue( + "https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata/{databases}" + ) |> + setNames(databases) +} + + #' URL pointing to the sample metadata file, which is smaller and for test, #' demonstration, and vignette purposes only #' @export @@ -142,7 +147,7 @@ SAMPLE_DATABASE_URL <- single_line_str( #' get_metadata(cache_directory = path.expand('~')) #' get_metadata <- function( - remote_url = DATABASE_URL, + remote_url = DATABASE_URL(), cache_directory = get_default_cache_dir(), use_cache = TRUE ) { @@ -153,7 +158,7 @@ get_metadata <- function( cached_connection } else { - db_path <- file.path(cache_directory, "metadata.0.2.3.parquet") + db_path <- file.path(cache_directory, remote_url |> names()) if (!file.exists(db_path)){ report_file_sizes(remote_url) @@ -171,3 +176,4 @@ get_metadata <- function( table } } + diff --git a/R/utils.R b/R/utils.R index b551aa8..11ca705 100644 --- a/R/utils.R +++ b/R/utils.R @@ -92,7 +92,8 @@ sync_remote_file <- function(full_url, output_file, ...) { #' @return An SQL data frame #' @keywords internal read_parquet <- function(conn, path){ - from_clause <- glue("FROM read_parquet('{path}')") |> sql() + path_list <- paste0("'", path, "'") |> paste(collapse = ", ") + from_clause <- glue("FROM read_parquet([{path_list}])") |> sql() tbl(conn, from_clause) } diff --git a/tests/testthat/test-query.R b/tests/testthat/test-query.R index 6e1610a..07f3b96 100755 --- a/tests/testthat/test-query.R +++ b/tests/testthat/test-query.R @@ -190,3 +190,15 @@ test_that("get_metadata() is cached", { identical(table, table_2) |> expect_true() }) + +test_that("database_url() expect character ", { + DATABASE_URL() |> + expect_s3_class("character") +}) + +test_that("get_metadata() returns fibrosis data when specify", { + fibro_url = DATABASE_URL("fibrosis.0.2.3.parquet") + fibro = get_metadata(remote_url = fibro_url) |> + filter(str_like(sample_, "%GSE122960%")) |> as_tibble() + expect_true(nrow(fibro) >= 1) +}) From d6d193f160896fe0ecf48e45f447631684170c07 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Wed, 21 Feb 2024 10:36:42 +1100 Subject: [PATCH 29/85] update and testthat metadata function to allow users to choose multiple databases --- R/metadata.R | 19 +++++++++++-------- R/utils.R | 2 +- tests/testthat/test-query.R | 12 ++++++++++++ 3 files changed, 24 insertions(+), 9 deletions(-) diff --git a/R/metadata.R b/R/metadata.R index 3a372ae..2bdd5e2 100644 --- a/R/metadata.R +++ b/R/metadata.R @@ -146,6 +146,8 @@ SAMPLE_DATABASE_URL <- single_line_str( #' #' get_metadata(cache_directory = path.expand('~')) #' + + get_metadata <- function( remote_url = DATABASE_URL(), cache_directory = get_default_cache_dir(), @@ -158,16 +160,17 @@ get_metadata <- function( cached_connection } else { - db_path <- file.path(cache_directory, remote_url |> names()) - - if (!file.exists(db_path)){ - report_file_sizes(remote_url) + db_path <- file.path(cache_directory, remote_url |> names()) + purrr::map2(remote_url, db_path, function(url, db_path) { + if (!file.exists(db_path)) { + report_file_sizes(url) sync_remote_file( - remote_url, - db_path, - progress(type = "down", con = stderr()) + url, + db_path, + progress(type = "down", con = stderr()) ) - } + } + }) table <- duckdb() |> dbConnect(drv = _, read_only = TRUE) |> diff --git a/R/utils.R b/R/utils.R index 11ca705..b28d977 100644 --- a/R/utils.R +++ b/R/utils.R @@ -70,7 +70,7 @@ sync_remote_file <- function(full_url, output_file, ...) { cli_alert_info("Downloading {full_url} to {output_file}") tryCatch( - GET(full_url, write_disk(output_file), ...) |> stop_for_status(), + GET(url, write_disk(output_file), ...) |> stop_for_status(), error = function(e) { # Clean up if we had an error file.remove(output_file) diff --git a/tests/testthat/test-query.R b/tests/testthat/test-query.R index 07f3b96..07ff407 100755 --- a/tests/testthat/test-query.R +++ b/tests/testthat/test-query.R @@ -202,3 +202,15 @@ test_that("get_metadata() returns fibrosis data when specify", { filter(str_like(sample_, "%GSE122960%")) |> as_tibble() expect_true(nrow(fibro) >= 1) }) + +test_that("get_metadata() returns cellxgene and fibrosis data together", { + databases = c("fibrosis.0.2.3.parquet", "metadata.0.2.3.parquet") + metadata = get_metadata(remote_url = DATABASE_URL(databases)) |> + filter(str_like(sample_, "%GSE122960%") | str_like(sample_, "bd54ab01e2d9cafabe482e9a1a599780")) |> + as_tibble() + expect_true(nrow(metadata) >2) +}) + + + + From 4092f4ceafa79ca6e56756e366b860c75f08afb6 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Wed, 21 Feb 2024 10:52:23 +1100 Subject: [PATCH 30/85] fix typo and the way duckdb handles multiple schemas --- R/utils.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/utils.R b/R/utils.R index b28d977..7f6ce00 100644 --- a/R/utils.R +++ b/R/utils.R @@ -70,7 +70,7 @@ sync_remote_file <- function(full_url, output_file, ...) { cli_alert_info("Downloading {full_url} to {output_file}") tryCatch( - GET(url, write_disk(output_file), ...) |> stop_for_status(), + GET(full_url, write_disk(output_file), ...) |> stop_for_status(), error = function(e) { # Clean up if we had an error file.remove(output_file) @@ -93,7 +93,7 @@ sync_remote_file <- function(full_url, output_file, ...) { #' @keywords internal read_parquet <- function(conn, path){ path_list <- paste0("'", path, "'") |> paste(collapse = ", ") - from_clause <- glue("FROM read_parquet([{path_list}])") |> sql() + from_clause <- glue("FROM read_parquet([{path_list}], union_by_name = true)") |> sql() tbl(conn, from_clause) } From f47d015df559b0225bb6ec3545cc63d62410ba0b Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Wed, 21 Feb 2024 14:21:53 +1100 Subject: [PATCH 31/85] use glue_sql for reading multiple databases --- NAMESPACE | 2 ++ R/metadata.R | 25 ++++++++++++------------- R/utils.R | 4 ++-- 3 files changed, 16 insertions(+), 15 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index eef68d0..5efa9b7 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -43,6 +43,7 @@ importFrom(dplyr,tbl) importFrom(dplyr,transmute) importFrom(duckdb,duckdb) importFrom(glue,glue) +importFrom(glue,glue_sql) importFrom(httr,GET) importFrom(httr,HEAD) importFrom(httr,modify_url) @@ -61,6 +62,7 @@ importFrom(purrr,pmap_chr) importFrom(purrr,reduce) importFrom(purrr,set_names) importFrom(purrr,walk) +importFrom(purrr,walk2) importFrom(rlang,.data) importFrom(stats,setNames) importFrom(stringr,str_remove_all) diff --git a/R/metadata.R b/R/metadata.R index 2bdd5e2..f86edb8 100644 --- a/R/metadata.R +++ b/R/metadata.R @@ -11,9 +11,9 @@ cache <- rlang::env( #' URL pointing to the full metadata file #' @export -#' @return A character scalar consisting of the URL +#' @return A character scalar from glue interpolation consisting of the URLs #' @examples -#' get_metadata(remote_url = DATABASE_URL) +#' get_metadata(remote_url = DATABASE_URL()) DATABASE_URL <- function(databases = c("metadata.0.2.3.parquet", "fibrosis.0.2.3.parquet")) { @@ -73,6 +73,7 @@ SAMPLE_DATABASE_URL <- single_line_str( #' @importFrom httr progress #' @importFrom cli cli_alert_info hash_sha256 #' @importFrom glue glue +#' @importFrom purrr walk2 #' #' @details #' @@ -160,17 +161,15 @@ get_metadata <- function( cached_connection } else { - db_path <- file.path(cache_directory, remote_url |> names()) - purrr::map2(remote_url, db_path, function(url, db_path) { - if (!file.exists(db_path)) { - report_file_sizes(url) - sync_remote_file( - url, - db_path, - progress(type = "down", con = stderr()) - ) - } - }) + db_path <- file.path(cache_directory, remote_url |> names()) + walk2(remote_url, db_path, function(url, path) { + if (!file.exists(path)) { + report_file_sizes(url) + sync_remote_file(url, + path, + progress(type = "down", con = stderr())) + } + }) table <- duckdb() |> dbConnect(drv = _, read_only = TRUE) |> diff --git a/R/utils.R b/R/utils.R index 7f6ce00..0ba3950 100644 --- a/R/utils.R +++ b/R/utils.R @@ -89,11 +89,11 @@ sync_remote_file <- function(full_url, output_file, ...) { #' @importFrom glue glue #' @importFrom dplyr tbl #' @importFrom dbplyr sql +#' @importFrom glue glue_sql #' @return An SQL data frame #' @keywords internal read_parquet <- function(conn, path){ - path_list <- paste0("'", path, "'") |> paste(collapse = ", ") - from_clause <- glue("FROM read_parquet([{path_list}], union_by_name = true)") |> sql() + from_clause <- glue_sql("FROM read_parquet([{`path`*}], union_by_name=true)", .con=conn) |> sql() tbl(conn, from_clause) } From 52073c75b5bd4be4e50ffed71e48c64598c01bc8 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Wed, 21 Feb 2024 15:51:01 +1100 Subject: [PATCH 32/85] update documentation for remote_url. provide a descriptive name for database_url function. fix reading sample_metadata database --- R/metadata.R | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/R/metadata.R b/R/metadata.R index f86edb8..0f28bed 100644 --- a/R/metadata.R +++ b/R/metadata.R @@ -9,14 +9,14 @@ cache <- rlang::env( metadata_table = rlang::env() ) -#' URL pointing to the full metadata file +#' prints out the URLs for all metadata files #' @export #' @return A character scalar from glue interpolation consisting of the URLs #' @examples -#' get_metadata(remote_url = DATABASE_URL()) +#' get_metadata(remote_url = get_database_url("metadata.0.2.3.parquet")) -DATABASE_URL <- function(databases = c("metadata.0.2.3.parquet", "fibrosis.0.2.3.parquet")) { +get_database_url <- function(databases = c("metadata.0.2.3.parquet", "fibrosis.0.2.3.parquet")) { glue::glue( "https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata/{databases}" ) |> @@ -43,8 +43,8 @@ SAMPLE_DATABASE_URL <- single_line_str( #' into [get_single_cell_experiment()] to obtain a #' [`SingleCellExperiment::SingleCellExperiment-class`] #' -#' @param remote_url Optional character vector of length 1. An HTTP URL pointing -#' to the location of the parquet database. +#' @param remote_url Optional character vector of any length. HTTP URL/URLs pointing +#' to the name and location of parquet database/databases. #' @param cache_directory Optional character vector of length 1. A file path on #' your local system to a directory (not a file) that will be used to store #' `metadata.parquet` @@ -150,7 +150,7 @@ SAMPLE_DATABASE_URL <- single_line_str( get_metadata <- function( - remote_url = DATABASE_URL(), + remote_url = get_database_url(), cache_directory = get_default_cache_dir(), use_cache = TRUE ) { @@ -161,7 +161,7 @@ get_metadata <- function( cached_connection } else { - db_path <- file.path(cache_directory, remote_url |> names()) + db_path <- file.path(cache_directory, remote_url |> basename()) walk2(remote_url, db_path, function(url, path) { if (!file.exists(path)) { report_file_sizes(url) From c58803eec0e8d61c1fbe1bca78aebe50c3da555d Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Wed, 21 Feb 2024 16:53:35 +1100 Subject: [PATCH 33/85] rename DATABASE_URL to get_database_url() --- NAMESPACE | 2 +- man/DATABASE_URL.Rd | 8 ++++---- man/get_metadata.Rd | 2 +- vignettes/Introduction.Rmd | 2 +- 4 files changed, 7 insertions(+), 7 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 5efa9b7..b453e4a 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,7 +1,7 @@ # Generated by roxygen2: do not edit by hand S3method(as.sparse,DelayedMatrix) -export(DATABASE_URL) +export(get_database_url) export(SAMPLE_DATABASE_URL) export(get_SingleCellExperiment) export(get_metadata) diff --git a/man/DATABASE_URL.Rd b/man/DATABASE_URL.Rd index fa9818f..7dbb8de 100644 --- a/man/DATABASE_URL.Rd +++ b/man/DATABASE_URL.Rd @@ -1,14 +1,14 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/metadata.R \docType{data} -\name{DATABASE_URL} -\alias{DATABASE_URL} +\name{get_database_url} +\alias{get_database_url} \title{URL pointing to the full metadata file} \format{ An object of class \code{character} of length 1. } \usage{ -DATABASE_URL +get_database_url } \value{ A character scalar consisting of the URL @@ -17,6 +17,6 @@ A character scalar consisting of the URL URL pointing to the full metadata file } \examples{ -get_metadata(remote_url = DATABASE_URL) +get_metadata(remote_url = get_database_url()) } \keyword{datasets} diff --git a/man/get_metadata.Rd b/man/get_metadata.Rd index 07273ca..df5f873 100644 --- a/man/get_metadata.Rd +++ b/man/get_metadata.Rd @@ -5,7 +5,7 @@ \title{Gets the Curated Atlas metadata as a data frame.} \usage{ get_metadata( - remote_url = DATABASE_URL, + remote_url = get_database_url(), cache_directory = get_default_cache_dir(), use_cache = TRUE ) diff --git a/vignettes/Introduction.Rmd b/vignettes/Introduction.Rmd index dbba7f5..916e3d6 100644 --- a/vignettes/Introduction.Rmd +++ b/vignettes/Introduction.Rmd @@ -51,7 +51,7 @@ find_figure <- function(names){ } METADATA_URL = if (params$demo_metadata) CuratedAtlasQueryR::SAMPLE_DATABASE_URL else - CuratedAtlasQueryR::DATABASE_URL + CuratedAtlasQueryR::get_database_url ``` From 421065dfb23f134c61814e9c012768916142cc57 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Wed, 21 Feb 2024 17:16:23 +1100 Subject: [PATCH 34/85] fix comments --- R/metadata.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/metadata.R b/R/metadata.R index 0f28bed..94cff7a 100644 --- a/R/metadata.R +++ b/R/metadata.R @@ -9,9 +9,9 @@ cache <- rlang::env( metadata_table = rlang::env() ) -#' prints out the URLs for all metadata files +#' Returns the URLs for all metadata files #' @export -#' @return A character scalar from glue interpolation consisting of the URLs +#' @return A named character vector whose names are parquet file names, and whose values are URLs #' @examples #' get_metadata(remote_url = get_database_url("metadata.0.2.3.parquet")) From 1afda94dddc089b460ccec724131ca290b61bff2 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Thu, 22 Feb 2024 09:26:00 +1100 Subject: [PATCH 35/85] test whether a cell type exists --- tests/testthat/test-query.R | 19 +++++-------------- 1 file changed, 5 insertions(+), 14 deletions(-) diff --git a/tests/testthat/test-query.R b/tests/testthat/test-query.R index 07ff407..9c9e958 100755 --- a/tests/testthat/test-query.R +++ b/tests/testthat/test-query.R @@ -192,23 +192,14 @@ test_that("get_metadata() is cached", { }) test_that("database_url() expect character ", { - DATABASE_URL() |> + get_database_url() |> expect_s3_class("character") }) -test_that("get_metadata() returns fibrosis data when specify", { - fibro_url = DATABASE_URL("fibrosis.0.2.3.parquet") - fibro = get_metadata(remote_url = fibro_url) |> - filter(str_like(sample_, "%GSE122960%")) |> as_tibble() - expect_true(nrow(fibro) >= 1) -}) - -test_that("get_metadata() returns cellxgene and fibrosis data together", { - databases = c("fibrosis.0.2.3.parquet", "metadata.0.2.3.parquet") - metadata = get_metadata(remote_url = DATABASE_URL(databases)) |> - filter(str_like(sample_, "%GSE122960%") | str_like(sample_, "bd54ab01e2d9cafabe482e9a1a599780")) |> - as_tibble() - expect_true(nrow(metadata) >2) + +test_that("get_metadata() expect a unique cell_type `b` is present, which comes from fibrosis database", { + n_cell <- get_metadata() |> filter(cell_type_harmonised == 'b') |> as_tibble() |> nrow() + expect_true(n_cell > 0) }) From 86c32fd88e7b0c37e08326fc95338a56c0b08765 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Thu, 22 Feb 2024 09:59:55 +1100 Subject: [PATCH 36/85] replace donttest with dontrun --- R/dev.R | 4 ++-- R/unharmonised.R | 2 +- man/get_unharmonised_dataset.Rd | 2 +- man/hdf5_to_anndata.Rd | 2 +- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/R/dev.R b/R/dev.R index a30d4cd..18a9b2c 100644 --- a/R/dev.R +++ b/R/dev.R @@ -135,7 +135,7 @@ update_unharmonised <- function(unharmonised_parquet_dir, ...){ #' @keywords internal #' @return A character vector of the newly-created anndata files #' @examples -#' \donttest{ +#' \dontrun{ #' hdf5_to_anndata( #' "/vast/projects/cellxgene_curated/splitted_DB2_data_0.2.1", #' "/vast/projects/cellxgene_curated/splitted_DB2_anndata_0.2.1" @@ -194,7 +194,7 @@ hdf5_to_anndata <- function(input_directory, output_directory){ # @return A character vector of the newly-created anndata files # @noRd # @examples -# \donttest{ +# \dontrun{ # h5seurat_to_anndata( # "/vast/projects/cellxgene_curated/splitted_DB2_data_0.2.1", # "/vast/projects/cellxgene_curated/splitted_DB2_anndata_0.2.1" diff --git a/R/unharmonised.R b/R/unharmonised.R index 7fa2730..ae67706 100644 --- a/R/unharmonised.R +++ b/R/unharmonised.R @@ -37,7 +37,7 @@ UNHARMONISED_URL <- single_line_str( #' @return A named list, where each name is a dataset file ID, and each value is #' a "lazy data frame", ie a `tbl`. #' @examples -#' \donttest{ +#' \dontrun{ #' dataset <- "838ea006-2369-4e2c-b426-b2a744a2b02b" #' harmonised_meta <- get_metadata() |> #' dplyr::filter(file_id == dataset) |> dplyr::collect() diff --git a/man/get_unharmonised_dataset.Rd b/man/get_unharmonised_dataset.Rd index 261793c..3402325 100644 --- a/man/get_unharmonised_dataset.Rd +++ b/man/get_unharmonised_dataset.Rd @@ -40,7 +40,7 @@ make sense for these to live in the main metadata table. This function is a utility that allows easy fetching of this data if necessary. } \examples{ -\donttest{ +\dontrun{ dataset <- "838ea006-2369-4e2c-b426-b2a744a2b02b" harmonised_meta <- get_metadata() |> dplyr::filter(file_id == dataset) |> dplyr::collect() diff --git a/man/hdf5_to_anndata.Rd b/man/hdf5_to_anndata.Rd index a1ef720..586fed5 100644 --- a/man/hdf5_to_anndata.Rd +++ b/man/hdf5_to_anndata.Rd @@ -20,7 +20,7 @@ A character vector of the newly-created anndata files Converts a series of HDF5Array-serialized SingleCellExperiments to AnnData } \examples{ -\donttest{ +\dontrun{ hdf5_to_anndata( "/vast/projects/cellxgene_curated/splitted_DB2_data_0.2.1", "/vast/projects/cellxgene_curated/splitted_DB2_anndata_0.2.1" From b15b7a71f2f7b88c38d8a8b8f2527822e65a5182 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Thu, 22 Feb 2024 10:52:41 +1100 Subject: [PATCH 37/85] improve the counts database structure --- R/counts.R | 2 +- R/unharmonised.R | 1 - R/utils.R | 3 ++- tests/testthat/test-query.R | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/R/counts.R b/R/counts.R index f8c9a93..8e06663 100644 --- a/R/counts.R +++ b/R/counts.R @@ -112,7 +112,7 @@ get_single_cell_experiment <- function( has_name(raw_data, c("cell_", "file_id_db")) ) - versioned_cache_directory <- file.path(cache_directory, COUNTS_VERSION) + versioned_cache_directory <- cache_directory versioned_cache_directory |> dir.create( showWarnings = FALSE, recursive = TRUE diff --git a/R/unharmonised.R b/R/unharmonised.R index ae67706..48f4870 100644 --- a/R/unharmonised.R +++ b/R/unharmonised.R @@ -54,7 +54,6 @@ get_unharmonised_dataset <- function( ){ unharmonised_root <- file.path( cache_directory, - COUNTS_VERSION, "unharmonised" ) file_name <- glue::glue("{dataset_id}.parquet") diff --git a/R/utils.R b/R/utils.R index 0ba3950..7140a28 100644 --- a/R/utils.R +++ b/R/utils.R @@ -41,7 +41,7 @@ single_line_str <- function(text){ str_remove_all(text, r"(\n\s*)") } -#' Returns the default cache directory +#' Returns the default cache directory with a version number #' @return A length one character vector. #' @importFrom tools R_user_dir #' @importFrom utils packageName @@ -51,6 +51,7 @@ get_default_cache_dir <- function() { R_user_dir( "cache" ) |> + file.path(COUNTS_VERSION) |> normalizePath() |> suppressWarnings() } diff --git a/tests/testthat/test-query.R b/tests/testthat/test-query.R index 9c9e958..12137d2 100755 --- a/tests/testthat/test-query.R +++ b/tests/testthat/test-query.R @@ -131,7 +131,7 @@ test_that("get_SingleCellExperiment() assigns the right cell ID to each cell", { # Load the SCE from cache directly assay_1 = CuratedAtlasQueryR:::get_default_cache_dir() |> - file.path(CuratedAtlasQueryR:::COUNTS_VERSION, "original", id) |> + file.path("original", id) |> HDF5Array::loadHDF5SummarizedExperiment() |> assay("X") |> as.matrix() From 78b8c0f82d0bebdc7928c8f935c64bf84d219555 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Thu, 22 Feb 2024 14:11:19 +1100 Subject: [PATCH 38/85] fix testthat for ubuntu env --- tests/testthat/test-query.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/testthat/test-query.R b/tests/testthat/test-query.R index 12137d2..313d121 100755 --- a/tests/testthat/test-query.R +++ b/tests/testthat/test-query.R @@ -19,7 +19,7 @@ test_that("get_default_cache_dir() returns the correct directory on Linux", { grepl("linux", version$platform, fixed = TRUE) |> skip_if_not() - "~/.cache/R/CuratedAtlasQueryR" |> + "~/.cache/R/CuratedAtlasQueryR/0.2.1" |> normalizePath() |> expect_equal( get_default_cache_dir(), From 1ab4cd7209b4448fbedb5a32bf6c943cedc94b6b Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Wed, 28 Feb 2024 16:52:55 +1100 Subject: [PATCH 39/85] accepting counts from multiple databases --- DESCRIPTION | 2 +- NAMESPACE | 3 +- R/metadata.R | 74 ++++++++++++++++++------------------ man/DATABASE_URL.Rd | 22 ----------- man/SAMPLE_DATABASE_URL.Rd | 2 +- man/get_database_url.Rd | 22 +++++++++++ man/get_default_cache_dir.Rd | 4 +- man/get_metadata.Rd | 4 +- vignettes/Introduction.Rmd | 15 ++++++-- 9 files changed, 78 insertions(+), 70 deletions(-) delete mode 100644 man/DATABASE_URL.Rd create mode 100644 man/get_database_url.Rd diff --git a/DESCRIPTION b/DESCRIPTION index 6e7b33f..5dc8301 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -121,7 +121,7 @@ biocViews: Transcription, Transcriptomics Encoding: UTF-8 -RoxygenNote: 7.2.3 +RoxygenNote: 7.3.1 LazyDataCompression: xz URL: https://github.com/stemangiola/CuratedAtlasQueryR BugReports: https://github.com/stemangiola/CuratedAtlasQueryR/issues diff --git a/NAMESPACE b/NAMESPACE index b453e4a..a620231 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,9 +1,9 @@ # Generated by roxygen2: do not edit by hand S3method(as.sparse,DelayedMatrix) -export(get_database_url) export(SAMPLE_DATABASE_URL) export(get_SingleCellExperiment) +export(get_database_url) export(get_metadata) export(get_seurat) export(get_single_cell_experiment) @@ -62,7 +62,6 @@ importFrom(purrr,pmap_chr) importFrom(purrr,reduce) importFrom(purrr,set_names) importFrom(purrr,walk) -importFrom(purrr,walk2) importFrom(rlang,.data) importFrom(stats,setNames) importFrom(stringr,str_remove_all) diff --git a/R/metadata.R b/R/metadata.R index 94cff7a..81ab358 100644 --- a/R/metadata.R +++ b/R/metadata.R @@ -6,32 +6,28 @@ NULL #' Environment that we use to cache the DuckDB connections #' @noRd cache <- rlang::env( - metadata_table = rlang::env() + metadata_table = rlang::env() ) #' Returns the URLs for all metadata files +#' @param databases A character vector specifying the names of the metadata files. Default is c("metadata.0.2.3.parquet", "fibrosis.0.2.3.parquet") #' @export -#' @return A named character vector whose names are parquet file names, and whose values are URLs +#' @return A character vector of URLs to parquet files to download #' @examples -#' get_metadata(remote_url = get_database_url("metadata.0.2.3.parquet")) - - +#' get_database_url("metadata.0.2.3.parquet") get_database_url <- function(databases = c("metadata.0.2.3.parquet", "fibrosis.0.2.3.parquet")) { - glue::glue( - "https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata/{databases}" - ) |> - setNames(databases) + glue::glue( + "https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata/{databases}") } - #' URL pointing to the sample metadata file, which is smaller and for test, #' demonstration, and vignette purposes only #' @export #' @return A character scalar consisting of the URL #' @examples -#' get_metadata(remote_url = SAMPLE_DATABASE_URL) +#' get_metadata(remote_url = SAMPLE_DATABASE_URL, cache_directory = tempdir()) SAMPLE_DATABASE_URL <- single_line_str( - "https://object-store.rc.nectar.org.au/v1/ + "https://object-store.rc.nectar.org.au/v1/ AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata/ sample_metadata.0.2.3.parquet" ) @@ -73,7 +69,7 @@ SAMPLE_DATABASE_URL <- single_line_str( #' @importFrom httr progress #' @importFrom cli cli_alert_info hash_sha256 #' @importFrom glue glue -#' @importFrom purrr walk2 +#' @importFrom purrr walk #' #' @details #' @@ -147,35 +143,39 @@ SAMPLE_DATABASE_URL <- single_line_str( #' #' get_metadata(cache_directory = path.expand('~')) #' - - get_metadata <- function( remote_url = get_database_url(), cache_directory = get_default_cache_dir(), use_cache = TRUE ) { - hash <- c(remote_url, cache_directory) |> paste0(collapse="") |> - hash_sha256() - cached_connection <- cache$metadata_table[[hash]] - if (!is.null(cached_connection) && isTRUE(use_cache)) { - cached_connection - } - else { - db_path <- file.path(cache_directory, remote_url |> basename()) - walk2(remote_url, db_path, function(url, path) { - if (!file.exists(path)) { - report_file_sizes(url) - sync_remote_file(url, - path, - progress(type = "down", con = stderr())) - } - }) - - table <- duckdb() |> - dbConnect(drv = _, read_only = TRUE) |> - read_parquet(db_path) - cache$metadata_table[[hash]] <- table - table + # Synchronize remote files + walk(remote_url, function(url) { + # Calculate the file path from the URL + path <- file.path(cache_directory, url |> basename()) + if (!file.exists(path)) { + report_file_sizes(url) + sync_remote_file(url, + path, + progress(type = "down", con = stderr())) } + }) + all_parquet <- file.path(cache_directory, dir(cache_directory, pattern = ".parquet$")) + # We try to avoid re-reading a set of parquet files + # that is identical to a previous set by hashing the file list + hash <- all_parquet |> paste0(collapse="") |> + hash_sha256() + cached_connection <- cache$metadata_table[[hash]] + + if (!is.null(cached_connection) && isTRUE(use_cache)) { + # If the file list is identical, just re-use the database table + cached_connection + } + else { + table <- duckdb() |> + dbConnect(drv = _, read_only = TRUE) |> + read_parquet(path = all_parquet) + cache$metadata_table[[hash]] <- table + table + } } diff --git a/man/DATABASE_URL.Rd b/man/DATABASE_URL.Rd deleted file mode 100644 index 7dbb8de..0000000 --- a/man/DATABASE_URL.Rd +++ /dev/null @@ -1,22 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/metadata.R -\docType{data} -\name{get_database_url} -\alias{get_database_url} -\title{URL pointing to the full metadata file} -\format{ -An object of class \code{character} of length 1. -} -\usage{ -get_database_url -} -\value{ -A character scalar consisting of the URL -} -\description{ -URL pointing to the full metadata file -} -\examples{ -get_metadata(remote_url = get_database_url()) -} -\keyword{datasets} diff --git a/man/SAMPLE_DATABASE_URL.Rd b/man/SAMPLE_DATABASE_URL.Rd index 52df1d0..33d0927 100644 --- a/man/SAMPLE_DATABASE_URL.Rd +++ b/man/SAMPLE_DATABASE_URL.Rd @@ -19,6 +19,6 @@ URL pointing to the sample metadata file, which is smaller and for test, demonstration, and vignette purposes only } \examples{ -get_metadata(remote_url = SAMPLE_DATABASE_URL) +get_metadata(remote_url = SAMPLE_DATABASE_URL, cache_directory = tempdir()) } \keyword{datasets} diff --git a/man/get_database_url.Rd b/man/get_database_url.Rd new file mode 100644 index 0000000..0140f13 --- /dev/null +++ b/man/get_database_url.Rd @@ -0,0 +1,22 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/metadata.R +\name{get_database_url} +\alias{get_database_url} +\title{Returns the URLs for all metadata files} +\usage{ +get_database_url( + databases = c("metadata.0.2.3.parquet", "fibrosis.0.2.3.parquet") +) +} +\arguments{ +\item{databases}{A character vector specifying the names of the metadata files. Default is c("metadata.0.2.3.parquet", "fibrosis.0.2.3.parquet")} +} +\value{ +A character vector of URLs to parquet files to download +} +\description{ +Returns the URLs for all metadata files +} +\examples{ +get_database_url("metadata.0.2.3.parquet") +} diff --git a/man/get_default_cache_dir.Rd b/man/get_default_cache_dir.Rd index f30f0b5..b42461f 100644 --- a/man/get_default_cache_dir.Rd +++ b/man/get_default_cache_dir.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/utils.R \name{get_default_cache_dir} \alias{get_default_cache_dir} -\title{Returns the default cache directory} +\title{Returns the default cache directory with a version number} \usage{ get_default_cache_dir() } @@ -10,6 +10,6 @@ get_default_cache_dir() A length one character vector. } \description{ -Returns the default cache directory +Returns the default cache directory with a version number } \keyword{internal} diff --git a/man/get_metadata.Rd b/man/get_metadata.Rd index df5f873..6d19ccf 100644 --- a/man/get_metadata.Rd +++ b/man/get_metadata.Rd @@ -11,8 +11,8 @@ get_metadata( ) } \arguments{ -\item{remote_url}{Optional character vector of length 1. An HTTP URL pointing -to the location of the parquet database.} +\item{remote_url}{Optional character vector of any length. HTTP URL/URLs pointing +to the name and location of parquet database/databases.} \item{cache_directory}{Optional character vector of length 1. A file path on your local system to a directory (not a file) that will be used to store diff --git a/vignettes/Introduction.Rmd b/vignettes/Introduction.Rmd index 916e3d6..04e3942 100644 --- a/vignettes/Introduction.Rmd +++ b/vignettes/Introduction.Rmd @@ -50,8 +50,9 @@ find_figure <- function(names){ file.path("man", "figures", names) } METADATA_URL = if (params$demo_metadata) - CuratedAtlasQueryR::SAMPLE_DATABASE_URL else - CuratedAtlasQueryR::get_database_url + CuratedAtlasQueryR::SAMPLE_DATABASE_URL else + CuratedAtlasQueryR::get_database_url + ``` @@ -98,12 +99,20 @@ library(CuratedAtlasQueryR) ### Load the metadata -```{r} +```{r, eval=FALSE} # Note: in real applications you should use the default value of remote_url metadata <- get_metadata(remote_url = METADATA_URL) metadata ``` +```{r, echo=FALSE} +# Note: a custom cache is used here ONLY for R CHECK compliance purposes. Users will NOT need to specify a custom cache +metadata <- get_metadata(remote_url = METADATA_URL, cache_directory = tempdir()) +metadata +``` + + + The `metadata` variable can then be re-used for all subsequent queries. ### Explore the tissue From b80627b39a5ca8b948fd87bef3842c2b28f76635 Mon Sep 17 00:00:00 2001 From: Stefano Mangiola Date: Wed, 28 Feb 2024 16:57:34 +1030 Subject: [PATCH 40/85] Update DESCRIPTION --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 5dc8301..86a4707 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Type: Package Package: CuratedAtlasQueryR Title: Queries the Human Cell Atlas -Version: 1.1.1 +Version: 1.1.2 Authors@R: c( person( "Stefano", From 9a2b287da0e7f01072384f7145d5799c902885d3 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Thu, 29 Feb 2024 10:51:59 +1100 Subject: [PATCH 41/85] export default_cache function, add clear_cache function --- NAMESPACE | 2 ++ R/utils.R | 12 ++++++++++++ man/clear_cache.Rd | 15 +++++++++++++++ 3 files changed, 29 insertions(+) create mode 100644 man/clear_cache.Rd diff --git a/NAMESPACE b/NAMESPACE index a620231..7b7c4ed 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -2,8 +2,10 @@ S3method(as.sparse,DelayedMatrix) export(SAMPLE_DATABASE_URL) +export(clear_cache) export(get_SingleCellExperiment) export(get_database_url) +export(get_default_cache_dir) export(get_metadata) export(get_seurat) export(get_single_cell_experiment) diff --git a/R/utils.R b/R/utils.R index 7140a28..b29004b 100644 --- a/R/utils.R +++ b/R/utils.R @@ -42,6 +42,7 @@ single_line_str <- function(text){ } #' Returns the default cache directory with a version number +#' @export #' @return A length one character vector. #' @importFrom tools R_user_dir #' @importFrom utils packageName @@ -56,6 +57,17 @@ get_default_cache_dir <- function() { suppressWarnings() } +#' Clear the default cache directory +#' @export +#' @return A length one character vector. +#' @importFrom tools R_user_dir +#' @importFrom utils packageName +#' @keywords internal +clear_cache <- function() { + get_default_cache_dir() |> unlink(TRUE, TRUE) +} + + #' Synchronises a single remote file with a local path #' @importFrom httr write_disk GET stop_for_status #' @importFrom cli cli_abort cli_alert_info diff --git a/man/clear_cache.Rd b/man/clear_cache.Rd new file mode 100644 index 0000000..2f4c766 --- /dev/null +++ b/man/clear_cache.Rd @@ -0,0 +1,15 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/utils.R +\name{clear_cache} +\alias{clear_cache} +\title{Clear the default cache directory} +\usage{ +clear_cache() +} +\value{ +A length one character vector. +} +\description{ +Clear the default cache directory +} +\keyword{internal} From 94a6edce2eec0d600a060cd906f3c1ed7f73ac13 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Fri, 1 Mar 2024 15:47:58 +1100 Subject: [PATCH 42/85] check metadata, gene names, positive counts, file_id --- R/import_metadata_and_counts.R | 60 ++++++++++++++++++++++++++++++++++ 1 file changed, 60 insertions(+) create mode 100644 R/import_metadata_and_counts.R diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R new file mode 100644 index 0000000..279ef93 --- /dev/null +++ b/R/import_metadata_and_counts.R @@ -0,0 +1,60 @@ +library(CuratedAtlasQueryR) +library(dplyr) +library(SingleCellExperiment) +library(assertthat) +library(checkmate) +library(arrow) + +# import_metadata_and_counts(metadata_tbl, list_of_count_matriced) +# +# +# will accept +# +# - single_cell_expeirment object, with certain characteristics (we need to decide the checks to do) +# +# - tibble of metadata, with certain characteristics (we need to decide the checks to do) +# +# what will do + +# - the metadata has the minimum set of columns, with the right types (we could use https://github.com/mllg/checkmate) +# +# - check that the cache does not include multiple version of the same metadata_file + + + +# Maps user provided assay names to their corresponding paths in the repository +assay_map <- c(counts = "original", + cpm = "cpm") + +import_metadata_counts <- function(meta_input, + cache_dir = CuratedAtlasQueryR:::get_default_cache_dir(), + # it will be solved once the pull request is submitted and merged + meta_output, + version = "0.2.3") { + # A few checks from here + counts <- assay_map["counts"] + cpm <- assay_map['cpm'] + checkTibble(meta_input) + check_directory_exists(cache_dir) # should we add the constraint, if exist, delete? if not, continue? + check_directory_exists(file.path(cache_dir, counts)) + sce <- meta_input |> + get_single_cell_experiment() + se <- meta_input |> + get_seurat() + + counts_data <- assays(sce)$counts |> + as_tibble() + assert_that(inherits(sce, "SingleCellExperiment"), + msg = "SingleCellExperiment Object is not created from metadata.") + assert_that(!'^ENS' %in% rownames(se[['originalexp']]), + msg = "Gene names cannot contain Ensembl IDs.") + assert_that(all(counts_data >= 0), + msg = "Counts for SingleCellExperiment cannot be negative.") + all(meta_input |> pull(file_id_db) %in% dir(file.path(cache_dir, counts))) |> + assert_that(msg = "The metadata sample file ID and the count file ID does not match") + arrow::write_parquet(meta_input, file.path(cache_dir, glue("{meta_output}.{version}.parquet"))) + +} + + + From 670c10fb48655f031903f3c4b6e7893cf8b5ed2c Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Fri, 1 Mar 2024 16:35:46 +1100 Subject: [PATCH 43/85] generate cpm from original, check subdir number match --- R/import_metadata_and_counts.R | 23 ++++++++++++++++++++++- 1 file changed, 22 insertions(+), 1 deletion(-) diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 279ef93..4b34f85 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -41,7 +41,6 @@ import_metadata_counts <- function(meta_input, get_single_cell_experiment() se <- meta_input |> get_seurat() - counts_data <- assays(sce)$counts |> as_tibble() assert_that(inherits(sce, "SingleCellExperiment"), @@ -54,6 +53,28 @@ import_metadata_counts <- function(meta_input, assert_that(msg = "The metadata sample file ID and the count file ID does not match") arrow::write_parquet(meta_input, file.path(cache_dir, glue("{meta_output}.{version}.parquet"))) + # if checkpoints above pass, generate cpm + cli_alert_info("Generating cpm from {.path {cache_dir}/{counts}}. ") + check_file_exists("count_per_millions.R") + + input_dir <- file.path(cache_dir, counts) + output_dir <- file.path(cache_dir, cpm) + + for (subdir in list.files(input_dir, full.names = TRUE)) { + file_name <- basename(subdir) + count_path <- glue("{input_dir}/{file_name}/") + cpm_path <- glue("{output_dir}/{file_name}/") + # Iterate count_per_millions.R to generate cpm for each raw count + command <- + glue("Rscript ./count_per_millions.R {count_path} {cpm_path}" ) + system(command) + } + + cli_alert_info("cpm are generated in {.path {cache_dir}/{cpm}}. ") + + # check the number of sub directories in original match cpm + check_set_equal(length(list.dirs(file.path(cache_dir, counts))), length(list.dirs(file.path(cache_dir, cpm)))) + } From c816770ac3f88a567c6d8ede020caab09daee7a8 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Fri, 1 Mar 2024 17:07:31 +1100 Subject: [PATCH 44/85] check metadata input has minimum set of column names and types --- R/import_metadata_and_counts.R | 41 +++++++++++++++++----------------- 1 file changed, 21 insertions(+), 20 deletions(-) diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 4b34f85..9f2eaf0 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -5,27 +5,18 @@ library(assertthat) library(checkmate) library(arrow) -# import_metadata_and_counts(metadata_tbl, list_of_count_matriced) -# -# -# will accept -# -# - single_cell_expeirment object, with certain characteristics (we need to decide the checks to do) -# -# - tibble of metadata, with certain characteristics (we need to decide the checks to do) -# -# what will do - -# - the metadata has the minimum set of columns, with the right types (we could use https://github.com/mllg/checkmate) -# -# - check that the cache does not include multiple version of the same metadata_file - - - # Maps user provided assay names to their corresponding paths in the repository assay_map <- c(counts = "original", cpm = "cpm") +cols <- get_metadata(remote_url = get_database_url("fibrosis.0.2.3.parquet")) |> + select(1:22) |> head(10) |> as_tibble() + +col_map <- tibble( + colnames = names(cols), + class = sapply(cols, class) +) + import_metadata_counts <- function(meta_input, cache_dir = CuratedAtlasQueryR:::get_default_cache_dir(), # it will be solved once the pull request is submitted and merged @@ -41,6 +32,7 @@ import_metadata_counts <- function(meta_input, get_single_cell_experiment() se <- meta_input |> get_seurat() + counts_data <- assays(sce)$counts |> as_tibble() assert_that(inherits(sce, "SingleCellExperiment"), @@ -53,6 +45,18 @@ import_metadata_counts <- function(meta_input, assert_that(msg = "The metadata sample file ID and the count file ID does not match") arrow::write_parquet(meta_input, file.path(cache_dir, glue("{meta_output}.{version}.parquet"))) + # check the number of sub directories in original match cpm + check_set_equal(length(list.dirs(file.path(cache_dir, counts))), length(list.dirs(file.path(cache_dir, cpm)))) + + # check the metadata has the minimum set of columns names + check_subset(names(cols), names(meta_input)) + + # check the metadata has the correct column types + common_columns <- intersect(names(meta_input), names(cols)) + meta_input_class <- sapply(meta_input[, common_columns, drop=FALSE], class) + col_map_class <- col_map$class + check_set_equal(meta_input_class, col_map_class) + # if checkpoints above pass, generate cpm cli_alert_info("Generating cpm from {.path {cache_dir}/{counts}}. ") check_file_exists("count_per_millions.R") @@ -72,9 +76,6 @@ import_metadata_counts <- function(meta_input, cli_alert_info("cpm are generated in {.path {cache_dir}/{cpm}}. ") - # check the number of sub directories in original match cpm - check_set_equal(length(list.dirs(file.path(cache_dir, counts))), length(list.dirs(file.path(cache_dir, cpm)))) - } From 1b0d60890824e5b536a5e10b05696e5d9b4a0fb2 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Mon, 4 Mar 2024 12:11:20 +1100 Subject: [PATCH 45/85] add check age, sex, minimum sets of input columns --- R/import_metadata_and_counts.R | 22 +++++++++++++--------- 1 file changed, 13 insertions(+), 9 deletions(-) diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 9f2eaf0..9d98202 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -19,14 +19,13 @@ col_map <- tibble( import_metadata_counts <- function(meta_input, cache_dir = CuratedAtlasQueryR:::get_default_cache_dir(), - # it will be solved once the pull request is submitted and merged meta_output, version = "0.2.3") { # A few checks from here counts <- assay_map["counts"] cpm <- assay_map['cpm'] checkTibble(meta_input) - check_directory_exists(cache_dir) # should we add the constraint, if exist, delete? if not, continue? + check_directory_exists(cache_dir) check_directory_exists(file.path(cache_dir, counts)) sce <- meta_input |> get_single_cell_experiment() @@ -48,14 +47,19 @@ import_metadata_counts <- function(meta_input, # check the number of sub directories in original match cpm check_set_equal(length(list.dirs(file.path(cache_dir, counts))), length(list.dirs(file.path(cache_dir, cpm)))) - # check the metadata has the minimum set of columns names - check_subset(names(cols), names(meta_input)) + # check the metadata contains cell_, file_id_db, sample_ with correct types + check_true("cell_" %in% names(meta_input)) + check_true("file_id_db" %in% names(meta_input)) + check_true("sample_" %in% names(meta_input)) + meta_input |> select(cell_, file_id_db, sample_) |> sapply(class) |> check_character() - # check the metadata has the correct column types - common_columns <- intersect(names(meta_input), names(cols)) - meta_input_class <- sapply(meta_input[, common_columns, drop=FALSE], class) - col_map_class <- col_map$class - check_set_equal(meta_input_class, col_map_class) + # check age_days is either -99 or greater than 365 + assert_that(any(meta_input$age_days==-99 | meta_input$age_days> 365), + msg = "age_days should be either -99 for unknown or greater than 365") + + # check sex capitalisation then convert to lower case + meta_input <- meta_input |> mutate(sex = tolower(sex)) + meta_input |> distinct(sex) |> pull() |> check_subset(c('female','male','unknown')) # if checkpoints above pass, generate cpm cli_alert_info("Generating cpm from {.path {cache_dir}/{counts}}. ") From 7d264f37cf2907d62a364af6acc58f6ad39c014d Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Mon, 4 Mar 2024 15:20:10 +1100 Subject: [PATCH 46/85] upload meta, counts to cloud --- R/upload_cloud.R | 30 ++++++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) create mode 100644 R/upload_cloud.R diff --git a/R/upload_cloud.R b/R/upload_cloud.R new file mode 100644 index 0000000..7f3768a --- /dev/null +++ b/R/upload_cloud.R @@ -0,0 +1,30 @@ +# how do we want to structure meta and counts container on the cloud? +# how does file_id_db get generated? if load all counts into one container, there would be an issue if file_id_db in new atlas duplicate with that on the cloud +# Ideally a container should contain meta, and directories for raw counts and cpm + +assay_map <- c(counts = "original", + cpm = "cpm") + +testing <- read_parquet("testing.0.2.3.parquet") +testing<- tibble(testing) + +upload_cloud <- function(meta, + assay = "counts", + version = "0.2.3", + cache_dir = CuratedAtlasQueryR:::get_default_cache_dir()) { + #check if the software is in the directory + check_file_exists(file.path(cache_dir, "harmonised_human_cell_atlas-openrc.sh")) + meta_input <- paste(meta, version, sep=".") + upload_meta <- glue("swift upload {meta_input} {cache_dir}/{meta_input}.parquet)") + upload_counts <- glue("swift upload {meta_input} {cache_dir}/{assay})") + # Swift software can be installed when python>=3.11 + system("module load python/3.11", intern = TRUE,) + check_swift <- system("swift list --help", intern = TRUE, ignore.stderr = TRUE) + + if (length(check_swift) == 0){ + system("python - pip install python-swiftclient", intern = TRUE) + } + system("source harmonised_human_cell_atlas-openrc.sh", intern = TRUE) + system(upload_meta) + system(upload_counts) +} From 47f237a973891ce7a3f2ed460aeca1e1fc04d194 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Mon, 4 Mar 2024 15:38:00 +1100 Subject: [PATCH 47/85] add documentation --- R/import_metadata_and_counts.R | 42 ++++++++++++++++++++++------------ 1 file changed, 27 insertions(+), 15 deletions(-) diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 9d98202..b20f14e 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -1,21 +1,33 @@ -library(CuratedAtlasQueryR) -library(dplyr) -library(SingleCellExperiment) -library(assertthat) -library(checkmate) -library(arrow) - # Maps user provided assay names to their corresponding paths in the repository assay_map <- c(counts = "original", cpm = "cpm") -cols <- get_metadata(remote_url = get_database_url("fibrosis.0.2.3.parquet")) |> - select(1:22) |> head(10) |> as_tibble() +#' Checks metadata meets importing criteria. +#' Returns +#' Calculating counts per million from raw counts +#' Gets the Curated Atlas metadata as a data frame. +#' +#' +#' @param meta_input +#' @param cache_dir Optional character vector of length 1. A file path on +#' your local system to a directory (not a file) that will be used to store +#' `metadata.parquet` +#' @param meta_output +#' @return A directory stores counts per million +#' @export +#' @examples +#' example_metadata <- get_metadata() |> head(3) |> as_tibble() +#' check_new_metadata <- import_metadata_counts(meta_input = example_metadata, +#' meta_output = "example_metadata", +#' cache_dir = tempdir()) +#' +#' @importFrom assertthat assertthat +#' @importFrom checkmate check_tibble check_directory_exists check_set_equal check_true check_character check_subset check_file_exists +#' @importFrom dplyr tbl +#' @importFrom cli cli_alert_info +#' @importFrom glue glue +#' @importFrom arrow read_parquet -col_map <- tibble( - colnames = names(cols), - class = sapply(cols, class) -) import_metadata_counts <- function(meta_input, cache_dir = CuratedAtlasQueryR:::get_default_cache_dir(), @@ -24,7 +36,7 @@ import_metadata_counts <- function(meta_input, # A few checks from here counts <- assay_map["counts"] cpm <- assay_map['cpm'] - checkTibble(meta_input) + check_tibble(meta_input) check_directory_exists(cache_dir) check_directory_exists(file.path(cache_dir, counts)) sce <- meta_input |> @@ -42,7 +54,7 @@ import_metadata_counts <- function(meta_input, msg = "Counts for SingleCellExperiment cannot be negative.") all(meta_input |> pull(file_id_db) %in% dir(file.path(cache_dir, counts))) |> assert_that(msg = "The metadata sample file ID and the count file ID does not match") - arrow::write_parquet(meta_input, file.path(cache_dir, glue("{meta_output}.{version}.parquet"))) + write_parquet(meta_input, file.path(cache_dir, glue("{meta_output}.{version}.parquet"))) # check the number of sub directories in original match cpm check_set_equal(length(list.dirs(file.path(cache_dir, counts))), length(list.dirs(file.path(cache_dir, cpm)))) From fd38b9ec79b338e33c0759f108ddcc71ffb52be9 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Mon, 4 Mar 2024 15:39:43 +1100 Subject: [PATCH 48/85] Revert "upload meta, counts to cloud" This reverts commit 7d264f37cf2907d62a364af6acc58f6ad39c014d. --- R/upload_cloud.R | 30 ------------------------------ 1 file changed, 30 deletions(-) delete mode 100644 R/upload_cloud.R diff --git a/R/upload_cloud.R b/R/upload_cloud.R deleted file mode 100644 index 7f3768a..0000000 --- a/R/upload_cloud.R +++ /dev/null @@ -1,30 +0,0 @@ -# how do we want to structure meta and counts container on the cloud? -# how does file_id_db get generated? if load all counts into one container, there would be an issue if file_id_db in new atlas duplicate with that on the cloud -# Ideally a container should contain meta, and directories for raw counts and cpm - -assay_map <- c(counts = "original", - cpm = "cpm") - -testing <- read_parquet("testing.0.2.3.parquet") -testing<- tibble(testing) - -upload_cloud <- function(meta, - assay = "counts", - version = "0.2.3", - cache_dir = CuratedAtlasQueryR:::get_default_cache_dir()) { - #check if the software is in the directory - check_file_exists(file.path(cache_dir, "harmonised_human_cell_atlas-openrc.sh")) - meta_input <- paste(meta, version, sep=".") - upload_meta <- glue("swift upload {meta_input} {cache_dir}/{meta_input}.parquet)") - upload_counts <- glue("swift upload {meta_input} {cache_dir}/{assay})") - # Swift software can be installed when python>=3.11 - system("module load python/3.11", intern = TRUE,) - check_swift <- system("swift list --help", intern = TRUE, ignore.stderr = TRUE) - - if (length(check_swift) == 0){ - system("python - pip install python-swiftclient", intern = TRUE) - } - system("source harmonised_human_cell_atlas-openrc.sh", intern = TRUE) - system(upload_meta) - system(upload_counts) -} From 0e4123fef4a1ac6982429891dd9fff22541edda8 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Mon, 4 Mar 2024 16:14:15 +1100 Subject: [PATCH 49/85] add function `import_metadata_counts` --- DESCRIPTION | 1 + NAMESPACE | 9 ++++++++ R/import_metadata_and_counts.R | 14 +++++------- man/import_metadata_counts.Rd | 41 ++++++++++++++++++++++++++++++++++ 4 files changed, 56 insertions(+), 9 deletions(-) create mode 100644 man/import_metadata_counts.Rd diff --git a/DESCRIPTION b/DESCRIPTION index 86a4707..910ba89 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -131,6 +131,7 @@ Collate: 'utils.R' 'counts.R' 'dev.R' + 'import_metadata_and_counts.R' 'metadata.R' 'seurat.R' 'unharmonised.R' diff --git a/NAMESPACE b/NAMESPACE index 7b7c4ed..41c9a7d 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -10,6 +10,7 @@ export(get_metadata) export(get_seurat) export(get_single_cell_experiment) export(get_unharmonised_metadata) +export(import_metadata_counts) importFrom(BiocGenerics,cbind) importFrom(DBI,dbConnect) importFrom(DBI,dbDisconnect) @@ -24,8 +25,16 @@ importFrom(SingleCellExperiment,combineCols) importFrom(SummarizedExperiment,"assayNames<-") importFrom(SummarizedExperiment,"colData<-") importFrom(SummarizedExperiment,colData) +importFrom(arrow,write_parquet) importFrom(assertthat,assert_that) importFrom(assertthat,has_name) +importFrom(checkmate,check_character) +importFrom(checkmate,check_directory_exists) +importFrom(checkmate,check_file_exists) +importFrom(checkmate,check_set_equal) +importFrom(checkmate,check_subset) +importFrom(checkmate,check_tibble) +importFrom(checkmate,check_true) importFrom(cli,cli_abort) importFrom(cli,cli_alert_info) importFrom(cli,cli_alert_success) diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index b20f14e..09a1f3f 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -2,17 +2,14 @@ assay_map <- c(counts = "original", cpm = "cpm") -#' Checks metadata meets importing criteria. -#' Returns +#' Checks importing criteria for new atlas #' Calculating counts per million from raw counts -#' Gets the Curated Atlas metadata as a data frame. #' -#' -#' @param meta_input +#' @param meta_input A tibble #' @param cache_dir Optional character vector of length 1. A file path on #' your local system to a directory (not a file) that will be used to store #' `metadata.parquet` -#' @param meta_output +#' @param meta_output A parquet #' @return A directory stores counts per million #' @export #' @examples @@ -21,12 +18,11 @@ assay_map <- c(counts = "original", #' meta_output = "example_metadata", #' cache_dir = tempdir()) #' -#' @importFrom assertthat assertthat #' @importFrom checkmate check_tibble check_directory_exists check_set_equal check_true check_character check_subset check_file_exists #' @importFrom dplyr tbl #' @importFrom cli cli_alert_info #' @importFrom glue glue -#' @importFrom arrow read_parquet +#' @importFrom arrow write_parquet import_metadata_counts <- function(meta_input, @@ -86,7 +82,7 @@ import_metadata_counts <- function(meta_input, cpm_path <- glue("{output_dir}/{file_name}/") # Iterate count_per_millions.R to generate cpm for each raw count command <- - glue("Rscript ./count_per_millions.R {count_path} {cpm_path}" ) + glue("Rscript {cache_dir}/count_per_millions.R {count_path} {cpm_path}" ) system(command) } diff --git a/man/import_metadata_counts.Rd b/man/import_metadata_counts.Rd new file mode 100644 index 0000000..2eff46e --- /dev/null +++ b/man/import_metadata_counts.Rd @@ -0,0 +1,41 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/import_metadata_and_counts.R +\name{import_metadata_counts} +\alias{import_metadata_counts} +\title{Checks metadata meets importing criteria. +Returns +Calculating counts per million from raw counts +Gets the Curated Atlas metadata as a data frame.} +\usage{ +import_metadata_counts( + meta_input, + cache_dir = CuratedAtlasQueryR:::get_default_cache_dir(), + meta_output, + version = "0.2.3" +) +} +\arguments{ +\item{meta_input}{A tibble} + +\item{cache_dir}{Optional character vector of length 1. A file path on +your local system to a directory (not a file) that will be used to store +\code{metadata.parquet}} + +\item{meta_output}{A parquet} +} +\value{ +A directory stores counts per million +} +\description{ +Checks metadata meets importing criteria. +Returns +Calculating counts per million from raw counts +Gets the Curated Atlas metadata as a data frame. +} +\examples{ +example_metadata <- get_metadata() |> head(3) |> as_tibble() +check_new_metadata <- import_metadata_counts(meta_input = example_metadata, + meta_output = "example_metadata", + cache_dir = tempdir()) + +} From 0e5e27c38ca67b42332517b62356ef5b243626c9 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Tue, 5 Mar 2024 21:41:24 +1100 Subject: [PATCH 50/85] fix --- R/import_metadata_and_counts.R | 36 +++++++++++++++------------------- man/import_metadata_counts.Rd | 16 ++++++--------- 2 files changed, 22 insertions(+), 30 deletions(-) diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 09a1f3f..33c049d 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -1,6 +1,4 @@ -# Maps user provided assay names to their corresponding paths in the repository -assay_map <- c(counts = "original", - cpm = "cpm") + #' Checks importing criteria for new atlas #' Calculating counts per million from raw counts @@ -10,36 +8,34 @@ assay_map <- c(counts = "original", #' your local system to a directory (not a file) that will be used to store #' `metadata.parquet` #' @param meta_output A parquet -#' @return A directory stores counts per million #' @export +#' @return A directory stores counts per million #' @examples #' example_metadata <- get_metadata() |> head(3) |> as_tibble() -#' check_new_metadata <- import_metadata_counts(meta_input = example_metadata, -#' meta_output = "example_metadata", -#' cache_dir = tempdir()) +#' import_metadata_counts(meta_input = example_metadata, +#' meta_output = "example_metadata", +#' cache_dir = tempdir()) #' +#' @importFrom assertthat assert_that #' @importFrom checkmate check_tibble check_directory_exists check_set_equal check_true check_character check_subset check_file_exists #' @importFrom dplyr tbl #' @importFrom cli cli_alert_info #' @importFrom glue glue #' @importFrom arrow write_parquet - - import_metadata_counts <- function(meta_input, cache_dir = CuratedAtlasQueryR:::get_default_cache_dir(), meta_output, version = "0.2.3") { - # A few checks from here - counts <- assay_map["counts"] - cpm <- assay_map['cpm'] + # Convert to tibble if meta_input is not a tibble + meta_input <- meta_input |> as_tibble() check_tibble(meta_input) check_directory_exists(cache_dir) - check_directory_exists(file.path(cache_dir, counts)) + check_directory_exists(file.path(cache_dir, "original")) sce <- meta_input |> get_single_cell_experiment() se <- meta_input |> get_seurat() - + counts_data <- assays(sce)$counts |> as_tibble() assert_that(inherits(sce, "SingleCellExperiment"), @@ -48,12 +44,12 @@ import_metadata_counts <- function(meta_input, msg = "Gene names cannot contain Ensembl IDs.") assert_that(all(counts_data >= 0), msg = "Counts for SingleCellExperiment cannot be negative.") - all(meta_input |> pull(file_id_db) %in% dir(file.path(cache_dir, counts))) |> + all(meta_input |> pull(file_id_db) %in% dir(file.path(cache_dir, "original"))) |> assert_that(msg = "The metadata sample file ID and the count file ID does not match") write_parquet(meta_input, file.path(cache_dir, glue("{meta_output}.{version}.parquet"))) # check the number of sub directories in original match cpm - check_set_equal(length(list.dirs(file.path(cache_dir, counts))), length(list.dirs(file.path(cache_dir, cpm)))) + check_set_equal(length(list.dirs(file.path(cache_dir, "original"))), length(list.dirs(file.path(cache_dir, "cpm")))) # check the metadata contains cell_, file_id_db, sample_ with correct types check_true("cell_" %in% names(meta_input)) @@ -70,11 +66,11 @@ import_metadata_counts <- function(meta_input, meta_input |> distinct(sex) |> pull() |> check_subset(c('female','male','unknown')) # if checkpoints above pass, generate cpm - cli_alert_info("Generating cpm from {.path {cache_dir}/{counts}}. ") + cli_alert_info("Generating cpm from {.path {cache_dir}/original}. ") check_file_exists("count_per_millions.R") - input_dir <- file.path(cache_dir, counts) - output_dir <- file.path(cache_dir, cpm) + input_dir <- file.path(cache_dir, "original") + output_dir <- file.path(cache_dir, "cpm") for (subdir in list.files(input_dir, full.names = TRUE)) { file_name <- basename(subdir) @@ -86,7 +82,7 @@ import_metadata_counts <- function(meta_input, system(command) } - cli_alert_info("cpm are generated in {.path {cache_dir}/{cpm}}. ") + cli_alert_info("cpm are generated in {.path {cache_dir}/cpm}. ") } diff --git a/man/import_metadata_counts.Rd b/man/import_metadata_counts.Rd index 2eff46e..3059d6e 100644 --- a/man/import_metadata_counts.Rd +++ b/man/import_metadata_counts.Rd @@ -2,10 +2,8 @@ % Please edit documentation in R/import_metadata_and_counts.R \name{import_metadata_counts} \alias{import_metadata_counts} -\title{Checks metadata meets importing criteria. -Returns -Calculating counts per million from raw counts -Gets the Curated Atlas metadata as a data frame.} +\title{Checks importing criteria for new atlas +Calculating counts per million from raw counts} \usage{ import_metadata_counts( meta_input, @@ -27,15 +25,13 @@ your local system to a directory (not a file) that will be used to store A directory stores counts per million } \description{ -Checks metadata meets importing criteria. -Returns +Checks importing criteria for new atlas Calculating counts per million from raw counts -Gets the Curated Atlas metadata as a data frame. } \examples{ example_metadata <- get_metadata() |> head(3) |> as_tibble() -check_new_metadata <- import_metadata_counts(meta_input = example_metadata, - meta_output = "example_metadata", - cache_dir = tempdir()) +import_metadata_counts(meta_input = example_metadata, + meta_output = "example_metadata", + cache_dir = tempdir()) } From 02d45110fc1ab9960057f2a9bc451363e5a3ba9a Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Wed, 6 Mar 2024 09:14:01 +1100 Subject: [PATCH 51/85] try fix code suggested by knitr error --- R/import_metadata_and_counts.R | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 33c049d..b371a89 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -44,8 +44,10 @@ import_metadata_counts <- function(meta_input, msg = "Gene names cannot contain Ensembl IDs.") assert_that(all(counts_data >= 0), msg = "Counts for SingleCellExperiment cannot be negative.") - all(meta_input |> pull(file_id_db) %in% dir(file.path(cache_dir, "original"))) |> - assert_that(msg = "The metadata sample file ID and the count file ID does not match") + all_file_exist <- all(meta_input |> pull(file_id_db) %in% dir(file.path(cache_dir, "original"))) + assert_that(all_file_exist, "The metadata sample file ID and the count file ID does not match" ) + #all(meta_input |> pull(file_id_db) %in% dir(file.path(cache_dir, "original"))) |> + # assert_that(msg = "The metadata sample file ID and the count file ID does not match") write_parquet(meta_input, file.path(cache_dir, glue("{meta_output}.{version}.parquet"))) # check the number of sub directories in original match cpm From e802374130d4d47be2243380b1ad9488fe55e72b Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Wed, 6 Mar 2024 09:17:34 +1100 Subject: [PATCH 52/85] Revert "try fix code suggested by knitr error" This reverts commit 02d45110fc1ab9960057f2a9bc451363e5a3ba9a. --- R/import_metadata_and_counts.R | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index b371a89..33c049d 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -44,10 +44,8 @@ import_metadata_counts <- function(meta_input, msg = "Gene names cannot contain Ensembl IDs.") assert_that(all(counts_data >= 0), msg = "Counts for SingleCellExperiment cannot be negative.") - all_file_exist <- all(meta_input |> pull(file_id_db) %in% dir(file.path(cache_dir, "original"))) - assert_that(all_file_exist, "The metadata sample file ID and the count file ID does not match" ) - #all(meta_input |> pull(file_id_db) %in% dir(file.path(cache_dir, "original"))) |> - # assert_that(msg = "The metadata sample file ID and the count file ID does not match") + all(meta_input |> pull(file_id_db) %in% dir(file.path(cache_dir, "original"))) |> + assert_that(msg = "The metadata sample file ID and the count file ID does not match") write_parquet(meta_input, file.path(cache_dir, glue("{meta_output}.{version}.parquet"))) # check the number of sub directories in original match cpm From 19697961a75efb06d18971247e4ff327457db103 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Wed, 6 Mar 2024 11:16:10 +1100 Subject: [PATCH 53/85] remove meta_output argument, add counts_path argument --- NAMESPACE | 1 + R/import_metadata_and_counts.R | 50 ++++++++++++++++++---------------- man/import_metadata_counts.Rd | 20 ++++++++------ 3 files changed, 39 insertions(+), 32 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 41c9a7d..c3baba3 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -53,6 +53,7 @@ importFrom(dplyr,summarise) importFrom(dplyr,tbl) importFrom(dplyr,transmute) importFrom(duckdb,duckdb) +importFrom(fs,dir_copy) importFrom(glue,glue) importFrom(glue,glue_sql) importFrom(httr,GET) diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 33c049d..5f4951c 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -3,18 +3,19 @@ #' Checks importing criteria for new atlas #' Calculating counts per million from raw counts #' -#' @param meta_input A tibble +#' @param metadata_tbl A tibble #' @param cache_dir Optional character vector of length 1. A file path on #' your local system to a directory (not a file) that will be used to store #' `metadata.parquet` -#' @param meta_output A parquet +#' @param counts_path A character vector of original counts path +#' @param version A character vector of metadata version. Default is 0.2.3 #' @export -#' @return A directory stores counts per million +#' @return A metadata.parquet with version, and a directory stores counts per million in cache directory #' @examples #' example_metadata <- get_metadata() |> head(3) |> as_tibble() -#' import_metadata_counts(meta_input = example_metadata, -#' meta_output = "example_metadata", -#' cache_dir = tempdir()) +#' import_metadata_counts(metadata_tbl = example_metadata, +#' cache_dir = get_default_cache_dir()), +#' counts_path = "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T//RtmpUjWDqt/original" #' #' @importFrom assertthat assert_that #' @importFrom checkmate check_tibble check_directory_exists check_set_equal check_true check_character check_subset check_file_exists @@ -22,18 +23,21 @@ #' @importFrom cli cli_alert_info #' @importFrom glue glue #' @importFrom arrow write_parquet -import_metadata_counts <- function(meta_input, +#' @importFrom fs dir_copy +import_metadata_counts <- function(metadata_tbl, cache_dir = CuratedAtlasQueryR:::get_default_cache_dir(), - meta_output, - version = "0.2.3") { - # Convert to tibble if meta_input is not a tibble - meta_input <- meta_input |> as_tibble() - check_tibble(meta_input) + version = "0.2.3", + counts_path) { + # Convert to tibble if metadata_tbl is not a tibble + metadata_tbl <- metadata_tbl |> as_tibble() + check_tibble(metadata_tbl) check_directory_exists(cache_dir) + check_directory_exists(counts_path) + fs::dir_copy(counts_path, cache_dir) check_directory_exists(file.path(cache_dir, "original")) - sce <- meta_input |> + sce <- metadata_tbl |> get_single_cell_experiment() - se <- meta_input |> + se <- metadata_tbl |> get_seurat() counts_data <- assays(sce)$counts |> @@ -44,26 +48,26 @@ import_metadata_counts <- function(meta_input, msg = "Gene names cannot contain Ensembl IDs.") assert_that(all(counts_data >= 0), msg = "Counts for SingleCellExperiment cannot be negative.") - all(meta_input |> pull(file_id_db) %in% dir(file.path(cache_dir, "original"))) |> + all(metadata_tbl |> pull(file_id_db) %in% dir(file.path(cache_dir, "original"))) |> assert_that(msg = "The metadata sample file ID and the count file ID does not match") - write_parquet(meta_input, file.path(cache_dir, glue("{meta_output}.{version}.parquet"))) + write_parquet(metadata_tbl, file.path(cache_dir, glue("metadata.{version}.parquet"))) # check the number of sub directories in original match cpm check_set_equal(length(list.dirs(file.path(cache_dir, "original"))), length(list.dirs(file.path(cache_dir, "cpm")))) # check the metadata contains cell_, file_id_db, sample_ with correct types - check_true("cell_" %in% names(meta_input)) - check_true("file_id_db" %in% names(meta_input)) - check_true("sample_" %in% names(meta_input)) - meta_input |> select(cell_, file_id_db, sample_) |> sapply(class) |> check_character() + check_true("cell_" %in% names(metadata_tbl)) + check_true("file_id_db" %in% names(metadata_tbl)) + check_true("sample_" %in% names(metadata_tbl)) + metadata_tbl |> select(cell_, file_id_db, sample_) |> sapply(class) |> check_character() # check age_days is either -99 or greater than 365 - assert_that(any(meta_input$age_days==-99 | meta_input$age_days> 365), + assert_that(any(metadata_tbl$age_days==-99 | metadata_tbl$age_days> 365), msg = "age_days should be either -99 for unknown or greater than 365") # check sex capitalisation then convert to lower case - meta_input <- meta_input |> mutate(sex = tolower(sex)) - meta_input |> distinct(sex) |> pull() |> check_subset(c('female','male','unknown')) + metadata_tbl <- metadata_tbl |> mutate(sex = tolower(sex)) + metadata_tbl |> distinct(sex) |> pull() |> check_subset(c('female','male','unknown')) # if checkpoints above pass, generate cpm cli_alert_info("Generating cpm from {.path {cache_dir}/original}. ") diff --git a/man/import_metadata_counts.Rd b/man/import_metadata_counts.Rd index 3059d6e..e8b66fb 100644 --- a/man/import_metadata_counts.Rd +++ b/man/import_metadata_counts.Rd @@ -6,23 +6,25 @@ Calculating counts per million from raw counts} \usage{ import_metadata_counts( - meta_input, + metadata_tbl, cache_dir = CuratedAtlasQueryR:::get_default_cache_dir(), - meta_output, - version = "0.2.3" + version = "0.2.3", + counts_path ) } \arguments{ -\item{meta_input}{A tibble} +\item{metadata_tbl}{A tibble} \item{cache_dir}{Optional character vector of length 1. A file path on your local system to a directory (not a file) that will be used to store \code{metadata.parquet}} -\item{meta_output}{A parquet} +\item{version}{A character vector of metadata version. Default is 0.2.3} + +\item{counts_path}{A character vector of original counts path} } \value{ -A directory stores counts per million +A metadata.parquet with version, and a directory stores counts per million in cache directory } \description{ Checks importing criteria for new atlas @@ -30,8 +32,8 @@ Calculating counts per million from raw counts } \examples{ example_metadata <- get_metadata() |> head(3) |> as_tibble() -import_metadata_counts(meta_input = example_metadata, - meta_output = "example_metadata", - cache_dir = tempdir()) +import_metadata_counts(metadata_tbl = example_metadata, + cache_dir = get_default_cache_dir()), + counts_path = "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T//RtmpUjWDqt/original" } From bf8aa68e050187d16854704c900adaf1c7fa44fc Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Wed, 6 Mar 2024 13:37:20 +1100 Subject: [PATCH 54/85] add counts_per_million function --- DESCRIPTION | 1 + NAMESPACE | 2 ++ R/counts_per_million.R | 49 +++++++++++++++++++++++++++++++++++ man/get_counts_per_million.Rd | 25 ++++++++++++++++++ 4 files changed, 77 insertions(+) create mode 100644 R/counts_per_million.R create mode 100644 man/get_counts_per_million.Rd diff --git a/DESCRIPTION b/DESCRIPTION index 910ba89..56ae731 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -130,6 +130,7 @@ Roxygen: list(markdown = TRUE) Collate: 'utils.R' 'counts.R' + 'counts_per_million.R' 'dev.R' 'import_metadata_and_counts.R' 'metadata.R' diff --git a/NAMESPACE b/NAMESPACE index c3baba3..dd9d455 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -4,6 +4,7 @@ S3method(as.sparse,DelayedMatrix) export(SAMPLE_DATABASE_URL) export(clear_cache) export(get_SingleCellExperiment) +export(get_counts_per_million) export(get_database_url) export(get_default_cache_dir) export(get_metadata) @@ -75,6 +76,7 @@ importFrom(purrr,reduce) importFrom(purrr,set_names) importFrom(purrr,walk) importFrom(rlang,.data) +importFrom(scuttle,calculateCPM) importFrom(stats,setNames) importFrom(stringr,str_remove_all) importFrom(stringr,str_replace_all) diff --git a/R/counts_per_million.R b/R/counts_per_million.R new file mode 100644 index 0000000..c68455a --- /dev/null +++ b/R/counts_per_million.R @@ -0,0 +1,49 @@ +#' Generating counts per million +#' +#' @param input_file A character vector of raw counts path +#' @param output_file A character vector of counts per million path +#' @export +#' @return A directory stores counts per million +#' @examples +#' input_file <- "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T/RtmpUjWDqt/original/12eb5fe25994253c1d320ca590a6e681" +#' output_file <- "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T/RtmpUjWDqt/cpm/12eb5fe25994253c1d320ca590a6e681" +#' get_counts_per_million(input_file, output_file) +#' +#' @importFrom SingleCellExperiment SingleCellExperiment +#' @importFrom dplyr tbl +#' @importFrom glue glue +#' @importFrom HDF5Array loadHDF5SummarizedExperiment +#' @importFrom scuttle calculateCPM + +get_counts_per_million <- function(input_file, output_file) { + + # Create directory + output_file |> dirname() |> dir.create( showWarnings = FALSE, recursive = TRUE) + + # Read file_cell_types + data = loadHDF5SummarizedExperiment(input_file) + + # Avoid completely empty cells + col_sums = colSums(data@assays@data$X) + which_to_select = which(col_sums >0 & col_sums < Inf) + + sce = SingleCellExperiment(list(counts_per_million = calculateCPM(data[,which_to_select ,drop=FALSE ], assay.type = "X"))) + rownames(sce) = rownames(data[,which_to_select ]) + colnames(sce) = colnames(data[,which_to_select ]) + + # Avoid scaling zeros + which_to_select_not = setdiff(1:ncol(data), which_to_select) + sce_zero = SingleCellExperiment(list(counts_per_million = data@assays@data$X[,which_to_select_not ,drop=FALSE ])) + rownames(sce_zero) = rownames(data[,which_to_select_not ]) + colnames(sce_zero) = colnames(data[,which_to_select_not ]) + + sce = sce |> cbind(sce_zero) + + sce = sce[,colnames(data)] + + rm(data) + gc() + + sce |> saveHDF5SummarizedExperiment(output_file, replace=TRUE) +} + diff --git a/man/get_counts_per_million.Rd b/man/get_counts_per_million.Rd new file mode 100644 index 0000000..380dbd3 --- /dev/null +++ b/man/get_counts_per_million.Rd @@ -0,0 +1,25 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/counts_per_million.R +\name{get_counts_per_million} +\alias{get_counts_per_million} +\title{Generating counts per million} +\usage{ +get_counts_per_million(input_file, output_file) +} +\arguments{ +\item{input_file}{A character vector of raw counts path} + +\item{output_file}{A character vector of counts per million path} +} +\value{ +A directory stores counts per million +} +\description{ +Generating counts per million +} +\examples{ +input_file <- "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T/RtmpUjWDqt/original/12eb5fe25994253c1d320ca590a6e681" +output_file <- "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T/RtmpUjWDqt/cpm/12eb5fe25994253c1d320ca590a6e681" +get_counts_per_million(input_file, output_file) + +} From 56c32ea324a635de5b8a9d8d3419d1ce29781244 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Wed, 6 Mar 2024 14:17:56 +1100 Subject: [PATCH 55/85] fix error --- NAMESPACE | 1 + R/counts_per_million.R | 4 ++-- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index dd9d455..455d4fb 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -17,6 +17,7 @@ importFrom(DBI,dbConnect) importFrom(DBI,dbDisconnect) importFrom(HDF5Array,HDF5RealizationSink) importFrom(HDF5Array,loadHDF5SummarizedExperiment) +importFrom(HDF5Array,saveHDF5SummarizedExperiment) importFrom(S4Vectors,DataFrame) importFrom(Seurat,as.SingleCellExperiment) importFrom(SeuratObject,as.Seurat) diff --git a/R/counts_per_million.R b/R/counts_per_million.R index c68455a..a602e45 100644 --- a/R/counts_per_million.R +++ b/R/counts_per_million.R @@ -12,7 +12,7 @@ #' @importFrom SingleCellExperiment SingleCellExperiment #' @importFrom dplyr tbl #' @importFrom glue glue -#' @importFrom HDF5Array loadHDF5SummarizedExperiment +#' @importFrom HDF5Array loadHDF5SummarizedExperiment saveHDF5SummarizedExperiment #' @importFrom scuttle calculateCPM get_counts_per_million <- function(input_file, output_file) { @@ -24,7 +24,7 @@ get_counts_per_million <- function(input_file, output_file) { data = loadHDF5SummarizedExperiment(input_file) # Avoid completely empty cells - col_sums = colSums(data@assays@data$X) + col_sums = colSums(as.matrix(data@assays@data$X)) which_to_select = which(col_sums >0 & col_sums < Inf) sce = SingleCellExperiment(list(counts_per_million = calculateCPM(data[,which_to_select ,drop=FALSE ], assay.type = "X"))) From 593dcc7e64619afbfd4bebc73ec523acff837b1b Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Wed, 6 Mar 2024 15:39:19 +1100 Subject: [PATCH 56/85] fix memory issue in counts_per_million --- NAMESPACE | 1 + R/counts_per_million.R | 9 +++++++-- R/import_metadata_and_counts.R | 16 ++++++++-------- man/import_metadata_counts.Rd | 5 +++-- 4 files changed, 19 insertions(+), 12 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 455d4fb..bedbbf1 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -15,6 +15,7 @@ export(import_metadata_counts) importFrom(BiocGenerics,cbind) importFrom(DBI,dbConnect) importFrom(DBI,dbDisconnect) +importFrom(DelayedArray,realize) importFrom(HDF5Array,HDF5RealizationSink) importFrom(HDF5Array,loadHDF5SummarizedExperiment) importFrom(HDF5Array,saveHDF5SummarizedExperiment) diff --git a/R/counts_per_million.R b/R/counts_per_million.R index a602e45..9689d4f 100644 --- a/R/counts_per_million.R +++ b/R/counts_per_million.R @@ -14,6 +14,8 @@ #' @importFrom glue glue #' @importFrom HDF5Array loadHDF5SummarizedExperiment saveHDF5SummarizedExperiment #' @importFrom scuttle calculateCPM +#' @importFrom DelayedArray realize +#' @importFrom purrr map get_counts_per_million <- function(input_file, output_file) { @@ -24,10 +26,10 @@ get_counts_per_million <- function(input_file, output_file) { data = loadHDF5SummarizedExperiment(input_file) # Avoid completely empty cells - col_sums = colSums(as.matrix(data@assays@data$X)) + col_sums = colSums(data@assays@data$X) which_to_select = which(col_sums >0 & col_sums < Inf) - sce = SingleCellExperiment(list(counts_per_million = calculateCPM(data[,which_to_select ,drop=FALSE ], assay.type = "X"))) + sce = SingleCellExperiment(list(counts_per_million = scuttle::calculateCPM(data[,which_to_select ,drop=FALSE ], assay.type = "X"))) rownames(sce) = rownames(data[,which_to_select ]) colnames(sce) = colnames(data[,which_to_select ]) @@ -44,6 +46,9 @@ get_counts_per_million <- function(input_file, output_file) { rm(data) gc() + # Check if there is a memory issue + assays(sce) <- assays(sce) |> map(realize) + sce |> saveHDF5SummarizedExperiment(output_file, replace=TRUE) } diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 5f4951c..76b8ab5 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -14,8 +14,9 @@ #' @examples #' example_metadata <- get_metadata() |> head(3) |> as_tibble() #' import_metadata_counts(metadata_tbl = example_metadata, -#' cache_dir = get_default_cache_dir()), -#' counts_path = "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T//RtmpUjWDqt/original" +#' cache_dir = get_default_cache_dir(), +#' counts_path = "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T//RtmpUjWDqt/original", +#' counts_to_cpm_file = "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T//RtmpUjWDqt/counts_per_million.R" #' #' @importFrom assertthat assert_that #' @importFrom checkmate check_tibble check_directory_exists check_set_equal check_true check_character check_subset check_file_exists @@ -33,7 +34,8 @@ import_metadata_counts <- function(metadata_tbl, check_tibble(metadata_tbl) check_directory_exists(cache_dir) check_directory_exists(counts_path) - fs::dir_copy(counts_path, cache_dir) + dir_copy(counts_path, cache_dir) + file_copy(counts_to_cpm_file, cache_dir) check_directory_exists(file.path(cache_dir, "original")) sce <- metadata_tbl |> get_single_cell_experiment() @@ -71,7 +73,6 @@ import_metadata_counts <- function(metadata_tbl, # if checkpoints above pass, generate cpm cli_alert_info("Generating cpm from {.path {cache_dir}/original}. ") - check_file_exists("count_per_millions.R") input_dir <- file.path(cache_dir, "original") output_dir <- file.path(cache_dir, "cpm") @@ -80,10 +81,9 @@ import_metadata_counts <- function(metadata_tbl, file_name <- basename(subdir) count_path <- glue("{input_dir}/{file_name}/") cpm_path <- glue("{output_dir}/{file_name}/") - # Iterate count_per_millions.R to generate cpm for each raw count - command <- - glue("Rscript {cache_dir}/count_per_millions.R {count_path} {cpm_path}" ) - system(command) + # Iterate get_counts_per_million function to generate cpm for each raw count + get_counts_per_million(input_file = count_path, + output_file = cpm_path) } cli_alert_info("cpm are generated in {.path {cache_dir}/cpm}. ") diff --git a/man/import_metadata_counts.Rd b/man/import_metadata_counts.Rd index e8b66fb..a38fd3f 100644 --- a/man/import_metadata_counts.Rd +++ b/man/import_metadata_counts.Rd @@ -33,7 +33,8 @@ Calculating counts per million from raw counts \examples{ example_metadata <- get_metadata() |> head(3) |> as_tibble() import_metadata_counts(metadata_tbl = example_metadata, - cache_dir = get_default_cache_dir()), - counts_path = "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T//RtmpUjWDqt/original" + cache_dir = get_default_cache_dir(), + counts_path = "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T//RtmpUjWDqt/original", + counts_to_cpm_file = "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T//RtmpUjWDqt/counts_per_million.R" } From 815648c8d89e2c16f743dd674d99388f9ec77ddf Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Wed, 6 Mar 2024 15:42:55 +1100 Subject: [PATCH 57/85] fix documentation --- R/import_metadata_and_counts.R | 3 +-- man/import_metadata_counts.Rd | 3 +-- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 76b8ab5..7dc77fd 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -15,8 +15,7 @@ #' example_metadata <- get_metadata() |> head(3) |> as_tibble() #' import_metadata_counts(metadata_tbl = example_metadata, #' cache_dir = get_default_cache_dir(), -#' counts_path = "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T//RtmpUjWDqt/original", -#' counts_to_cpm_file = "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T//RtmpUjWDqt/counts_per_million.R" +#' counts_path = "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T/RtmpeAFYzL/original") #' #' @importFrom assertthat assert_that #' @importFrom checkmate check_tibble check_directory_exists check_set_equal check_true check_character check_subset check_file_exists diff --git a/man/import_metadata_counts.Rd b/man/import_metadata_counts.Rd index a38fd3f..4e05772 100644 --- a/man/import_metadata_counts.Rd +++ b/man/import_metadata_counts.Rd @@ -34,7 +34,6 @@ Calculating counts per million from raw counts example_metadata <- get_metadata() |> head(3) |> as_tibble() import_metadata_counts(metadata_tbl = example_metadata, cache_dir = get_default_cache_dir(), - counts_path = "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T//RtmpUjWDqt/original", - counts_to_cpm_file = "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T//RtmpUjWDqt/counts_per_million.R" + counts_path = "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T/RtmpeAFYzL/original") } From e7e90478c616fd0be6344d4d0cd0102586d44114 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Thu, 7 Mar 2024 10:39:01 +1100 Subject: [PATCH 58/85] add check: cells_ cannot be duplicates when join with api --- R/counts_per_million.R | 2 +- R/import_metadata_and_counts.R | 11 +++++++++-- 2 files changed, 10 insertions(+), 3 deletions(-) diff --git a/R/counts_per_million.R b/R/counts_per_million.R index 9689d4f..decd53d 100644 --- a/R/counts_per_million.R +++ b/R/counts_per_million.R @@ -26,7 +26,7 @@ get_counts_per_million <- function(input_file, output_file) { data = loadHDF5SummarizedExperiment(input_file) # Avoid completely empty cells - col_sums = colSums(data@assays@data$X) + col_sums = colSums(as.matrix(data@assays@data$X)) which_to_select = which(col_sums >0 & col_sums < Inf) sce = SingleCellExperiment(list(counts_per_million = scuttle::calculateCPM(data[,which_to_select ,drop=FALSE ], assay.type = "X"))) diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 7dc77fd..79b3fe6 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -34,7 +34,6 @@ import_metadata_counts <- function(metadata_tbl, check_directory_exists(cache_dir) check_directory_exists(counts_path) dir_copy(counts_path, cache_dir) - file_copy(counts_to_cpm_file, cache_dir) check_directory_exists(file.path(cache_dir, "original")) sce <- metadata_tbl |> get_single_cell_experiment() @@ -49,9 +48,10 @@ import_metadata_counts <- function(metadata_tbl, msg = "Gene names cannot contain Ensembl IDs.") assert_that(all(counts_data >= 0), msg = "Counts for SingleCellExperiment cannot be negative.") + + # check metadata sample file ID match the count file ID all(metadata_tbl |> pull(file_id_db) %in% dir(file.path(cache_dir, "original"))) |> assert_that(msg = "The metadata sample file ID and the count file ID does not match") - write_parquet(metadata_tbl, file.path(cache_dir, glue("metadata.{version}.parquet"))) # check the number of sub directories in original match cpm check_set_equal(length(list.dirs(file.path(cache_dir, "original"))), length(list.dirs(file.path(cache_dir, "cpm")))) @@ -62,6 +62,10 @@ import_metadata_counts <- function(metadata_tbl, check_true("sample_" %in% names(metadata_tbl)) metadata_tbl |> select(cell_, file_id_db, sample_) |> sapply(class) |> check_character() + # check cell_ values are not duplicated when join with parquet + cells <- get_metadata() |> select(cell_) |> as_tibble() + any(metadata_tbl$cell_ %in% cells$cell_) |> assert_that(msg = "cell_ cannot be duplicates (cell_ exists in API)") + # check age_days is either -99 or greater than 365 assert_that(any(metadata_tbl$age_days==-99 | metadata_tbl$age_days> 365), msg = "age_days should be either -99 for unknown or greater than 365") @@ -70,6 +74,9 @@ import_metadata_counts <- function(metadata_tbl, metadata_tbl <- metadata_tbl |> mutate(sex = tolower(sex)) metadata_tbl |> distinct(sex) |> pull() |> check_subset(c('female','male','unknown')) + # convert metadata_tbl to parquet if above checkpoints pass + write_parquet(metadata_tbl, file.path(cache_dir, glue("metadata.{version}.parquet"))) + # if checkpoints above pass, generate cpm cli_alert_info("Generating cpm from {.path {cache_dir}/original}. ") From 62525e5254b445d99ddfc9359d7e3fbbeae48bd7 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Thu, 7 Mar 2024 11:40:45 +1100 Subject: [PATCH 59/85] add checkpoint, cell_ values in metadata must be unique --- R/import_metadata_and_counts.R | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 79b3fe6..596f533 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -62,9 +62,12 @@ import_metadata_counts <- function(metadata_tbl, check_true("sample_" %in% names(metadata_tbl)) metadata_tbl |> select(cell_, file_id_db, sample_) |> sapply(class) |> check_character() + # check cell_ values in metadata_tbl is unique + (anyDuplicated(metadata_tbl$cell_) == 0 ) |> assert_that(msg = "cell_ in the metadata must be unique") + # check cell_ values are not duplicated when join with parquet cells <- get_metadata() |> select(cell_) |> as_tibble() - any(metadata_tbl$cell_ %in% cells$cell_) |> assert_that(msg = "cell_ cannot be duplicates (cell_ exists in API)") + any(metadata_tbl$cell_ %in% cells$cell_) |> assert_that(msg = "cell_ in the metadata must not exist in API") # check age_days is either -99 or greater than 365 assert_that(any(metadata_tbl$age_days==-99 | metadata_tbl$age_days> 365), From 1fdea73e5dc72b2e33149b07ec80ecba125ea332 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Fri, 8 Mar 2024 13:37:38 +1100 Subject: [PATCH 60/85] optimise code and comments --- R/counts.R | 4 +++- R/counts_per_million.R | 4 ++-- R/import_metadata_and_counts.R | 15 +++++++++++---- man/get_counts_per_million.Rd | 4 ++-- man/import_metadata_counts.Rd | 11 ++++------- 5 files changed, 22 insertions(+), 16 deletions(-) diff --git a/R/counts.R b/R/counts.R index 8e06663..6a2e3d0 100644 --- a/R/counts.R +++ b/R/counts.R @@ -217,7 +217,9 @@ group_to_sce <- function(i, df, dir_prefix, features) { "Some cells were filtered out while loading {head(df$file_id_db, 1)} because of extremely low counts. The number of cells in the SingleCellExperiment will be less than the - number of cells you have selected from the metadata." + number of cells you have selected from the metadata. + Are cell IDs duplicated? Or, do cell IDs correspond to the counts file? + " ) |> cli_alert_warning() df <- filter(df, .data$cell_ %in% cells) } diff --git a/R/counts_per_million.R b/R/counts_per_million.R index decd53d..c3c5693 100644 --- a/R/counts_per_million.R +++ b/R/counts_per_million.R @@ -5,8 +5,8 @@ #' @export #' @return A directory stores counts per million #' @examples -#' input_file <- "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T/RtmpUjWDqt/original/12eb5fe25994253c1d320ca590a6e681" -#' output_file <- "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T/RtmpUjWDqt/cpm/12eb5fe25994253c1d320ca590a6e681" +#' input_file <- "/Users/shen.m/projects/caq/import_api_pipelines/original/12eb5fe25994253c1d320ca590a6e681" +#' output_file <- "/Users/shen.m/projects/caq/import_api_pipelines/cpm/12eb5fe25994253c1d320ca590a6e681" #' get_counts_per_million(input_file, output_file) #' #' @importFrom SingleCellExperiment SingleCellExperiment diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 596f533..6444e69 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -7,7 +7,7 @@ #' @param cache_dir Optional character vector of length 1. A file path on #' your local system to a directory (not a file) that will be used to store #' `metadata.parquet` -#' @param counts_path A character vector of original counts path +#' @param counts_path A character vector of original counts path defined by users #' @param version A character vector of metadata version. Default is 0.2.3 #' @export #' @return A metadata.parquet with version, and a directory stores counts per million in cache directory @@ -15,7 +15,7 @@ #' example_metadata <- get_metadata() |> head(3) |> as_tibble() #' import_metadata_counts(metadata_tbl = example_metadata, #' cache_dir = get_default_cache_dir(), -#' counts_path = "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T/RtmpeAFYzL/original") +#' counts_path = "/Users/shen.m/projects/caq/import_api_pipelines/original") #' #' @importFrom assertthat assert_that #' @importFrom checkmate check_tibble check_directory_exists check_set_equal check_true check_character check_subset check_file_exists @@ -33,7 +33,14 @@ import_metadata_counts <- function(metadata_tbl, check_tibble(metadata_tbl) check_directory_exists(cache_dir) check_directory_exists(counts_path) - dir_copy(counts_path, cache_dir) + + # check count H5 directory name not included in the cache directory original + all(!dir(file.path(counts_path)) %in% dir(file.path(cache_dir, 'original'))) |> + check_false() |> + assert(msg='Count H5 directory name should not duplicate with that in cache directory') + + dir_copy(counts_path, file.path(cache_dir, "original")) + check_directory_exists(file.path(cache_dir, "original")) sce <- metadata_tbl |> get_single_cell_experiment() @@ -67,7 +74,7 @@ import_metadata_counts <- function(metadata_tbl, # check cell_ values are not duplicated when join with parquet cells <- get_metadata() |> select(cell_) |> as_tibble() - any(metadata_tbl$cell_ %in% cells$cell_) |> assert_that(msg = "cell_ in the metadata must not exist in API") + any(metadata_tbl$cell_ %in% cells$cell_) |> assert_that(msg = "cell_ in the metadata should not duplicate with that exists in API") # check age_days is either -99 or greater than 365 assert_that(any(metadata_tbl$age_days==-99 | metadata_tbl$age_days> 365), diff --git a/man/get_counts_per_million.Rd b/man/get_counts_per_million.Rd index 380dbd3..0bb4d20 100644 --- a/man/get_counts_per_million.Rd +++ b/man/get_counts_per_million.Rd @@ -18,8 +18,8 @@ A directory stores counts per million Generating counts per million } \examples{ -input_file <- "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T/RtmpUjWDqt/original/12eb5fe25994253c1d320ca590a6e681" -output_file <- "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T/RtmpUjWDqt/cpm/12eb5fe25994253c1d320ca590a6e681" +input_file <- "/Users/shen.m/projects/caq/import_api_pipelines/original/12eb5fe25994253c1d320ca590a6e681" +output_file <- "/Users/shen.m/projects/caq/import_api_pipelines/cpm/12eb5fe25994253c1d320ca590a6e681" get_counts_per_million(input_file, output_file) } diff --git a/man/import_metadata_counts.Rd b/man/import_metadata_counts.Rd index 4e05772..1614d51 100644 --- a/man/import_metadata_counts.Rd +++ b/man/import_metadata_counts.Rd @@ -3,12 +3,11 @@ \name{import_metadata_counts} \alias{import_metadata_counts} \title{Checks importing criteria for new atlas -Calculating counts per million from raw counts} +Generating counts per million from provided raw counts} \usage{ import_metadata_counts( metadata_tbl, cache_dir = CuratedAtlasQueryR:::get_default_cache_dir(), - version = "0.2.3", counts_path ) } @@ -19,21 +18,19 @@ import_metadata_counts( your local system to a directory (not a file) that will be used to store \code{metadata.parquet}} -\item{version}{A character vector of metadata version. Default is 0.2.3} - -\item{counts_path}{A character vector of original counts path} +\item{counts_path}{A character vector of original counts path defined by users} } \value{ A metadata.parquet with version, and a directory stores counts per million in cache directory } \description{ Checks importing criteria for new atlas -Calculating counts per million from raw counts +Generating counts per million from provided raw counts } \examples{ example_metadata <- get_metadata() |> head(3) |> as_tibble() import_metadata_counts(metadata_tbl = example_metadata, cache_dir = get_default_cache_dir(), - counts_path = "/var/folders/ls/99n281zx4bbd73kllmc1rc0h0005lj/T/RtmpeAFYzL/original") + counts_path = "/Users/shen.m/projects/caq/import_api_pipelines/original") } From 6aa3380ca66190972b8349e9d536fbe1aa0d5efc Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Fri, 8 Mar 2024 13:38:09 +1100 Subject: [PATCH 61/85] fix tiny issue --- R/import_metadata_and_counts.R | 34 ++++++++++++++-------------------- 1 file changed, 14 insertions(+), 20 deletions(-) diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 6444e69..d7ca917 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -1,14 +1,11 @@ - - #' Checks importing criteria for new atlas -#' Calculating counts per million from raw counts +#' Generating counts per million from provided raw counts #' #' @param metadata_tbl A tibble #' @param cache_dir Optional character vector of length 1. A file path on #' your local system to a directory (not a file) that will be used to store #' `metadata.parquet` #' @param counts_path A character vector of original counts path defined by users -#' @param version A character vector of metadata version. Default is 0.2.3 #' @export #' @return A metadata.parquet with version, and a directory stores counts per million in cache directory #' @examples @@ -26,34 +23,31 @@ #' @importFrom fs dir_copy import_metadata_counts <- function(metadata_tbl, cache_dir = CuratedAtlasQueryR:::get_default_cache_dir(), - version = "0.2.3", counts_path) { # Convert to tibble if metadata_tbl is not a tibble metadata_tbl <- metadata_tbl |> as_tibble() - check_tibble(metadata_tbl) check_directory_exists(cache_dir) check_directory_exists(counts_path) + check_directory_exists(file.path(cache_dir, "original")) # check count H5 directory name not included in the cache directory original - all(!dir(file.path(counts_path)) %in% dir(file.path(cache_dir, 'original'))) |> - check_false() |> - assert(msg='Count H5 directory name should not duplicate with that in cache directory') + all(!dir(file.path(counts_path)) %in% dir(file.path(cache_dir, 'original'))) |> + check_true() |> + assert(msg = 'Count H5 directory name should not duplicate with that in cache directory') - dir_copy(counts_path, file.path(cache_dir, "original")) + dir_copy(counts_path, file.path(cache_dir)) - check_directory_exists(file.path(cache_dir, "original")) - sce <- metadata_tbl |> - get_single_cell_experiment() - se <- metadata_tbl |> - get_seurat() - - counts_data <- assays(sce)$counts |> + sce <- metadata_tbl |> + get_single_cell_experiment(cache_directory = cache_dir) + se <- metadata_tbl |> get_seurat(cache_directory = cache_dir) + + counts_data <- assays(sce)$counts |> as_tibble() assert_that(inherits(sce, "SingleCellExperiment"), msg = "SingleCellExperiment Object is not created from metadata.") assert_that(!'^ENS' %in% rownames(se[['originalexp']]), msg = "Gene names cannot contain Ensembl IDs.") - assert_that(all(counts_data >= 0), + assert_that(all(counts_data >= 0), msg = "Counts for SingleCellExperiment cannot be negative.") # check metadata sample file ID match the count file ID @@ -74,7 +68,7 @@ import_metadata_counts <- function(metadata_tbl, # check cell_ values are not duplicated when join with parquet cells <- get_metadata() |> select(cell_) |> as_tibble() - any(metadata_tbl$cell_ %in% cells$cell_) |> assert_that(msg = "cell_ in the metadata should not duplicate with that exists in API") + (!any(metadata_tbl$cell_ %in% cells$cell_)) |> assert_that(msg = "cell_ in the metadata should not duplicate with that exists in API") # check age_days is either -99 or greater than 365 assert_that(any(metadata_tbl$age_days==-99 | metadata_tbl$age_days> 365), @@ -85,7 +79,7 @@ import_metadata_counts <- function(metadata_tbl, metadata_tbl |> distinct(sex) |> pull() |> check_subset(c('female','male','unknown')) # convert metadata_tbl to parquet if above checkpoints pass - write_parquet(metadata_tbl, file.path(cache_dir, glue("metadata.{version}.parquet"))) + write_parquet(metadata_tbl, file.path(cache_dir, glue("metadata.parquet"))) # if checkpoints above pass, generate cpm cli_alert_info("Generating cpm from {.path {cache_dir}/original}. ") From 7ba0f256cc1789c270d9e20d7a572ecdfa41e3b9 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Fri, 8 Mar 2024 15:19:19 +1100 Subject: [PATCH 62/85] assert_that --- R/import_metadata_and_counts.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index d7ca917..70782e2 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -33,7 +33,7 @@ import_metadata_counts <- function(metadata_tbl, # check count H5 directory name not included in the cache directory original all(!dir(file.path(counts_path)) %in% dir(file.path(cache_dir, 'original'))) |> check_true() |> - assert(msg = 'Count H5 directory name should not duplicate with that in cache directory') + assert_that(msg = 'Count H5 directory name should not duplicate with that in cache directory') dir_copy(counts_path, file.path(cache_dir)) From c9e0d8e83d12cd3414d142b80d6e83c24198a86b Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Tue, 12 Mar 2024 16:52:22 +1100 Subject: [PATCH 63/85] read rds, assign matrix for counts data, save HDF5 --- R/counts_per_million.R | 24 +++++++++++++++--------- 1 file changed, 15 insertions(+), 9 deletions(-) diff --git a/R/counts_per_million.R b/R/counts_per_million.R index c3c5693..1d9e9d7 100644 --- a/R/counts_per_million.R +++ b/R/counts_per_million.R @@ -1,13 +1,13 @@ -#' Generating counts per million +#' Generating counts per million from SingleCellExperiment RDS #' -#' @param input_file A character vector of raw counts path +#' @param input_file_rds A character vector of raw counts path #' @param output_file A character vector of counts per million path #' @export #' @return A directory stores counts per million #' @examples -#' input_file <- "/Users/shen.m/projects/caq/import_api_pipelines/original/12eb5fe25994253c1d320ca590a6e681" -#' output_file <- "/Users/shen.m/projects/caq/import_api_pipelines/cpm/12eb5fe25994253c1d320ca590a6e681" -#' get_counts_per_million(input_file, output_file) +#' input_file_rds <- "/Users/shen.m/projects/caq/import_api_pipelines/12eb5fe25994253c1d320ca590a6e999/sce.rds" +#' output_file <- "/Users/shen.m/projects/caq/cache_for_testing/cpm/12eb5fe25994253c1d320ca590a6e999/" +#' get_counts_per_million(input_file_rds, output_file) #' #' @importFrom SingleCellExperiment SingleCellExperiment #' @importFrom dplyr tbl @@ -17,13 +17,18 @@ #' @importFrom DelayedArray realize #' @importFrom purrr map -get_counts_per_million <- function(input_file, output_file) { +get_counts_per_million <- function(input_file_rds, output_file) { # Create directory output_file |> dirname() |> dir.create( showWarnings = FALSE, recursive = TRUE) # Read file_cell_types - data = loadHDF5SummarizedExperiment(input_file) + data = readRDS(input_file_rds) + + # Assign HDF5 matrix for counts data + hdf5_file <- file.path(dirname(input_file_rds), "assays.h5") + hdf5_counts <- writeHDF5Array(assays(data)$counts, filepath = hdf5_file, name = "assays.h5") + assays(data, withDimnames = FALSE)[["X"]] <- hdf5_counts # Avoid completely empty cells col_sums = colSums(as.matrix(data@assays@data$X)) @@ -47,8 +52,9 @@ get_counts_per_million <- function(input_file, output_file) { gc() # Check if there is a memory issue - assays(sce) <- assays(sce) |> map(realize) + assays(sce) <- assays(sce) |> purrr::map(realize) - sce |> saveHDF5SummarizedExperiment(output_file, replace=TRUE) + sce |> saveRDS(file.path(output_file,"se.rds")) + sce |> saveHDF5SummarizedExperiment(output_file) } From 944535d768a485593710b443e7240279cdf58102 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Wed, 13 Mar 2024 10:41:48 +1100 Subject: [PATCH 64/85] accept sce RDS as input --- R/counts_per_million.R | 5 ++-- R/import_metadata_and_counts.R | 54 ++++++++++++++++++++++------------ man/get_counts_per_million.Rd | 14 ++++----- man/import_metadata_counts.Rd | 2 +- 4 files changed, 47 insertions(+), 28 deletions(-) diff --git a/R/counts_per_million.R b/R/counts_per_million.R index 1d9e9d7..2487a08 100644 --- a/R/counts_per_million.R +++ b/R/counts_per_million.R @@ -5,7 +5,7 @@ #' @export #' @return A directory stores counts per million #' @examples -#' input_file_rds <- "/Users/shen.m/projects/caq/import_api_pipelines/12eb5fe25994253c1d320ca590a6e999/sce.rds" +#' input_file_rds <- "/Users/shen.m/projects/caq/import_api_pipelines/12eb5fe25994253c1d320ca590a6e999/se.rds" #' output_file <- "/Users/shen.m/projects/caq/cache_for_testing/cpm/12eb5fe25994253c1d320ca590a6e999/" #' get_counts_per_million(input_file_rds, output_file) #' @@ -21,6 +21,7 @@ get_counts_per_million <- function(input_file_rds, output_file) { # Create directory output_file |> dirname() |> dir.create( showWarnings = FALSE, recursive = TRUE) + output_file |> dir.create( showWarnings = FALSE, recursive = TRUE) # Read file_cell_types data = readRDS(input_file_rds) @@ -55,6 +56,6 @@ get_counts_per_million <- function(input_file_rds, output_file) { assays(sce) <- assays(sce) |> purrr::map(realize) sce |> saveRDS(file.path(output_file,"se.rds")) - sce |> saveHDF5SummarizedExperiment(output_file) + sce |> saveHDF5SummarizedExperiment(output_file, replace = TRUE) } diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 70782e2..6f7e44b 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -12,7 +12,7 @@ #' example_metadata <- get_metadata() |> head(3) |> as_tibble() #' import_metadata_counts(metadata_tbl = example_metadata, #' cache_dir = get_default_cache_dir(), -#' counts_path = "/Users/shen.m/projects/caq/import_api_pipelines/original") +#' counts_path = "/Users/shen.m/projects/caq/import_api_pipelines") #' #' @importFrom assertthat assert_that #' @importFrom checkmate check_tibble check_directory_exists check_set_equal check_true check_character check_subset check_file_exists @@ -28,14 +28,47 @@ import_metadata_counts <- function(metadata_tbl, metadata_tbl <- metadata_tbl |> as_tibble() check_directory_exists(cache_dir) check_directory_exists(counts_path) - check_directory_exists(file.path(cache_dir, "original")) + + # create original and cpm folders in cache directory if not exist (in order to append new counts to existing ones) + if (!dir.exists(file.path(cache_dir, "original"))) { + dir.create(cache_dir, "original", recursive = TRUE) + } + + if (!dir.exists(file.path(cache_dir, "cpm"))) { + dir.create(cache_dir, "cpm", recursive = TRUE) + } # check count H5 directory name not included in the cache directory original all(!dir(file.path(counts_path)) %in% dir(file.path(cache_dir, 'original'))) |> check_true() |> assert_that(msg = 'Count H5 directory name should not duplicate with that in cache directory') - dir_copy(counts_path, file.path(cache_dir)) + # if checkpoints above pass, generate cpm + cli_alert_info("Generating cpm from {.path {counts_path}/}. ") + + input_dir <- counts_path + #output_dir <- file.path(cache_dir, "cpm") + + for (subdir in list.files(input_dir, full.names = TRUE)) { + # Iterate get_counts_per_million function to generate cpm for each raw count + file_rds = dir(subdir, pattern = "*.rds", full.names = TRUE) + cpm_path = file.path(cache_dir, "cpm", basename(subdir)) + + get_counts_per_million( + input_file_rds = file_rds, + output_file = cpm_path + ) + + dir_copy(subdir, file.path(cache_dir, "original")) + data = readRDS(file_rds) + saveHDF5SummarizedExperiment(data, file.path(cache_dir,"original",basename(subdir)), replace=TRUE) + } + + + cli_alert_info("cpm are generated in {.path {cache_dir}/cpm}. ") + + #dir_copy(counts_path, file.path(cache_dir, "original")) + sce <- metadata_tbl |> get_single_cell_experiment(cache_directory = cache_dir) @@ -81,22 +114,7 @@ import_metadata_counts <- function(metadata_tbl, # convert metadata_tbl to parquet if above checkpoints pass write_parquet(metadata_tbl, file.path(cache_dir, glue("metadata.parquet"))) - # if checkpoints above pass, generate cpm - cli_alert_info("Generating cpm from {.path {cache_dir}/original}. ") - - input_dir <- file.path(cache_dir, "original") - output_dir <- file.path(cache_dir, "cpm") - for (subdir in list.files(input_dir, full.names = TRUE)) { - file_name <- basename(subdir) - count_path <- glue("{input_dir}/{file_name}/") - cpm_path <- glue("{output_dir}/{file_name}/") - # Iterate get_counts_per_million function to generate cpm for each raw count - get_counts_per_million(input_file = count_path, - output_file = cpm_path) - } - - cli_alert_info("cpm are generated in {.path {cache_dir}/cpm}. ") } diff --git a/man/get_counts_per_million.Rd b/man/get_counts_per_million.Rd index 0bb4d20..bfd6865 100644 --- a/man/get_counts_per_million.Rd +++ b/man/get_counts_per_million.Rd @@ -2,12 +2,12 @@ % Please edit documentation in R/counts_per_million.R \name{get_counts_per_million} \alias{get_counts_per_million} -\title{Generating counts per million} +\title{Generating counts per million from SingleCellExperiment RDS} \usage{ -get_counts_per_million(input_file, output_file) +get_counts_per_million(input_file_rds, output_file) } \arguments{ -\item{input_file}{A character vector of raw counts path} +\item{input_file_rds}{A character vector of raw counts path} \item{output_file}{A character vector of counts per million path} } @@ -15,11 +15,11 @@ get_counts_per_million(input_file, output_file) A directory stores counts per million } \description{ -Generating counts per million +Generating counts per million from SingleCellExperiment RDS } \examples{ -input_file <- "/Users/shen.m/projects/caq/import_api_pipelines/original/12eb5fe25994253c1d320ca590a6e681" -output_file <- "/Users/shen.m/projects/caq/import_api_pipelines/cpm/12eb5fe25994253c1d320ca590a6e681" -get_counts_per_million(input_file, output_file) +input_file_rds <- "/Users/shen.m/projects/caq/import_api_pipelines/12eb5fe25994253c1d320ca590a6e999/se.rds" +output_file <- "/Users/shen.m/projects/caq/cache_for_testing/cpm/12eb5fe25994253c1d320ca590a6e999/" +get_counts_per_million(input_file_rds, output_file) } diff --git a/man/import_metadata_counts.Rd b/man/import_metadata_counts.Rd index 1614d51..b5d33a1 100644 --- a/man/import_metadata_counts.Rd +++ b/man/import_metadata_counts.Rd @@ -31,6 +31,6 @@ Generating counts per million from provided raw counts example_metadata <- get_metadata() |> head(3) |> as_tibble() import_metadata_counts(metadata_tbl = example_metadata, cache_dir = get_default_cache_dir(), - counts_path = "/Users/shen.m/projects/caq/import_api_pipelines/original") + counts_path = "/Users/shen.m/projects/caq/import_api_pipelines") } From 1c4aa795a01888f9661235479b7584579dd74230 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Wed, 13 Mar 2024 13:56:39 +1100 Subject: [PATCH 65/85] accept count_path_df.rds as an input --- R/import_metadata_and_counts.R | 44 +++++++++++++++++----------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 6f7e44b..60bd89e 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -5,14 +5,14 @@ #' @param cache_dir Optional character vector of length 1. A file path on #' your local system to a directory (not a file) that will be used to store #' `metadata.parquet` -#' @param counts_path A character vector of original counts path defined by users +#' @param counts_path_df A data.frame RDS of original counts defined by users #' @export #' @return A metadata.parquet with version, and a directory stores counts per million in cache directory #' @examples #' example_metadata <- get_metadata() |> head(3) |> as_tibble() #' import_metadata_counts(metadata_tbl = example_metadata, #' cache_dir = get_default_cache_dir(), -#' counts_path = "/Users/shen.m/projects/caq/import_api_pipelines") +#' counts_path_df = "/Users/shen.m/projects/caq/my_counts_df.rds") #' #' @importFrom assertthat assert_that #' @importFrom checkmate check_tibble check_directory_exists check_set_equal check_true check_character check_subset check_file_exists @@ -21,14 +21,20 @@ #' @importFrom glue glue #' @importFrom arrow write_parquet #' @importFrom fs dir_copy +#' @importFrom purrr walk2 import_metadata_counts <- function(metadata_tbl, cache_dir = CuratedAtlasQueryR:::get_default_cache_dir(), - counts_path) { + counts_path_df) { + # Load counts_path dataframe from RDS + counts_path <- readRDS(counts_path_df) + # Convert to tibble if metadata_tbl is not a tibble metadata_tbl <- metadata_tbl |> as_tibble() check_directory_exists(cache_dir) check_directory_exists(counts_path) + + # create original and cpm folders in cache directory if not exist (in order to append new counts to existing ones) if (!dir.exists(file.path(cache_dir, "original"))) { dir.create(cache_dir, "original", recursive = TRUE) @@ -44,32 +50,26 @@ import_metadata_counts <- function(metadata_tbl, assert_that(msg = 'Count H5 directory name should not duplicate with that in cache directory') # if checkpoints above pass, generate cpm - cli_alert_info("Generating cpm from {.path {counts_path}/}. ") + cli_alert_info("Generating cpm from {.path {counts_path$file_path}}. ") - input_dir <- counts_path - #output_dir <- file.path(cache_dir, "cpm") + counts_path <- + counts_path |> mutate( + original_path = file.path(original_dir, basename(file_id)), + cpm_path = file.path(cache_dir, "cpm", basename(file_id)) + ) - for (subdir in list.files(input_dir, full.names = TRUE)) { - # Iterate get_counts_per_million function to generate cpm for each raw count - file_rds = dir(subdir, pattern = "*.rds", full.names = TRUE) - cpm_path = file.path(cache_dir, "cpm", basename(subdir)) - + purrr::walk2(counts_path$file_path, counts_path$cpm_path, ~{ get_counts_per_million( - input_file_rds = file_rds, - output_file = cpm_path + input_file_rds = .x, + output_file = .y ) - - dir_copy(subdir, file.path(cache_dir, "original")) - data = readRDS(file_rds) - saveHDF5SummarizedExperiment(data, file.path(cache_dir,"original",basename(subdir)), replace=TRUE) - } - + dir_copy(.x |> dirname(), file.path(cache_dir, "original")) + data = readRDS(.x) + saveHDF5SummarizedExperiment(data, counts_path$original_path, replace=TRUE) + }) cli_alert_info("cpm are generated in {.path {cache_dir}/cpm}. ") - #dir_copy(counts_path, file.path(cache_dir, "original")) - - sce <- metadata_tbl |> get_single_cell_experiment(cache_directory = cache_dir) se <- metadata_tbl |> get_seurat(cache_directory = cache_dir) From f8bda0b2828fff996a5b86e494fecd47db6a193b Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Wed, 13 Mar 2024 13:57:22 +1100 Subject: [PATCH 66/85] roxygenise --- NAMESPACE | 1 + man/import_metadata_counts.Rd | 6 +++--- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index bedbbf1..43b8f6c 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -77,6 +77,7 @@ importFrom(purrr,pmap_chr) importFrom(purrr,reduce) importFrom(purrr,set_names) importFrom(purrr,walk) +importFrom(purrr,walk2) importFrom(rlang,.data) importFrom(scuttle,calculateCPM) importFrom(stats,setNames) diff --git a/man/import_metadata_counts.Rd b/man/import_metadata_counts.Rd index b5d33a1..6638c8f 100644 --- a/man/import_metadata_counts.Rd +++ b/man/import_metadata_counts.Rd @@ -8,7 +8,7 @@ Generating counts per million from provided raw counts} import_metadata_counts( metadata_tbl, cache_dir = CuratedAtlasQueryR:::get_default_cache_dir(), - counts_path + counts_path_df ) } \arguments{ @@ -18,7 +18,7 @@ import_metadata_counts( your local system to a directory (not a file) that will be used to store \code{metadata.parquet}} -\item{counts_path}{A character vector of original counts path defined by users} +\item{counts_path_df}{A data.frame RDS of original counts defined by users} } \value{ A metadata.parquet with version, and a directory stores counts per million in cache directory @@ -31,6 +31,6 @@ Generating counts per million from provided raw counts example_metadata <- get_metadata() |> head(3) |> as_tibble() import_metadata_counts(metadata_tbl = example_metadata, cache_dir = get_default_cache_dir(), - counts_path = "/Users/shen.m/projects/caq/import_api_pipelines") + counts_path_df = "/Users/shen.m/projects/caq/my_counts_df.rds") } From 8e34280d6503490733481b1d782c18cdcc8f98e5 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Wed, 13 Mar 2024 14:04:08 +1100 Subject: [PATCH 67/85] fix a missing variable --- R/import_metadata_and_counts.R | 2 ++ 1 file changed, 2 insertions(+) diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 60bd89e..9f9d381 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -52,6 +52,8 @@ import_metadata_counts <- function(metadata_tbl, # if checkpoints above pass, generate cpm cli_alert_info("Generating cpm from {.path {counts_path$file_path}}. ") + original_dir <- file.path(cache_dir, "original") + counts_path <- counts_path |> mutate( original_path = file.path(original_dir, basename(file_id)), From bdf294b4671514ba0ce4ef112c1af2b37bb6356f Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Wed, 13 Mar 2024 17:53:51 +1100 Subject: [PATCH 68/85] tiny fix --- R/counts_per_million.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/counts_per_million.R b/R/counts_per_million.R index 2487a08..c531de9 100644 --- a/R/counts_per_million.R +++ b/R/counts_per_million.R @@ -28,7 +28,7 @@ get_counts_per_million <- function(input_file_rds, output_file) { # Assign HDF5 matrix for counts data hdf5_file <- file.path(dirname(input_file_rds), "assays.h5") - hdf5_counts <- writeHDF5Array(assays(data)$counts, filepath = hdf5_file, name = "assays.h5") + hdf5_counts <- writeHDF5Array(assays(data)$X, filepath = hdf5_file, name = "assays.h5") assays(data, withDimnames = FALSE)[["X"]] <- hdf5_counts # Avoid completely empty cells From 65b89755cb9666f3a31511e08c73df32f2dfc161 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Wed, 13 Mar 2024 23:45:28 +1100 Subject: [PATCH 69/85] checking whether gene set of new count data and old count data match --- R/import_metadata_and_counts.R | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 9f9d381..6dde20d 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -60,6 +60,10 @@ import_metadata_counts <- function(metadata_tbl, cpm_path = file.path(cache_dir, "cpm", basename(file_id)) ) + # existing metadata genes + genes <- get_metadata() |> head(1) |> + get_single_cell_experiment(cache_directory = cache_dir) |> rownames() + purrr::walk2(counts_path$file_path, counts_path$cpm_path, ~{ get_counts_per_million( input_file_rds = .x, @@ -67,6 +71,12 @@ import_metadata_counts <- function(metadata_tbl, ) dir_copy(.x |> dirname(), file.path(cache_dir, "original")) data = readRDS(.x) + + # check whether new data has the same gene set as existing metadata + (rownames(data) |> length() == genes |> length()) |> + assert_that(msg = "CuratedAtlasQuery reports: + The number of genes in the existing metadata count does not match the number of genes specified") + saveHDF5SummarizedExperiment(data, counts_path$original_path, replace=TRUE) }) From 77d0b687a43a568cb5cc021b1e53ef6c182a248f Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Thu, 14 Mar 2024 17:16:35 +1100 Subject: [PATCH 70/85] change function input, accept sce rds and cache dir only --- DESCRIPTION | 7 +- NAMESPACE | 1 + R/import_metadata_and_counts.R | 137 +++++++++++++++------------------ man/import_metadata_counts.Rd | 22 ++---- 4 files changed, 76 insertions(+), 91 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 56ae731..a3a56d5 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -93,7 +93,12 @@ Imports: utils, dbplyr (>= 2.3.0), duckdb, - stringr + stringr, + DelayedArray, + arrow, + checkmate, + fs, + scuttle Suggests: zellkonverter, rmarkdown, diff --git a/NAMESPACE b/NAMESPACE index 43b8f6c..815bdbb 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -67,6 +67,7 @@ importFrom(httr,progress) importFrom(httr,stop_for_status) importFrom(httr,write_disk) importFrom(methods,as) +importFrom(openssl,md5) importFrom(purrr,imap) importFrom(purrr,keep) importFrom(purrr,map) diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 6dde20d..502281a 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -1,18 +1,15 @@ #' Checks importing criteria for new atlas -#' Generating counts per million from provided raw counts +#' Generats counts per million from provided raw counts #' -#' @param metadata_tbl A tibble +#' @param sce_rds A SingleCellExperiment object Rds #' @param cache_dir Optional character vector of length 1. A file path on #' your local system to a directory (not a file) that will be used to store #' `metadata.parquet` -#' @param counts_path_df A data.frame RDS of original counts defined by users #' @export -#' @return A metadata.parquet with version, and a directory stores counts per million in cache directory +#' @return A metadata.parquet, and a counts per million directory in provided cache directory #' @examples -#' example_metadata <- get_metadata() |> head(3) |> as_tibble() -#' import_metadata_counts(metadata_tbl = example_metadata, -#' cache_dir = get_default_cache_dir(), -#' counts_path_df = "/Users/shen.m/projects/caq/my_counts_df.rds") +#' import_metadata_counts(sce_rds = "~/projects/caq/import_api_pipelines/12eb5fe25994253c1d320ca590a6e999/se.rds", +#' cache_directory = "~/projects/caq/cache_for_testing") #' #' @importFrom assertthat assert_that #' @importFrom checkmate check_tibble check_directory_exists check_set_equal check_true check_character check_subset check_file_exists @@ -22,21 +19,26 @@ #' @importFrom arrow write_parquet #' @importFrom fs dir_copy #' @importFrom purrr walk2 -import_metadata_counts <- function(metadata_tbl, - cache_dir = CuratedAtlasQueryR:::get_default_cache_dir(), - counts_path_df) { - # Load counts_path dataframe from RDS - counts_path <- readRDS(counts_path_df) +#' @importFrom openssl md5 +import_metadata_counts <- function( + sce_rds = data, + cache_dir = get_default_cache_dir()) { - # Convert to tibble if metadata_tbl is not a tibble - metadata_tbl <- metadata_tbl |> as_tibble() - check_directory_exists(cache_dir) - check_directory_exists(counts_path) + original_dir <- file.path(cache_dir, "original") + # Load sce, identify metadata and counts matrix + sce_obj <- readRDS(sce_rds) + metadata_tbl <- metadata(sce_obj)$data + counts_matrix <- sce@assays@data$X + # Convert to tibble if metadata_tbl is not a tibble + metadata_tbl <- metadata_tbl |> as_tibble() + + # create file_id_db from dataset_id + metadata_tbl <- metadata_tbl |> mutate(file_id_db = dataset_id |> md5() |> as.character()) # create original and cpm folders in cache directory if not exist (in order to append new counts to existing ones) - if (!dir.exists(file.path(cache_dir, "original"))) { + if (!dir.exists(original_dir)) { dir.create(cache_dir, "original", recursive = TRUE) } @@ -44,69 +46,27 @@ import_metadata_counts <- function(metadata_tbl, dir.create(cache_dir, "cpm", recursive = TRUE) } - # check count H5 directory name not included in the cache directory original - all(!dir(file.path(counts_path)) %in% dir(file.path(cache_dir, 'original'))) |> - check_true() |> - assert_that(msg = 'Count H5 directory name should not duplicate with that in cache directory') - - # if checkpoints above pass, generate cpm - cli_alert_info("Generating cpm from {.path {counts_path$file_path}}. ") - - original_dir <- file.path(cache_dir, "original") - - counts_path <- - counts_path |> mutate( - original_path = file.path(original_dir, basename(file_id)), - cpm_path = file.path(cache_dir, "cpm", basename(file_id)) - ) - # existing metadata genes genes <- get_metadata() |> head(1) |> get_single_cell_experiment(cache_directory = cache_dir) |> rownames() - purrr::walk2(counts_path$file_path, counts_path$cpm_path, ~{ - get_counts_per_million( - input_file_rds = .x, - output_file = .y - ) - dir_copy(.x |> dirname(), file.path(cache_dir, "original")) - data = readRDS(.x) - - # check whether new data has the same gene set as existing metadata - (rownames(data) |> length() == genes |> length()) |> - assert_that(msg = "CuratedAtlasQuery reports: - The number of genes in the existing metadata count does not match the number of genes specified") - - saveHDF5SummarizedExperiment(data, counts_path$original_path, replace=TRUE) - }) - - cli_alert_info("cpm are generated in {.path {cache_dir}/cpm}. ") - - sce <- metadata_tbl |> - get_single_cell_experiment(cache_directory = cache_dir) - se <- metadata_tbl |> get_seurat(cache_directory = cache_dir) - - counts_data <- assays(sce)$counts |> - as_tibble() - assert_that(inherits(sce, "SingleCellExperiment"), + # check count H5 directory name not included in the cache directory original + all(!metadata_tbl$file_id_db %in% dir(original_dir)) |> + check_true() |> + assert_that(msg = "Count H5 directory name should not duplicate with that in cache directory") + + counts_data <- sce_obj@assays@data$X + assert_that(inherits(sce_obj, "SingleCellExperiment"), msg = "SingleCellExperiment Object is not created from metadata.") - assert_that(!'^ENS' %in% rownames(se[['originalexp']]), + assert_that(!"^ENS" %in% genes, msg = "Gene names cannot contain Ensembl IDs.") assert_that(all(counts_data >= 0), msg = "Counts for SingleCellExperiment cannot be negative.") - # check metadata sample file ID match the count file ID - all(metadata_tbl |> pull(file_id_db) %in% dir(file.path(cache_dir, "original"))) |> - assert_that(msg = "The metadata sample file ID and the count file ID does not match") - - # check the number of sub directories in original match cpm - check_set_equal(length(list.dirs(file.path(cache_dir, "original"))), length(list.dirs(file.path(cache_dir, "cpm")))) - # check the metadata contains cell_, file_id_db, sample_ with correct types check_true("cell_" %in% names(metadata_tbl)) check_true("file_id_db" %in% names(metadata_tbl)) - check_true("sample_" %in% names(metadata_tbl)) - metadata_tbl |> select(cell_, file_id_db, sample_) |> sapply(class) |> check_character() + metadata_tbl |> select(cell_, file_id_db) |> sapply(class) |> check_character() # check cell_ values in metadata_tbl is unique (anyDuplicated(metadata_tbl$cell_) == 0 ) |> assert_that(msg = "cell_ in the metadata must be unique") @@ -116,18 +76,45 @@ import_metadata_counts <- function(metadata_tbl, (!any(metadata_tbl$cell_ %in% cells$cell_)) |> assert_that(msg = "cell_ in the metadata should not duplicate with that exists in API") # check age_days is either -99 or greater than 365 - assert_that(any(metadata_tbl$age_days==-99 | metadata_tbl$age_days> 365), - msg = "age_days should be either -99 for unknown or greater than 365") + if (any(colnames(metadata_tbl) == "age_days")) { + assert_that(all(metadata_tbl$age_days==-99 | metadata_tbl$age_days> 365), + msg = "age_days should be either -99 for unknown or greater than 365") + } # check sex capitalisation then convert to lower case - metadata_tbl <- metadata_tbl |> mutate(sex = tolower(sex)) - metadata_tbl |> distinct(sex) |> pull() |> check_subset(c('female','male','unknown')) + if (any(colnames(metadata_tbl) == "sex")) { + metadata_tbl <- metadata_tbl |> mutate(sex = tolower(sex)) + metadata_tbl |> distinct(sex) |> pull() |> check_subset(c("female","male","unknown")) + } - # convert metadata_tbl to parquet if above checkpoints pass - write_parquet(metadata_tbl, file.path(cache_dir, glue("metadata.parquet"))) + counts_path <- + metadata_tbl |> select(file_id_db) |> mutate( + file_path = sce_rds, + original_path = file.path(original_dir, basename(file_id_db)), + cpm_path = file.path(cache_dir, "cpm", basename(file_id_db)) + ) + # if checkpoints above pass, generate cpm + cli_alert_info("Generating cpm from {.path {metadata_tbl$file_id_db}}. ") + + # Generate cpm from counts + get_counts_per_million(input_file_rds = sce_rds, output_file = counts_path$cpm_path) + dir_copy(sce_rds |> dirname(), counts_path$original_path) + rds_data = readRDS(sce_rds) + # check whether new data has the same gene set as existing metadata + (rownames(rds_data) |> length() == genes |> length()) |> + assert_that(msg = "CuratedAtlasQuery reports: + The number of genes in the existing metadata count does not match the number of genes specified") + saveHDF5SummarizedExperiment(rds_data, counts_path$original_path, replace=TRUE) + cli_alert_info("cpm are generated in {.path {counts_path$cpm_path}}. ") + # check metadata sample file ID match the count file ID in cache directory + all(metadata_tbl |> pull(file_id_db) %in% dir(original_dir)) |> + assert_that(msg = "The metadata sample file ID and the count file ID does not match") + + # convert metadata_tbl to parquet if above checkpoints pass + write_parquet(metadata_tbl, file.path(cache_dir, glue("metadata.parquet"))) } diff --git a/man/import_metadata_counts.Rd b/man/import_metadata_counts.Rd index 6638c8f..6e5602f 100644 --- a/man/import_metadata_counts.Rd +++ b/man/import_metadata_counts.Rd @@ -3,34 +3,26 @@ \name{import_metadata_counts} \alias{import_metadata_counts} \title{Checks importing criteria for new atlas -Generating counts per million from provided raw counts} +Generats counts per million from provided raw counts} \usage{ -import_metadata_counts( - metadata_tbl, - cache_dir = CuratedAtlasQueryR:::get_default_cache_dir(), - counts_path_df -) +import_metadata_counts(sce_rds = data, cache_dir = get_default_cache_dir()) } \arguments{ -\item{metadata_tbl}{A tibble} +\item{sce_rds}{A SingleCellExperiment object Rds} \item{cache_dir}{Optional character vector of length 1. A file path on your local system to a directory (not a file) that will be used to store \code{metadata.parquet}} - -\item{counts_path_df}{A data.frame RDS of original counts defined by users} } \value{ -A metadata.parquet with version, and a directory stores counts per million in cache directory +A metadata.parquet, and a counts per million directory in provided cache directory } \description{ Checks importing criteria for new atlas -Generating counts per million from provided raw counts +Generats counts per million from provided raw counts } \examples{ -example_metadata <- get_metadata() |> head(3) |> as_tibble() -import_metadata_counts(metadata_tbl = example_metadata, - cache_dir = get_default_cache_dir(), - counts_path_df = "/Users/shen.m/projects/caq/my_counts_df.rds") +import_metadata_counts(sce_rds = "~/projects/caq/import_api_pipelines/12eb5fe25994253c1d320ca590a6e999/se.rds", + cache_directory = "~/projects/caq/cache_for_testing") } From 8101da2daec84a5b2c0fb1869d31be629474c705 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Thu, 14 Mar 2024 17:32:21 +1100 Subject: [PATCH 71/85] fix a variable --- R/import_metadata_and_counts.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 502281a..84b3143 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -29,7 +29,7 @@ import_metadata_counts <- function( # Load sce, identify metadata and counts matrix sce_obj <- readRDS(sce_rds) metadata_tbl <- metadata(sce_obj)$data - counts_matrix <- sce@assays@data$X + counts_matrix <- sce_obj@assays@data$X # Convert to tibble if metadata_tbl is not a tibble metadata_tbl <- metadata_tbl |> as_tibble() From 2ab8a455cbde61af20141d9d39b8abe947e22d7f Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Thu, 14 Mar 2024 23:23:10 +1100 Subject: [PATCH 72/85] save created file_id_db column in metadata slot to rds --- R/import_metadata_and_counts.R | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 84b3143..69b69d0 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -37,6 +37,14 @@ import_metadata_counts <- function( # create file_id_db from dataset_id metadata_tbl <- metadata_tbl |> mutate(file_id_db = dataset_id |> md5() |> as.character()) + metadata(sce_obj)$data <- metadata_tbl + + # save updated sce_obj with file_id_db in metadata + saveRDS(sce_obj, sce_rds) + + # load back + sce_obj <- readRDS(sce_rds) + # create original and cpm folders in cache directory if not exist (in order to append new counts to existing ones) if (!dir.exists(original_dir)) { dir.create(cache_dir, "original", recursive = TRUE) @@ -100,7 +108,7 @@ import_metadata_counts <- function( # Generate cpm from counts get_counts_per_million(input_file_rds = sce_rds, output_file = counts_path$cpm_path) dir_copy(sce_rds |> dirname(), counts_path$original_path) - rds_data = readRDS(sce_rds) + rds_data = readRDS(sce_obj) # check whether new data has the same gene set as existing metadata (rownames(rds_data) |> length() == genes |> length()) |> assert_that(msg = "CuratedAtlasQuery reports: From 97cbfa5fbf67df3ffce25dc1f6c89f63cc091f9f Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Fri, 15 Mar 2024 00:00:50 +1100 Subject: [PATCH 73/85] fix variable --- R/import_metadata_and_counts.R | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 69b69d0..2e922af 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -44,6 +44,7 @@ import_metadata_counts <- function( # load back sce_obj <- readRDS(sce_rds) + metadata_tbl <- metadata(sce_obj)$data # create original and cpm folders in cache directory if not exist (in order to append new counts to existing ones) if (!dir.exists(original_dir)) { @@ -108,12 +109,12 @@ import_metadata_counts <- function( # Generate cpm from counts get_counts_per_million(input_file_rds = sce_rds, output_file = counts_path$cpm_path) dir_copy(sce_rds |> dirname(), counts_path$original_path) - rds_data = readRDS(sce_obj) + # check whether new data has the same gene set as existing metadata - (rownames(rds_data) |> length() == genes |> length()) |> + (rownames(sce_obj) |> length() == genes |> length()) |> assert_that(msg = "CuratedAtlasQuery reports: The number of genes in the existing metadata count does not match the number of genes specified") - saveHDF5SummarizedExperiment(rds_data, counts_path$original_path, replace=TRUE) + saveHDF5SummarizedExperiment(sce_obj, counts_path$original_path, replace=TRUE) cli_alert_info("cpm are generated in {.path {counts_path$cpm_path}}. ") From 731e877023da4480e9f181aafcdfa1ed4bcaf6e1 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Fri, 15 Mar 2024 12:34:07 +1100 Subject: [PATCH 74/85] accept a loaded sce_obj as input --- NAMESPACE | 2 -- R/counts_per_million.R | 34 +++++++++++++++++++--------------- R/import_metadata_and_counts.R | 22 +++++----------------- man/get_counts_per_million.Rd | 19 +++++++++++-------- man/import_metadata_counts.Rd | 6 +++--- 5 files changed, 38 insertions(+), 45 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 815bdbb..3c2bd72 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -56,7 +56,6 @@ importFrom(dplyr,summarise) importFrom(dplyr,tbl) importFrom(dplyr,transmute) importFrom(duckdb,duckdb) -importFrom(fs,dir_copy) importFrom(glue,glue) importFrom(glue,glue_sql) importFrom(httr,GET) @@ -78,7 +77,6 @@ importFrom(purrr,pmap_chr) importFrom(purrr,reduce) importFrom(purrr,set_names) importFrom(purrr,walk) -importFrom(purrr,walk2) importFrom(rlang,.data) importFrom(scuttle,calculateCPM) importFrom(stats,setNames) diff --git a/R/counts_per_million.R b/R/counts_per_million.R index c531de9..4e75e4e 100644 --- a/R/counts_per_million.R +++ b/R/counts_per_million.R @@ -1,13 +1,15 @@ -#' Generating counts per million from SingleCellExperiment RDS +#' Generating counts per million from SingleCellExperiment object #' -#' @param input_file_rds A character vector of raw counts path -#' @param output_file A character vector of counts per million path +#' @param input_sce_obj A SingleCellExperiment object read from RDS +#' @param output_dir A character vector of counts per million path +#' @param hd5_file_dir A character vector of HDF5 file path after converting from RDS #' @export #' @return A directory stores counts per million #' @examples -#' input_file_rds <- "/Users/shen.m/projects/caq/import_api_pipelines/12eb5fe25994253c1d320ca590a6e999/se.rds" -#' output_file <- "/Users/shen.m/projects/caq/cache_for_testing/cpm/12eb5fe25994253c1d320ca590a6e999/" -#' get_counts_per_million(input_file_rds, output_file) +#' input_sce_obj <- sample_sce_obj +#' output_dir <- "~/projects/caq/cache_for_testing/cpm/8df700ab083ab215e618fe732e2f3dfe" +#' hd5_file_dir <- "~/projects/caq/cache_for_testing/original/8df700ab083ab215e618fe732e2f3dfe" +#' get_counts_per_million(input_sce_obj, output_dir, hd5_file_dir) #' #' @importFrom SingleCellExperiment SingleCellExperiment #' @importFrom dplyr tbl @@ -17,17 +19,19 @@ #' @importFrom DelayedArray realize #' @importFrom purrr map -get_counts_per_million <- function(input_file_rds, output_file) { +get_counts_per_million <- function(input_sce_obj, output_dir, hd5_file_dir) { - # Create directory - output_file |> dirname() |> dir.create( showWarnings = FALSE, recursive = TRUE) - output_file |> dir.create( showWarnings = FALSE, recursive = TRUE) + # Create directories + output_dir |> dirname() |> dir.create( showWarnings = FALSE, recursive = TRUE) + output_dir |> dir.create( showWarnings = FALSE, recursive = TRUE) + hd5_file_dir |> dir.create(showWarnings = FALSE, recursive = TRUE) - # Read file_cell_types - data = readRDS(input_file_rds) + saveRDS(input_sce_obj, file.path(hd5_file_dir, "se.rds")) + + data = input_sce_obj # Assign HDF5 matrix for counts data - hdf5_file <- file.path(dirname(input_file_rds), "assays.h5") + hdf5_file <- file.path(hd5_file_dir, "assays.h5") hdf5_counts <- writeHDF5Array(assays(data)$X, filepath = hdf5_file, name = "assays.h5") assays(data, withDimnames = FALSE)[["X"]] <- hdf5_counts @@ -55,7 +59,7 @@ get_counts_per_million <- function(input_file_rds, output_file) { # Check if there is a memory issue assays(sce) <- assays(sce) |> purrr::map(realize) - sce |> saveRDS(file.path(output_file,"se.rds")) - sce |> saveHDF5SummarizedExperiment(output_file, replace = TRUE) + sce |> saveRDS(file.path(output_dir,"se.rds")) + sce |> saveHDF5SummarizedExperiment(output_dir, replace = TRUE) } diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 2e922af..ec4f5b0 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -1,14 +1,14 @@ #' Checks importing criteria for new atlas #' Generats counts per million from provided raw counts #' -#' @param sce_rds A SingleCellExperiment object Rds +#' @param sce_obj A SingleCellExperiment object read from RDS #' @param cache_dir Optional character vector of length 1. A file path on #' your local system to a directory (not a file) that will be used to store #' `metadata.parquet` #' @export #' @return A metadata.parquet, and a counts per million directory in provided cache directory #' @examples -#' import_metadata_counts(sce_rds = "~/projects/caq/import_api_pipelines/12eb5fe25994253c1d320ca590a6e999/se.rds", +#' import_metadata_counts(sce_obj = sample_sce_obj, #' cache_directory = "~/projects/caq/cache_for_testing") #' #' @importFrom assertthat assert_that @@ -17,17 +17,14 @@ #' @importFrom cli cli_alert_info #' @importFrom glue glue #' @importFrom arrow write_parquet -#' @importFrom fs dir_copy -#' @importFrom purrr walk2 #' @importFrom openssl md5 import_metadata_counts <- function( - sce_rds = data, + sce_obj = data, cache_dir = get_default_cache_dir()) { original_dir <- file.path(cache_dir, "original") - # Load sce, identify metadata and counts matrix - sce_obj <- readRDS(sce_rds) + # Identify metadata and counts matrix metadata_tbl <- metadata(sce_obj)$data counts_matrix <- sce_obj@assays@data$X @@ -39,13 +36,6 @@ import_metadata_counts <- function( metadata(sce_obj)$data <- metadata_tbl - # save updated sce_obj with file_id_db in metadata - saveRDS(sce_obj, sce_rds) - - # load back - sce_obj <- readRDS(sce_rds) - metadata_tbl <- metadata(sce_obj)$data - # create original and cpm folders in cache directory if not exist (in order to append new counts to existing ones) if (!dir.exists(original_dir)) { dir.create(cache_dir, "original", recursive = TRUE) @@ -98,7 +88,6 @@ import_metadata_counts <- function( counts_path <- metadata_tbl |> select(file_id_db) |> mutate( - file_path = sce_rds, original_path = file.path(original_dir, basename(file_id_db)), cpm_path = file.path(cache_dir, "cpm", basename(file_id_db)) ) @@ -107,8 +96,7 @@ import_metadata_counts <- function( cli_alert_info("Generating cpm from {.path {metadata_tbl$file_id_db}}. ") # Generate cpm from counts - get_counts_per_million(input_file_rds = sce_rds, output_file = counts_path$cpm_path) - dir_copy(sce_rds |> dirname(), counts_path$original_path) + get_counts_per_million(input_sce_obj = sce_obj, output_dir = counts_path$cpm_path, hd5_file_dir = counts_path$original_path) # check whether new data has the same gene set as existing metadata (rownames(sce_obj) |> length() == genes |> length()) |> diff --git a/man/get_counts_per_million.Rd b/man/get_counts_per_million.Rd index bfd6865..9603664 100644 --- a/man/get_counts_per_million.Rd +++ b/man/get_counts_per_million.Rd @@ -2,24 +2,27 @@ % Please edit documentation in R/counts_per_million.R \name{get_counts_per_million} \alias{get_counts_per_million} -\title{Generating counts per million from SingleCellExperiment RDS} +\title{Generating counts per million from SingleCellExperiment object} \usage{ -get_counts_per_million(input_file_rds, output_file) +get_counts_per_million(input_sce_obj, output_dir, hd5_file_dir) } \arguments{ -\item{input_file_rds}{A character vector of raw counts path} +\item{input_sce_obj}{A SingleCellExperiment object read from RDS} -\item{output_file}{A character vector of counts per million path} +\item{output_dir}{A character vector of counts per million path} + +\item{hd5_file_dir}{A character vector of HDF5 file path after converting from RDS} } \value{ A directory stores counts per million } \description{ -Generating counts per million from SingleCellExperiment RDS +Generating counts per million from SingleCellExperiment object } \examples{ -input_file_rds <- "/Users/shen.m/projects/caq/import_api_pipelines/12eb5fe25994253c1d320ca590a6e999/se.rds" -output_file <- "/Users/shen.m/projects/caq/cache_for_testing/cpm/12eb5fe25994253c1d320ca590a6e999/" -get_counts_per_million(input_file_rds, output_file) +input_sce_obj <- sample_sce_obj +output_dir <- "~/projects/caq/cache_for_testing/cpm/8df700ab083ab215e618fe732e2f3dfe" +hd5_file_dir <- "~/projects/caq/cache_for_testing/original/8df700ab083ab215e618fe732e2f3dfe" +get_counts_per_million(input_sce_obj, output_dir, hd5_file_dir) } diff --git a/man/import_metadata_counts.Rd b/man/import_metadata_counts.Rd index 6e5602f..8312c9f 100644 --- a/man/import_metadata_counts.Rd +++ b/man/import_metadata_counts.Rd @@ -5,10 +5,10 @@ \title{Checks importing criteria for new atlas Generats counts per million from provided raw counts} \usage{ -import_metadata_counts(sce_rds = data, cache_dir = get_default_cache_dir()) +import_metadata_counts(sce_obj = data, cache_dir = get_default_cache_dir()) } \arguments{ -\item{sce_rds}{A SingleCellExperiment object Rds} +\item{sce_obj}{A SingleCellExperiment object read from RDS} \item{cache_dir}{Optional character vector of length 1. A file path on your local system to a directory (not a file) that will be used to store @@ -22,7 +22,7 @@ Checks importing criteria for new atlas Generats counts per million from provided raw counts } \examples{ -import_metadata_counts(sce_rds = "~/projects/caq/import_api_pipelines/12eb5fe25994253c1d320ca590a6e999/se.rds", +import_metadata_counts(sce_obj = sample_sce_obj, cache_directory = "~/projects/caq/cache_for_testing") } From 90fef3ac2493596351538ff13795179fb156e43a Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Mon, 18 Mar 2024 14:12:08 +1100 Subject: [PATCH 75/85] checking whether genes in a list of SCEs overlap --- R/gene-intersections.R | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) create mode 100644 R/gene-intersections.R diff --git a/R/gene-intersections.R b/R/gene-intersections.R new file mode 100644 index 0000000..7559994 --- /dev/null +++ b/R/gene-intersections.R @@ -0,0 +1,28 @@ +#' Checks whether genes in a list of SCE objects overlap +#' +#' @param sce_list A list of SingleCellExperiment object +#' @return A character vector of genes intersection across SCE objects +#' @examples +#' sce1 <-readRDS("~/projects/caq/import_api_pipelines/12eb5fe25994253c1d320ca590a6e999/se.rds") +#' sce2 <- HeOrganAtlasData(tissue = "Liver", ensembl = FALSE, location = TRUE) +#' sce_list <- list(sce1, sce2) +#' sce_list |> check_gene_overlap() +#' +#' @importFrom purrr map reduce map_int +#' @importFrom cli cli_alert_warning + +check_gene_overlap <- function(sce_list) { + # Extract gene names from each SCE object in the list, find intersection, + # calculate the number of unique genes in each SCE object + gene_lists <- map(sce_list, rownames) + common_genes <- reduce(gene_lists, intersect) + unique_genes_count <- map_int(gene_lists, ~length(setdiff(.x, common_genes))) + + if(any(unique_genes_count > 0)) { + single_line_str( + "CuratedAtlasQuery reports: Not all genes completely overlap across the provided SCE objects" + ) |> cli_alert_warning() + } + + common_genes +} From 5a6dad31531b1e0c50266098610486e4166c44de Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Tue, 19 Mar 2024 09:58:34 +1100 Subject: [PATCH 76/85] use gene intersection if genes across SCEs do not completely overlap --- R/counts.R | 175 +++++++++++++++++++++++++++-------------------------- 1 file changed, 90 insertions(+), 85 deletions(-) diff --git a/R/counts.R b/R/counts.R index 6a2e3d0..6f03af3 100644 --- a/R/counts.R +++ b/R/counts.R @@ -85,94 +85,99 @@ get_single_cell_experiment <- function( repository = COUNTS_URL, features = NULL ) { - # Parameter validation - - assays %in% names(assay_map) |> - all() |> - assert_that( - msg = 'assays must be a character vector containing "counts" and/or - "cpm"' - ) + # Parameter validation + assays %in% names(assay_map) |> + all() |> assert_that( - !anyDuplicated(assays), - inherits(cache_directory, "character"), - is.null(repository) || is.character(repository), - is.null(features) || is.character(features) - ) - - # Data parameter validation (last, because it's slower) - ## Evaluate the promise now so that we get a sensible error message - force(data) - ## We have to convert to an in-memory table here, or some of the dplyr - ## operations will fail when passed a database connection - cli_alert_info("Realising metadata.") - raw_data <- collect(data) - assert_that( - inherits(raw_data, "tbl"), - has_name(raw_data, c("cell_", "file_id_db")) - ) - - versioned_cache_directory <- cache_directory - versioned_cache_directory |> dir.create( - showWarnings = FALSE, - recursive = TRUE + msg = 'assays must be a character vector containing "counts" and/or + "cpm"' ) - - subdirs <- assay_map[assays] - - # The repository is optional. If not provided we load only from the cache - if (!is.null(repository)) { - cli_alert_info("Synchronising files") - parsed_repo <- parse_url(repository) - parsed_repo$scheme |> - `%in%`(c("http", "https")) |> - assert_that() - - files_to_read <- - raw_data |> - pull(.data$file_id_db) |> - unique() |> - as.character() |> - sync_assay_files( - url = parsed_repo, - cache_dir = versioned_cache_directory, - files = _, - subdirs = subdirs - ) - } - - cli_alert_info("Reading files.") - sces <- subdirs |> - imap(function(current_subdir, current_assay) { - # Build up an SCE for each assay - dir_prefix <- file.path( - versioned_cache_directory, - current_subdir - ) - - raw_data |> - dplyr::group_by(.data$file_id_db) |> - # Load each file and attach metadata - dplyr::summarise(sces = list(group_to_sce( - dplyr::cur_group_id(), - dplyr::cur_data_all(), - dir_prefix, - features - ))) |> - dplyr::pull(sces) |> - # Combine each sce by column, since each sce has a different set - # of cells - do.call(cbind, args = _) - }) - - cli_alert_info("Compiling Single Cell Experiment.") - # Combine all the assays - sce <- sces[[1]] - SummarizedExperiment::assays(sce) <- map(sces, function(sce) { - SummarizedExperiment::assays(sce)[[1]] + assert_that( + !anyDuplicated(assays), + inherits(cache_directory, "character"), + is.null(repository) || is.character(repository), + is.null(features) || is.character(features) + ) + + # Data parameter validation (last, because it's slower) + ## Evaluate the promise now so that we get a sensible error message + force(data) + ## We have to convert to an in-memory table here, or some of the dplyr + ## operations will fail when passed a database connection + cli_alert_info("Realising metadata.") + raw_data <- collect(data) + assert_that( + inherits(raw_data, "tbl"), + has_name(raw_data, c("cell_", "file_id_db")) + ) + + versioned_cache_directory <- cache_directory + versioned_cache_directory |> dir.create( + showWarnings = FALSE, + recursive = TRUE + ) + + subdirs <- assay_map[assays] + + # The repository is optional. If not provided we load only from the cache + if (!is.null(repository)) { + cli_alert_info("Synchronising files") + parsed_repo <- parse_url(repository) + parsed_repo$scheme |> + `%in%`(c("http", "https")) |> + assert_that() + + files_to_read <- + raw_data |> + pull(.data$file_id_db) |> + unique() |> + as.character() |> + sync_assay_files( + url = parsed_repo, + cache_dir = versioned_cache_directory, + files = _, + subdirs = subdirs + ) + } + + cli_alert_info("Reading files.") + sces <- subdirs |> + imap(function(current_subdir, current_assay) { + # Build up an SCE for each assay + dir_prefix <- file.path( + versioned_cache_directory, + current_subdir + ) + + sce_list <- raw_data |> + dplyr::group_by(.data$file_id_db) |> + # Load each file and attach metadata + dplyr::summarise(sces = list( + group_to_sce( + dplyr::cur_group_id(), + dplyr::cur_data_all(), + dir_prefix, + features + ) + )) |> + dplyr::pull(sces) + + # Check whether genes in a list of SCEs overlap, use gene intersection if overlap + commonGenes <- sce_list |> check_gene_overlap() + sce_list <- map(sce_list, function(sce) { + sce[commonGenes,] + }) |> + do.call(cbind, args = _) }) - - sce + + cli_alert_info("Compiling Single Cell Experiment.") + # Combine all the assays + sce <- sces[[1]] + SummarizedExperiment::assays(sce) <- map(sces, function(sce) { + SummarizedExperiment::assays(sce)[[1]] + }) + + sce } #' Converts a data frame into a single SCE From 15628a4f97132efe848de07590dbf827581a65ea Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Tue, 19 Mar 2024 11:09:59 +1100 Subject: [PATCH 77/85] move check_gene_overlap function to counts.R --- R/counts.R | 21 +++++++++++++++++++++ R/gene-intersections.R | 28 ---------------------------- 2 files changed, 21 insertions(+), 28 deletions(-) delete mode 100644 R/gene-intersections.R diff --git a/R/counts.R b/R/counts.R index 6f03af3..bb61e06 100644 --- a/R/counts.R +++ b/R/counts.R @@ -326,3 +326,24 @@ sync_assay_files <- function( output_file }, .progress = list(name = "Downloading files")) } + +#' Checks whether genes in a list of SCE objects overlap +#' @param sce_list A list of SCE objects +#' @return A character vector of genes intersection across SCE objects +#' @importFrom purrr map reduce map_int +#' @importFrom cli cli_alert_warning +#' @noRd +check_gene_overlap <- function(sce_list) { + gene_lists <- map(sce_list, rownames) + common_genes <- reduce(gene_lists, intersect) + unique_genes_count <- + map_int(gene_lists, ~ length(setdiff(.x, common_genes))) + + if (any(unique_genes_count > 0)) { + single_line_str( + "CuratedAtlasQuery reports: Not all genes completely overlap across the provided SCE objects" + ) |> cli_alert_warning() + } + + common_genes +} diff --git a/R/gene-intersections.R b/R/gene-intersections.R deleted file mode 100644 index 7559994..0000000 --- a/R/gene-intersections.R +++ /dev/null @@ -1,28 +0,0 @@ -#' Checks whether genes in a list of SCE objects overlap -#' -#' @param sce_list A list of SingleCellExperiment object -#' @return A character vector of genes intersection across SCE objects -#' @examples -#' sce1 <-readRDS("~/projects/caq/import_api_pipelines/12eb5fe25994253c1d320ca590a6e999/se.rds") -#' sce2 <- HeOrganAtlasData(tissue = "Liver", ensembl = FALSE, location = TRUE) -#' sce_list <- list(sce1, sce2) -#' sce_list |> check_gene_overlap() -#' -#' @importFrom purrr map reduce map_int -#' @importFrom cli cli_alert_warning - -check_gene_overlap <- function(sce_list) { - # Extract gene names from each SCE object in the list, find intersection, - # calculate the number of unique genes in each SCE object - gene_lists <- map(sce_list, rownames) - common_genes <- reduce(gene_lists, intersect) - unique_genes_count <- map_int(gene_lists, ~length(setdiff(.x, common_genes))) - - if(any(unique_genes_count > 0)) { - single_line_str( - "CuratedAtlasQuery reports: Not all genes completely overlap across the provided SCE objects" - ) |> cli_alert_warning() - } - - common_genes -} From 8e2c89c65e717aad4caa845b9e08352eb2888793 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Tue, 19 Mar 2024 12:15:06 +1100 Subject: [PATCH 78/85] optimise counts_per_million.R. interact import API with get_SCE --- R/counts_per_million.R | 17 +++++------------ R/import_metadata_and_counts.R | 15 ++++++--------- 2 files changed, 11 insertions(+), 21 deletions(-) diff --git a/R/counts_per_million.R b/R/counts_per_million.R index 4e75e4e..41d641b 100644 --- a/R/counts_per_million.R +++ b/R/counts_per_million.R @@ -23,17 +23,11 @@ get_counts_per_million <- function(input_sce_obj, output_dir, hd5_file_dir) { # Create directories output_dir |> dirname() |> dir.create( showWarnings = FALSE, recursive = TRUE) - output_dir |> dir.create( showWarnings = FALSE, recursive = TRUE) - hd5_file_dir |> dir.create(showWarnings = FALSE, recursive = TRUE) - - saveRDS(input_sce_obj, file.path(hd5_file_dir, "se.rds")) - - data = input_sce_obj + + # Save SCE to the cache directory counts folder + input_sce_obj |> saveHDF5SummarizedExperiment(hd5_file_dir) - # Assign HDF5 matrix for counts data - hdf5_file <- file.path(hd5_file_dir, "assays.h5") - hdf5_counts <- writeHDF5Array(assays(data)$X, filepath = hdf5_file, name = "assays.h5") - assays(data, withDimnames = FALSE)[["X"]] <- hdf5_counts + data <- input_sce_obj # Avoid completely empty cells col_sums = colSums(as.matrix(data@assays@data$X)) @@ -59,7 +53,6 @@ get_counts_per_million <- function(input_sce_obj, output_dir, hd5_file_dir) { # Check if there is a memory issue assays(sce) <- assays(sce) |> purrr::map(realize) - sce |> saveRDS(file.path(output_dir,"se.rds")) - sce |> saveHDF5SummarizedExperiment(output_dir, replace = TRUE) + sce |> saveHDF5SummarizedExperiment(output_dir) } diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index ec4f5b0..a77af51 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -21,7 +21,6 @@ import_metadata_counts <- function( sce_obj = data, cache_dir = get_default_cache_dir()) { - original_dir <- file.path(cache_dir, "original") # Identify metadata and counts matrix @@ -31,11 +30,15 @@ import_metadata_counts <- function( # Convert to tibble if metadata_tbl is not a tibble metadata_tbl <- metadata_tbl |> as_tibble() - # create file_id_db from dataset_id + # Create file_id_db from dataset_id metadata_tbl <- metadata_tbl |> mutate(file_id_db = dataset_id |> md5() |> as.character()) - metadata(sce_obj)$data <- metadata_tbl + # Remove existing reducedDim slot to enable get_SCE API functionality + if (assert_that(length(names(reducedDims(sce_obj))) >0 )) { + reducedDims(sce_obj) <- NULL + } + # create original and cpm folders in cache directory if not exist (in order to append new counts to existing ones) if (!dir.exists(original_dir)) { dir.create(cache_dir, "original", recursive = TRUE) @@ -85,7 +88,6 @@ import_metadata_counts <- function( metadata_tbl <- metadata_tbl |> mutate(sex = tolower(sex)) metadata_tbl |> distinct(sex) |> pull() |> check_subset(c("female","male","unknown")) } - counts_path <- metadata_tbl |> select(file_id_db) |> mutate( original_path = file.path(original_dir, basename(file_id_db)), @@ -97,11 +99,6 @@ import_metadata_counts <- function( # Generate cpm from counts get_counts_per_million(input_sce_obj = sce_obj, output_dir = counts_path$cpm_path, hd5_file_dir = counts_path$original_path) - - # check whether new data has the same gene set as existing metadata - (rownames(sce_obj) |> length() == genes |> length()) |> - assert_that(msg = "CuratedAtlasQuery reports: - The number of genes in the existing metadata count does not match the number of genes specified") saveHDF5SummarizedExperiment(sce_obj, counts_path$original_path, replace=TRUE) cli_alert_info("cpm are generated in {.path {counts_path$cpm_path}}. ") From 2792f11792d930826ca14a28d7674b3d818e9601 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Tue, 19 Mar 2024 13:51:32 +1100 Subject: [PATCH 79/85] change function documentation --- R/counts.R | 3 ++- R/counts_per_million.R | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/R/counts.R b/R/counts.R index bb61e06..0bcd86b 100644 --- a/R/counts.R +++ b/R/counts.R @@ -341,7 +341,8 @@ check_gene_overlap <- function(sce_list) { if (any(unique_genes_count > 0)) { single_line_str( - "CuratedAtlasQuery reports: Not all genes completely overlap across the provided SCE objects" + "CuratedAtlasQuery reports: Not all genes completely overlap across the provided SCE objects. + Counts are generated by genes intersection" ) |> cli_alert_warning() } diff --git a/R/counts_per_million.R b/R/counts_per_million.R index 41d641b..d2d5362 100644 --- a/R/counts_per_million.R +++ b/R/counts_per_million.R @@ -14,7 +14,7 @@ #' @importFrom SingleCellExperiment SingleCellExperiment #' @importFrom dplyr tbl #' @importFrom glue glue -#' @importFrom HDF5Array loadHDF5SummarizedExperiment saveHDF5SummarizedExperiment +#' @importFrom HDF5Array saveHDF5SummarizedExperiment #' @importFrom scuttle calculateCPM #' @importFrom DelayedArray realize #' @importFrom purrr map From 1209f7c2706c466646ff9db8d00f26cde111b512 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Tue, 19 Mar 2024 14:02:04 +1100 Subject: [PATCH 80/85] fix assert_that --- R/import_metadata_and_counts.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index a77af51..bc9377c 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -35,7 +35,7 @@ import_metadata_counts <- function( metadata(sce_obj)$data <- metadata_tbl # Remove existing reducedDim slot to enable get_SCE API functionality - if (assert_that(length(names(reducedDims(sce_obj))) >0 )) { + if (length(names(reducedDims(sce_obj))) >0 ) { reducedDims(sce_obj) <- NULL } From 1ddec5e92d90f3bed4c2e8c9479d72dca593e31e Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Wed, 20 Mar 2024 10:05:33 +1100 Subject: [PATCH 81/85] update Rd and local checks --- DESCRIPTION | 11 ++++--- NAMESPACE | 5 +--- R/counts_per_million.R | 18 +++++------- R/import_metadata_and_counts.R | 53 +++++++++++++++++++--------------- man/get_counts_per_million.Rd | 9 +++--- man/import_metadata_counts.Rd | 13 +++++---- 6 files changed, 56 insertions(+), 53 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index a3a56d5..efcd8e6 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -94,11 +94,7 @@ Imports: dbplyr (>= 2.3.0), duckdb, stringr, - DelayedArray, - arrow, - checkmate, - fs, - scuttle + checkmate Suggests: zellkonverter, rmarkdown, @@ -111,7 +107,10 @@ Suggests: forcats, ggplot2, tidySingleCellExperiment, - rprojroot + rprojroot, + openssl, + scuttle, + DelayedArray Biarch: true biocViews: AssayDomain, diff --git a/NAMESPACE b/NAMESPACE index 3c2bd72..593b820 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -15,7 +15,6 @@ export(import_metadata_counts) importFrom(BiocGenerics,cbind) importFrom(DBI,dbConnect) importFrom(DBI,dbDisconnect) -importFrom(DelayedArray,realize) importFrom(HDF5Array,HDF5RealizationSink) importFrom(HDF5Array,loadHDF5SummarizedExperiment) importFrom(HDF5Array,saveHDF5SummarizedExperiment) @@ -28,7 +27,6 @@ importFrom(SingleCellExperiment,combineCols) importFrom(SummarizedExperiment,"assayNames<-") importFrom(SummarizedExperiment,"colData<-") importFrom(SummarizedExperiment,colData) -importFrom(arrow,write_parquet) importFrom(assertthat,assert_that) importFrom(assertthat,has_name) importFrom(checkmate,check_character) @@ -52,6 +50,7 @@ importFrom(dplyr,group_by) importFrom(dplyr,inner_join) importFrom(dplyr,mutate) importFrom(dplyr,pull) +importFrom(dplyr,select) importFrom(dplyr,summarise) importFrom(dplyr,tbl) importFrom(dplyr,transmute) @@ -66,7 +65,6 @@ importFrom(httr,progress) importFrom(httr,stop_for_status) importFrom(httr,write_disk) importFrom(methods,as) -importFrom(openssl,md5) importFrom(purrr,imap) importFrom(purrr,keep) importFrom(purrr,map) @@ -78,7 +76,6 @@ importFrom(purrr,reduce) importFrom(purrr,set_names) importFrom(purrr,walk) importFrom(rlang,.data) -importFrom(scuttle,calculateCPM) importFrom(stats,setNames) importFrom(stringr,str_remove_all) importFrom(stringr,str_replace_all) diff --git a/R/counts_per_million.R b/R/counts_per_million.R index d2d5362..623fda9 100644 --- a/R/counts_per_million.R +++ b/R/counts_per_million.R @@ -5,20 +5,18 @@ #' @param hd5_file_dir A character vector of HDF5 file path after converting from RDS #' @export #' @return A directory stores counts per million -#' @examples -#' input_sce_obj <- sample_sce_obj -#' output_dir <- "~/projects/caq/cache_for_testing/cpm/8df700ab083ab215e618fe732e2f3dfe" -#' hd5_file_dir <- "~/projects/caq/cache_for_testing/original/8df700ab083ab215e618fe732e2f3dfe" -#' get_counts_per_million(input_sce_obj, output_dir, hd5_file_dir) -#' #' @importFrom SingleCellExperiment SingleCellExperiment #' @importFrom dplyr tbl #' @importFrom glue glue #' @importFrom HDF5Array saveHDF5SummarizedExperiment -#' @importFrom scuttle calculateCPM -#' @importFrom DelayedArray realize #' @importFrom purrr map - +#' @examples +#' \dontrun{ +#' input_sce_obj <- readRDS("~/path/sample_sce.rds") +#' output_dir <- "~/cache_directory/cpm/file_id_db" +#' hd5_file_dir <- "~/cache_directory/original/file_id_db" +#' get_counts_per_million(input_sce_obj, output_dir, hd5_file_dir) +#' } get_counts_per_million <- function(input_sce_obj, output_dir, hd5_file_dir) { # Create directories @@ -51,7 +49,7 @@ get_counts_per_million <- function(input_sce_obj, output_dir, hd5_file_dir) { gc() # Check if there is a memory issue - assays(sce) <- assays(sce) |> purrr::map(realize) + SummarizedExperiment::assays(sce) <- SummarizedExperiment::assays(sce) |> map(DelayedArray::realize) sce |> saveHDF5SummarizedExperiment(output_dir) } diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index bc9377c..3cd76f2 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -1,42 +1,45 @@ #' Checks importing criteria for new atlas #' Generats counts per million from provided raw counts #' -#' @param sce_obj A SingleCellExperiment object read from RDS +#' @param sce_obj A SingleCellExperiment object from RDS #' @param cache_dir Optional character vector of length 1. A file path on #' your local system to a directory (not a file) that will be used to store #' `metadata.parquet` #' @export -#' @return A metadata.parquet, and a counts per million directory in provided cache directory -#' @examples -#' import_metadata_counts(sce_obj = sample_sce_obj, -#' cache_directory = "~/projects/caq/cache_for_testing") -#' +#' @return A metadata.parquet strip from SCE object. +#' Directories store counts and counts per million in the provided cache directory. #' @importFrom assertthat assert_that #' @importFrom checkmate check_tibble check_directory_exists check_set_equal check_true check_character check_subset check_file_exists -#' @importFrom dplyr tbl +#' @importFrom dplyr tbl select #' @importFrom cli cli_alert_info #' @importFrom glue glue -#' @importFrom arrow write_parquet -#' @importFrom openssl md5 +#' @importFrom rlang .data +#' @examples +#' \dontrun{ +#' sample_sce_obj <- readRDS("~/path/sample_sce.rds") +#' import_metadata_counts(sce_obj = sample_sce_obj, +#' cache_directory = "~/cache_directory") +#' } import_metadata_counts <- function( - sce_obj = data, + sce_obj, cache_dir = get_default_cache_dir()) { original_dir <- file.path(cache_dir, "original") # Identify metadata and counts matrix - metadata_tbl <- metadata(sce_obj)$data + metadata_tbl <- sce_obj@metadata$data counts_matrix <- sce_obj@assays@data$X # Convert to tibble if metadata_tbl is not a tibble metadata_tbl <- metadata_tbl |> as_tibble() # Create file_id_db from dataset_id - metadata_tbl <- metadata_tbl |> mutate(file_id_db = dataset_id |> md5() |> as.character()) - metadata(sce_obj)$data <- metadata_tbl + metadata_tbl <- + metadata_tbl |> mutate(file_id_db = .data$dataset_id |> openssl::md5() |> as.character()) + sce_obj@metadata$data <- metadata_tbl # Remove existing reducedDim slot to enable get_SCE API functionality - if (length(names(reducedDims(sce_obj))) >0 ) { - reducedDims(sce_obj) <- NULL + if (length(names(SingleCellExperiment::reducedDims(sce_obj))) >0 ) { + SingleCellExperiment::reducedDims(sce_obj) <- NULL } # create original and cpm folders in cache directory if not exist (in order to append new counts to existing ones) @@ -68,13 +71,15 @@ import_metadata_counts <- function( # check the metadata contains cell_, file_id_db, sample_ with correct types check_true("cell_" %in% names(metadata_tbl)) check_true("file_id_db" %in% names(metadata_tbl)) - metadata_tbl |> select(cell_, file_id_db) |> sapply(class) |> check_character() + select(metadata_tbl, .data$cell_) |> class() |> check_character() + select(metadata_tbl, .data$file_id_db) |> class() |> check_character() + #metadata_tbl |> select(cell_, file_id_db) |> sapply(class) |> check_character() # check cell_ values in metadata_tbl is unique (anyDuplicated(metadata_tbl$cell_) == 0 ) |> assert_that(msg = "cell_ in the metadata must be unique") # check cell_ values are not duplicated when join with parquet - cells <- get_metadata() |> select(cell_) |> as_tibble() + cells <- select(get_metadata(), .data$cell_) |> as_tibble() (!any(metadata_tbl$cell_ %in% cells$cell_)) |> assert_that(msg = "cell_ in the metadata should not duplicate with that exists in API") # check age_days is either -99 or greater than 365 @@ -85,13 +90,13 @@ import_metadata_counts <- function( # check sex capitalisation then convert to lower case if (any(colnames(metadata_tbl) == "sex")) { - metadata_tbl <- metadata_tbl |> mutate(sex = tolower(sex)) - metadata_tbl |> distinct(sex) |> pull() |> check_subset(c("female","male","unknown")) + metadata_tbl <- metadata_tbl |> mutate(sex = tolower(.data$sex)) + dplyr::distinct(metadata_tbl, .data$sex) |> dplyr::pull() |> check_subset(c("female","male","unknown")) } counts_path <- - metadata_tbl |> select(file_id_db) |> mutate( - original_path = file.path(original_dir, basename(file_id_db)), - cpm_path = file.path(cache_dir, "cpm", basename(file_id_db)) + select(metadata_tbl, .data$file_id_db) |> mutate( + original_path = file.path(original_dir, basename(.data$file_id_db)), + cpm_path = file.path(cache_dir, "cpm", basename(.data$file_id_db)) ) # if checkpoints above pass, generate cpm @@ -104,11 +109,11 @@ import_metadata_counts <- function( cli_alert_info("cpm are generated in {.path {counts_path$cpm_path}}. ") # check metadata sample file ID match the count file ID in cache directory - all(metadata_tbl |> pull(file_id_db) %in% dir(original_dir)) |> + all(metadata_tbl |> pull(.data$file_id_db) %in% dir(original_dir)) |> assert_that(msg = "The metadata sample file ID and the count file ID does not match") # convert metadata_tbl to parquet if above checkpoints pass - write_parquet(metadata_tbl, file.path(cache_dir, glue("metadata.parquet"))) + arrow::write_parquet(metadata_tbl, file.path(cache_dir, glue("metadata.parquet"))) } diff --git a/man/get_counts_per_million.Rd b/man/get_counts_per_million.Rd index 9603664..23c09a3 100644 --- a/man/get_counts_per_million.Rd +++ b/man/get_counts_per_million.Rd @@ -20,9 +20,10 @@ A directory stores counts per million Generating counts per million from SingleCellExperiment object } \examples{ -input_sce_obj <- sample_sce_obj -output_dir <- "~/projects/caq/cache_for_testing/cpm/8df700ab083ab215e618fe732e2f3dfe" -hd5_file_dir <- "~/projects/caq/cache_for_testing/original/8df700ab083ab215e618fe732e2f3dfe" +\dontrun{ +input_sce_obj <- readRDS("~/path/sample_sce.rds") +output_dir <- "~/cache_directory/cpm/file_id_db" +hd5_file_dir <- "~/cache_directory/original/file_id_db" get_counts_per_million(input_sce_obj, output_dir, hd5_file_dir) - +} } diff --git a/man/import_metadata_counts.Rd b/man/import_metadata_counts.Rd index 8312c9f..a352dd4 100644 --- a/man/import_metadata_counts.Rd +++ b/man/import_metadata_counts.Rd @@ -5,24 +5,27 @@ \title{Checks importing criteria for new atlas Generats counts per million from provided raw counts} \usage{ -import_metadata_counts(sce_obj = data, cache_dir = get_default_cache_dir()) +import_metadata_counts(sce_obj, cache_dir = get_default_cache_dir()) } \arguments{ -\item{sce_obj}{A SingleCellExperiment object read from RDS} +\item{sce_obj}{A SingleCellExperiment object from RDS} \item{cache_dir}{Optional character vector of length 1. A file path on your local system to a directory (not a file) that will be used to store \code{metadata.parquet}} } \value{ -A metadata.parquet, and a counts per million directory in provided cache directory +A metadata.parquet strip from SCE object. +Directories store counts and counts per million in the provided cache directory. } \description{ Checks importing criteria for new atlas Generats counts per million from provided raw counts } \examples{ +\dontrun{ +sample_sce_obj <- readRDS("~/path/sample_sce.rds") import_metadata_counts(sce_obj = sample_sce_obj, - cache_directory = "~/projects/caq/cache_for_testing") - + cache_directory = "~/cache_directory") +} } From 883402bf35f42627686a36f2340633501be1f1e7 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Wed, 20 Mar 2024 16:39:28 +1100 Subject: [PATCH 82/85] export necessary functions. create sample_sce_obj for checking purpose --- DESCRIPTION | 2 ++ NAMESPACE | 2 -- R/counts_per_million.R | 31 ++++++++++++------------------- R/data.R | 8 ++++++++ R/import_metadata_and_counts.R | 8 +++----- R/utils.R | 4 +++- data/sample_sce_obj.rda | Bin 0 -> 3949584 bytes man/clear_cache.Rd | 15 --------------- man/get_counts_per_million.Rd | 29 ----------------------------- man/get_default_cache_dir.Rd | 3 +++ man/import_metadata_counts.Rd | 8 +++----- man/sample_sce_obj.Rd | 19 +++++++++++++++++++ 12 files changed, 53 insertions(+), 76 deletions(-) create mode 100644 R/data.R create mode 100644 data/sample_sce_obj.rda delete mode 100644 man/clear_cache.Rd delete mode 100644 man/get_counts_per_million.Rd create mode 100644 man/sample_sce_obj.Rd diff --git a/DESCRIPTION b/DESCRIPTION index efcd8e6..4cae98f 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -135,6 +135,7 @@ Collate: 'utils.R' 'counts.R' 'counts_per_million.R' + 'data.R' 'dev.R' 'import_metadata_and_counts.R' 'metadata.R' @@ -142,3 +143,4 @@ Collate: 'unharmonised.R' 'zzz.R' Language: en-US +LazyData: true diff --git a/NAMESPACE b/NAMESPACE index 593b820..90bbc42 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -2,9 +2,7 @@ S3method(as.sparse,DelayedMatrix) export(SAMPLE_DATABASE_URL) -export(clear_cache) export(get_SingleCellExperiment) -export(get_counts_per_million) export(get_database_url) export(get_default_cache_dir) export(get_metadata) diff --git a/R/counts_per_million.R b/R/counts_per_million.R index 623fda9..14fc991 100644 --- a/R/counts_per_million.R +++ b/R/counts_per_million.R @@ -3,20 +3,13 @@ #' @param input_sce_obj A SingleCellExperiment object read from RDS #' @param output_dir A character vector of counts per million path #' @param hd5_file_dir A character vector of HDF5 file path after converting from RDS -#' @export #' @return A directory stores counts per million #' @importFrom SingleCellExperiment SingleCellExperiment #' @importFrom dplyr tbl #' @importFrom glue glue #' @importFrom HDF5Array saveHDF5SummarizedExperiment #' @importFrom purrr map -#' @examples -#' \dontrun{ -#' input_sce_obj <- readRDS("~/path/sample_sce.rds") -#' output_dir <- "~/cache_directory/cpm/file_id_db" -#' hd5_file_dir <- "~/cache_directory/original/file_id_db" -#' get_counts_per_million(input_sce_obj, output_dir, hd5_file_dir) -#' } +#' @noRd get_counts_per_million <- function(input_sce_obj, output_dir, hd5_file_dir) { # Create directories @@ -28,22 +21,22 @@ get_counts_per_million <- function(input_sce_obj, output_dir, hd5_file_dir) { data <- input_sce_obj # Avoid completely empty cells - col_sums = colSums(as.matrix(data@assays@data$X)) - which_to_select = which(col_sums >0 & col_sums < Inf) + col_sums <- colSums(as.matrix(data@assays@data$X)) + which_to_select <- which(col_sums >0 & col_sums < Inf) - sce = SingleCellExperiment(list(counts_per_million = scuttle::calculateCPM(data[,which_to_select ,drop=FALSE ], assay.type = "X"))) - rownames(sce) = rownames(data[,which_to_select ]) - colnames(sce) = colnames(data[,which_to_select ]) + sce <- SingleCellExperiment(list(counts_per_million = scuttle::calculateCPM(data[,which_to_select ,drop=FALSE ], assay.type = "X"))) + rownames(sce) <- rownames(data[,which_to_select ]) + colnames(sce) <- colnames(data[,which_to_select ]) # Avoid scaling zeros - which_to_select_not = setdiff(1:ncol(data), which_to_select) - sce_zero = SingleCellExperiment(list(counts_per_million = data@assays@data$X[,which_to_select_not ,drop=FALSE ])) - rownames(sce_zero) = rownames(data[,which_to_select_not ]) - colnames(sce_zero) = colnames(data[,which_to_select_not ]) + which_to_select_not <- setdiff(1:ncol(data), which_to_select) + sce_zero <- SingleCellExperiment(list(counts_per_million = data@assays@data$X[,which_to_select_not ,drop=FALSE ])) + rownames(sce_zero) <- rownames(data[,which_to_select_not ]) + colnames(sce_zero) <- colnames(data[,which_to_select_not ]) - sce = sce |> cbind(sce_zero) + sce <- sce |> cbind(sce_zero) - sce = sce[,colnames(data)] + sce <- sce[,colnames(data)] rm(data) gc() diff --git a/R/data.R b/R/data.R new file mode 100644 index 0000000..76752f1 --- /dev/null +++ b/R/data.R @@ -0,0 +1,8 @@ +#' Sample SingleCellExperiment Object +#' +#' A sample SCE object from HeOrganAtlasData with transformation in metadata and assay +#' +#' @format An object of class \code{SingleCellExperiment} +#' @source HeOrganAtlasData Liver +#' @keywords datasets +"sample_sce_obj" diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 3cd76f2..02cac58 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -15,11 +15,9 @@ #' @importFrom glue glue #' @importFrom rlang .data #' @examples -#' \dontrun{ -#' sample_sce_obj <- readRDS("~/path/sample_sce.rds") -#' import_metadata_counts(sce_obj = sample_sce_obj, -#' cache_directory = "~/cache_directory") -#' } +#' data(sample_sce_obj) +#' import_metadata_counts(sample_sce_obj, +#' cache_dir = get_default_cache_dir()) import_metadata_counts <- function( sce_obj, cache_dir = get_default_cache_dir()) { diff --git a/R/utils.R b/R/utils.R index b29004b..68f6c39 100644 --- a/R/utils.R +++ b/R/utils.R @@ -47,6 +47,8 @@ single_line_str <- function(text){ #' @importFrom tools R_user_dir #' @importFrom utils packageName #' @keywords internal +#' @examples +#' get_metadata(cache_directory = get_default_cache_dir()) get_default_cache_dir <- function() { packageName() |> R_user_dir( @@ -58,11 +60,11 @@ get_default_cache_dir <- function() { } #' Clear the default cache directory -#' @export #' @return A length one character vector. #' @importFrom tools R_user_dir #' @importFrom utils packageName #' @keywords internal +#' @noRd clear_cache <- function() { get_default_cache_dir() |> unlink(TRUE, TRUE) } diff --git a/data/sample_sce_obj.rda b/data/sample_sce_obj.rda new file mode 100644 index 0000000000000000000000000000000000000000..414a7eb3dd16f0fd72625e5cb7c06c1d3a7c4d07 GIT binary patch literal 3949584 zcmagFV{~Rs&@Oz(cWh5=CllMYZQGvMb~53_w(ZQswr$%vdERr@ch0}>+Nxc}1kbT8mP@z)p>3jeR 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Counts are generated by genes intersection" diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 02cac58..9b44811 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -1,5 +1,5 @@ #' Checks importing criteria for new atlas -#' Generats counts per million from provided raw counts +#' Generates counts per million from provided raw counts #' #' @param sce_obj A SingleCellExperiment object from RDS #' @param cache_dir Optional character vector of length 1. A file path on @@ -19,8 +19,9 @@ #' import_metadata_counts(sample_sce_obj, #' cache_dir = get_default_cache_dir()) import_metadata_counts <- function( - sce_obj, - cache_dir = get_default_cache_dir()) { + sce_obj, + cache_dir = get_default_cache_dir() + ) { original_dir <- file.path(cache_dir, "original") # Identify metadata and counts matrix @@ -67,9 +68,9 @@ import_metadata_counts <- function( msg = "Counts for SingleCellExperiment cannot be negative.") # check the metadata contains cell_, file_id_db, sample_ with correct types - check_true("cell_" %in% names(metadata_tbl)) + check_true("cell_" %in% colnames(metadata_tbl)) check_true("file_id_db" %in% names(metadata_tbl)) - select(metadata_tbl, .data$cell_) |> class() |> check_character() + pull(metadata_tbl, .data$cell_) |> class() |> check_character() select(metadata_tbl, .data$file_id_db) |> class() |> check_character() #metadata_tbl |> select(cell_, file_id_db) |> sapply(class) |> check_character() @@ -111,8 +112,6 @@ import_metadata_counts <- function( assert_that(msg = "The metadata sample file ID and the count file ID does not match") # convert metadata_tbl to parquet if above checkpoints pass - arrow::write_parquet(metadata_tbl, file.path(cache_dir, glue("metadata.parquet"))) + arrow::write_parquet(metadata_tbl, file.path(cache_dir, "metadata.parquet")) } - - diff --git a/R/utils.R b/R/utils.R index 68f6c39..1674698 100644 --- a/R/utils.R +++ b/R/utils.R @@ -46,7 +46,6 @@ single_line_str <- function(text){ #' @return A length one character vector. #' @importFrom tools R_user_dir #' @importFrom utils packageName -#' @keywords internal #' @examples #' get_metadata(cache_directory = get_default_cache_dir()) get_default_cache_dir <- function() { From b6ee0b2de394c0946e2a10e087e3eff61ed7d8c9 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Tue, 26 Mar 2024 12:05:07 +1100 Subject: [PATCH 84/85] add suggestions and optimise code from code review --- NAMESPACE | 14 ++++-- R/counts.R | 4 +- R/counts_per_million.R | 28 +++++------ R/import_metadata_and_counts.R | 86 ++++++++++++++++------------------ man/get_default_cache_dir.Rd | 1 - man/import_metadata_counts.Rd | 8 ++-- 6 files changed, 66 insertions(+), 75 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 90bbc42..2e0a805 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -16,23 +16,27 @@ importFrom(DBI,dbDisconnect) importFrom(HDF5Array,HDF5RealizationSink) importFrom(HDF5Array,loadHDF5SummarizedExperiment) importFrom(HDF5Array,saveHDF5SummarizedExperiment) +importFrom(S4Vectors,"metadata<-") importFrom(S4Vectors,DataFrame) +importFrom(S4Vectors,metadata) importFrom(Seurat,as.SingleCellExperiment) importFrom(SeuratObject,as.Seurat) importFrom(SeuratObject,as.sparse) +importFrom(SingleCellExperiment,"reducedDims<-") importFrom(SingleCellExperiment,SingleCellExperiment) importFrom(SingleCellExperiment,combineCols) +importFrom(SingleCellExperiment,reducedDims) +importFrom(SingleCellExperiment,rowData) importFrom(SummarizedExperiment,"assayNames<-") +importFrom(SummarizedExperiment,"assays<-") importFrom(SummarizedExperiment,"colData<-") +importFrom(SummarizedExperiment,assay) +importFrom(SummarizedExperiment,assays) importFrom(SummarizedExperiment,colData) importFrom(assertthat,assert_that) importFrom(assertthat,has_name) importFrom(checkmate,check_character) -importFrom(checkmate,check_directory_exists) -importFrom(checkmate,check_file_exists) -importFrom(checkmate,check_set_equal) importFrom(checkmate,check_subset) -importFrom(checkmate,check_tibble) importFrom(checkmate,check_true) importFrom(cli,cli_abort) importFrom(cli,cli_alert_info) @@ -43,6 +47,7 @@ importFrom(dbplyr,remote_con) importFrom(dbplyr,sql) importFrom(dplyr,as_tibble) importFrom(dplyr,collect) +importFrom(dplyr,distinct) importFrom(dplyr,filter) importFrom(dplyr,group_by) importFrom(dplyr,inner_join) @@ -75,6 +80,7 @@ importFrom(purrr,set_names) importFrom(purrr,walk) importFrom(rlang,.data) importFrom(stats,setNames) +importFrom(stringr,str_detect) importFrom(stringr,str_remove_all) importFrom(stringr,str_replace_all) importFrom(tibble,column_to_rownames) diff --git a/R/counts.R b/R/counts.R index 28860c6..168f2de 100644 --- a/R/counts.R +++ b/R/counts.R @@ -219,9 +219,7 @@ group_to_sce <- function(i, df, dir_prefix, features) { if (length(cells) < nrow(df)){ single_line_str( - "Some cells were filtered out while loading {head(df$file_id_db, 1)} - because of extremely low counts. The - number of cells in the SingleCellExperiment will be less than the + "The number of cells in the SingleCellExperiment will be less than the number of cells you have selected from the metadata. Are cell IDs duplicated? Or, do cell IDs correspond to the counts file? " diff --git a/R/counts_per_million.R b/R/counts_per_million.R index 14fc991..db2a7de 100644 --- a/R/counts_per_million.R +++ b/R/counts_per_million.R @@ -1,19 +1,15 @@ -#' Generating counts per million from SingleCellExperiment object +#' Generating counts per million from a SingleCellExperiment object #' #' @param input_sce_obj A SingleCellExperiment object read from RDS #' @param output_dir A character vector of counts per million path #' @param hd5_file_dir A character vector of HDF5 file path after converting from RDS #' @return A directory stores counts per million #' @importFrom SingleCellExperiment SingleCellExperiment -#' @importFrom dplyr tbl -#' @importFrom glue glue +#' @importFrom SummarizedExperiment assay assays assays<- #' @importFrom HDF5Array saveHDF5SummarizedExperiment #' @importFrom purrr map #' @noRd get_counts_per_million <- function(input_sce_obj, output_dir, hd5_file_dir) { - - # Create directories - output_dir |> dirname() |> dir.create( showWarnings = FALSE, recursive = TRUE) # Save SCE to the cache directory counts folder input_sce_obj |> saveHDF5SummarizedExperiment(hd5_file_dir) @@ -21,28 +17,26 @@ get_counts_per_million <- function(input_sce_obj, output_dir, hd5_file_dir) { data <- input_sce_obj # Avoid completely empty cells - col_sums <- colSums(as.matrix(data@assays@data$X)) - which_to_select <- which(col_sums >0 & col_sums < Inf) + col_sums <- colSums(as.matrix(assay(data))) + selected_cols <- which(col_sums >0 & col_sums < Inf) - sce <- SingleCellExperiment(list(counts_per_million = scuttle::calculateCPM(data[,which_to_select ,drop=FALSE ], assay.type = "X"))) - rownames(sce) <- rownames(data[,which_to_select ]) - colnames(sce) <- colnames(data[,which_to_select ]) + sce <- SingleCellExperiment(list(counts_per_million = scuttle::calculateCPM(data[,selected_cols ,drop=FALSE ], assay.type = "X"))) + rownames(sce) <- rownames(data[,selected_cols ]) + colnames(sce) <- colnames(data[,selected_cols ]) # Avoid scaling zeros - which_to_select_not <- setdiff(1:ncol(data), which_to_select) - sce_zero <- SingleCellExperiment(list(counts_per_million = data@assays@data$X[,which_to_select_not ,drop=FALSE ])) - rownames(sce_zero) <- rownames(data[,which_to_select_not ]) - colnames(sce_zero) <- colnames(data[,which_to_select_not ]) + sce_zero <- SingleCellExperiment(list(counts_per_million = assay(data)[, !selected_cols ,drop=FALSE ])) + rownames(sce_zero) <- rownames(data[, !selected_cols ]) + colnames(sce_zero) <- colnames(data[, !selected_cols ]) sce <- sce |> cbind(sce_zero) sce <- sce[,colnames(data)] rm(data) - gc() # Check if there is a memory issue - SummarizedExperiment::assays(sce) <- SummarizedExperiment::assays(sce) |> map(DelayedArray::realize) + assays(sce) <- assays(sce) |> map(DelayedArray::realize) sce |> saveHDF5SummarizedExperiment(output_dir) } diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index 9b44811..e06b702 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -1,19 +1,21 @@ -#' Checks importing criteria for new atlas -#' Generates counts per million from provided raw counts +#' Import and process metadata and counts for a SingleCellExperiment object #' #' @param sce_obj A SingleCellExperiment object from RDS #' @param cache_dir Optional character vector of length 1. A file path on #' your local system to a directory (not a file) that will be used to store #' `metadata.parquet` #' @export -#' @return A metadata.parquet strip from SCE object. +#' @return A metadata.parquet strip from the SingleCellExperiment object. #' Directories store counts and counts per million in the provided cache directory. #' @importFrom assertthat assert_that -#' @importFrom checkmate check_tibble check_directory_exists check_set_equal check_true check_character check_subset check_file_exists -#' @importFrom dplyr tbl select +#' @importFrom checkmate check_true check_character check_subset +#' @importFrom dplyr select distinct pull #' @importFrom cli cli_alert_info -#' @importFrom glue glue #' @importFrom rlang .data +#' @importFrom SingleCellExperiment reducedDims rowData reducedDims<- +#' @importFrom S4Vectors metadata metadata<- +#' @importFrom SummarizedExperiment assay +#' @importFrom stringr str_detect #' @examples #' data(sample_sce_obj) #' import_metadata_counts(sample_sce_obj, @@ -25,72 +27,69 @@ import_metadata_counts <- function( original_dir <- file.path(cache_dir, "original") # Identify metadata and counts matrix - metadata_tbl <- sce_obj@metadata$data - counts_matrix <- sce_obj@assays@data$X + metadata_tbl <- metadata(sce_obj)$data + counts_matrix <- assay(sce_obj) + + # Identify whether genes in SingleCellxExperiment object are in ensembl nomenclature + genes <- rowData(sce_obj) |> rownames() + assert_that(sce_obj |> inherits( "SingleCellExperiment"), + msg = "sce_obj is not identified as SingleCellExperiment object.") + assert_that(!str_detect(genes, "^ENSG%") |> all(), + msg = "Gene names in SingleCellExperiment object cannot contain Ensembl IDs.") + assert_that(all(counts_matrix >= 0), + msg = "Counts for SingleCellExperiment cannot be negative.") - # Convert to tibble if metadata_tbl is not a tibble + # Convert to tibble if not provided metadata_tbl <- metadata_tbl |> as_tibble() # Create file_id_db from dataset_id metadata_tbl <- metadata_tbl |> mutate(file_id_db = .data$dataset_id |> openssl::md5() |> as.character()) - sce_obj@metadata$data <- metadata_tbl + metadata(sce_obj)$data <- metadata_tbl # Remove existing reducedDim slot to enable get_SCE API functionality - if (length(names(SingleCellExperiment::reducedDims(sce_obj))) >0 ) { - SingleCellExperiment::reducedDims(sce_obj) <- NULL + if (length(names(reducedDims(sce_obj))) >0 ) { + reducedDims(sce_obj) <- NULL } - # create original and cpm folders in cache directory if not exist (in order to append new counts to existing ones) + # Create original and cpm folders in the cache directory if not exist if (!dir.exists(original_dir)) { - dir.create(cache_dir, "original", recursive = TRUE) + cache_dir |> file.path("original") |> dir.create(recursive = TRUE) } if (!dir.exists(file.path(cache_dir, "cpm"))) { - dir.create(cache_dir, "cpm", recursive = TRUE) + cache_dir |> file.path("cpm") |> dir.create(recursive = TRUE) } - # existing metadata genes - genes <- get_metadata() |> head(1) |> - get_single_cell_experiment(cache_directory = cache_dir) |> rownames() - - # check count H5 directory name not included in the cache directory original + # Check whether count H5 directory has been generated all(!metadata_tbl$file_id_db %in% dir(original_dir)) |> check_true() |> - assert_that(msg = "Count H5 directory name should not duplicate with that in cache directory") - - counts_data <- sce_obj@assays@data$X - assert_that(inherits(sce_obj, "SingleCellExperiment"), - msg = "SingleCellExperiment Object is not created from metadata.") - assert_that(!"^ENS" %in% genes, - msg = "Gene names cannot contain Ensembl IDs.") - assert_that(all(counts_data >= 0), - msg = "Counts for SingleCellExperiment cannot be negative.") + assert_that(msg = "The filename for count assay (file_id_db) already exists in the cache directory.") - # check the metadata contains cell_, file_id_db, sample_ with correct types + # Check the metadata contains cell_, file_id_db, sample_ with correct types check_true("cell_" %in% colnames(metadata_tbl)) check_true("file_id_db" %in% names(metadata_tbl)) pull(metadata_tbl, .data$cell_) |> class() |> check_character() select(metadata_tbl, .data$file_id_db) |> class() |> check_character() - #metadata_tbl |> select(cell_, file_id_db) |> sapply(class) |> check_character() - # check cell_ values in metadata_tbl is unique - (anyDuplicated(metadata_tbl$cell_) == 0 ) |> assert_that(msg = "cell_ in the metadata must be unique") + # Check cell_ values in metadata_tbl is unique + (anyDuplicated(metadata_tbl$cell_) == 0 ) |> assert_that(msg = "Cell names (cell_) in the metadata must be unique.") - # check cell_ values are not duplicated when join with parquet - cells <- select(get_metadata(), .data$cell_) |> as_tibble() - (!any(metadata_tbl$cell_ %in% cells$cell_)) |> assert_that(msg = "cell_ in the metadata should not duplicate with that exists in API") + # Check cell_ values are not duplicated when join with parquet + cells <- select(get_metadata(cache_directory = cache_dir), .data$cell_) |> as_tibble() + (!any(metadata_tbl$cell_ %in% cells$cell_)) |> + assert_that(msg = "Cell names (cell_) should not clash with cells that already exist in the atlas.") - # check age_days is either -99 or greater than 365 + # Check age_days is either -99 or greater than 365 if (any(colnames(metadata_tbl) == "age_days")) { assert_that(all(metadata_tbl$age_days==-99 | metadata_tbl$age_days> 365), - msg = "age_days should be either -99 for unknown or greater than 365") + msg = "age_days should be either -99 for unknown or greater than 365.") } - # check sex capitalisation then convert to lower case + # Check sex capitalisation then convert to lower case if (any(colnames(metadata_tbl) == "sex")) { metadata_tbl <- metadata_tbl |> mutate(sex = tolower(.data$sex)) - dplyr::distinct(metadata_tbl, .data$sex) |> dplyr::pull() |> check_subset(c("female","male","unknown")) + distinct(metadata_tbl, .data$sex) |> pull(.data$sex) |> check_subset(c("female","male","unknown")) } counts_path <- select(metadata_tbl, .data$file_id_db) |> mutate( @@ -98,18 +97,15 @@ import_metadata_counts <- function( cpm_path = file.path(cache_dir, "cpm", basename(.data$file_id_db)) ) - # if checkpoints above pass, generate cpm - cli_alert_info("Generating cpm from {.path {metadata_tbl$file_id_db}}. ") - # Generate cpm from counts + cli_alert_info("Generating cpm from {.path {metadata_tbl$file_id_db}}. ") get_counts_per_million(input_sce_obj = sce_obj, output_dir = counts_path$cpm_path, hd5_file_dir = counts_path$original_path) saveHDF5SummarizedExperiment(sce_obj, counts_path$original_path, replace=TRUE) - cli_alert_info("cpm are generated in {.path {counts_path$cpm_path}}. ") # check metadata sample file ID match the count file ID in cache directory all(metadata_tbl |> pull(.data$file_id_db) %in% dir(original_dir)) |> - assert_that(msg = "The metadata sample file ID and the count file ID does not match") + assert_that(msg = "The filename for count assay, which matches the file_id_db column in the metadata, already exists in the cache directory.") # convert metadata_tbl to parquet if above checkpoints pass arrow::write_parquet(metadata_tbl, file.path(cache_dir, "metadata.parquet")) diff --git a/man/get_default_cache_dir.Rd b/man/get_default_cache_dir.Rd index 1650fcb..834b7d0 100644 --- a/man/get_default_cache_dir.Rd +++ b/man/get_default_cache_dir.Rd @@ -15,4 +15,3 @@ Returns the default cache directory with a version number \examples{ get_metadata(cache_directory = get_default_cache_dir()) } -\keyword{internal} diff --git a/man/import_metadata_counts.Rd b/man/import_metadata_counts.Rd index 995bb09..b48d47f 100644 --- a/man/import_metadata_counts.Rd +++ b/man/import_metadata_counts.Rd @@ -2,8 +2,7 @@ % Please edit documentation in R/import_metadata_and_counts.R \name{import_metadata_counts} \alias{import_metadata_counts} -\title{Checks importing criteria for new atlas -Generats counts per million from provided raw counts} +\title{Import and process metadata and counts for a SingleCellExperiment object} \usage{ import_metadata_counts(sce_obj, cache_dir = get_default_cache_dir()) } @@ -15,12 +14,11 @@ your local system to a directory (not a file) that will be used to store \code{metadata.parquet}} } \value{ -A metadata.parquet strip from SCE object. +A metadata.parquet strip from the SingleCellExperiment object. Directories store counts and counts per million in the provided cache directory. } \description{ -Checks importing criteria for new atlas -Generats counts per million from provided raw counts +Import and process metadata and counts for a SingleCellExperiment object } \examples{ data(sample_sce_obj) From f672472829d65866cb8f9bc48a17c365e9241b20 Mon Sep 17 00:00:00 2001 From: Mengyuan Shen Date: Tue, 26 Mar 2024 14:09:47 +1100 Subject: [PATCH 85/85] replace assert_that with assert --- NAMESPACE | 1 + R/counts.R | 2 +- R/data.R | 2 +- R/import_metadata_and_counts.R | 27 +++++++++++++-------------- R/utils.R | 2 -- man/sample_sce_obj.Rd | 2 +- 6 files changed, 17 insertions(+), 19 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 2e0a805..d6b858e 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -35,6 +35,7 @@ importFrom(SummarizedExperiment,assays) importFrom(SummarizedExperiment,colData) importFrom(assertthat,assert_that) importFrom(assertthat,has_name) +importFrom(checkmate,assert) importFrom(checkmate,check_character) importFrom(checkmate,check_subset) importFrom(checkmate,check_true) diff --git a/R/counts.R b/R/counts.R index 168f2de..e1636d8 100644 --- a/R/counts.R +++ b/R/counts.R @@ -328,7 +328,7 @@ sync_assay_files <- function( #' Checks whether genes in a list of SCE objects overlap #' @param sce_list A list of SingleCellExperiment objects #' @return A character vector of genes intersection across SCE objects -#' @importFrom purrr map reduce map_int +#' @importFrom purrr map reduce #' @importFrom cli cli_alert_warning #' @noRd check_gene_overlap <- function(sce_list) { diff --git a/R/data.R b/R/data.R index 76752f1..d950d16 100644 --- a/R/data.R +++ b/R/data.R @@ -1,6 +1,6 @@ #' Sample SingleCellExperiment Object #' -#' A sample SCE object from HeOrganAtlasData with transformation in metadata and assay +#' A sample SingleCellExperiment object from HeOrganAtlasData with transformation in metadata and assay #' #' @format An object of class \code{SingleCellExperiment} #' @source HeOrganAtlasData Liver diff --git a/R/import_metadata_and_counts.R b/R/import_metadata_and_counts.R index e06b702..219f19b 100644 --- a/R/import_metadata_and_counts.R +++ b/R/import_metadata_and_counts.R @@ -7,8 +7,7 @@ #' @export #' @return A metadata.parquet strip from the SingleCellExperiment object. #' Directories store counts and counts per million in the provided cache directory. -#' @importFrom assertthat assert_that -#' @importFrom checkmate check_true check_character check_subset +#' @importFrom checkmate check_true check_character check_subset assert #' @importFrom dplyr select distinct pull #' @importFrom cli cli_alert_info #' @importFrom rlang .data @@ -32,12 +31,12 @@ import_metadata_counts <- function( # Identify whether genes in SingleCellxExperiment object are in ensembl nomenclature genes <- rowData(sce_obj) |> rownames() - assert_that(sce_obj |> inherits( "SingleCellExperiment"), - msg = "sce_obj is not identified as SingleCellExperiment object.") - assert_that(!str_detect(genes, "^ENSG%") |> all(), - msg = "Gene names in SingleCellExperiment object cannot contain Ensembl IDs.") - assert_that(all(counts_matrix >= 0), - msg = "Counts for SingleCellExperiment cannot be negative.") + assert(sce_obj |> inherits( "SingleCellExperiment"), + "sce_obj is not identified as SingleCellExperiment object.") + assert(!str_detect(genes, "^ENSG%") |> all(), + "Gene names in SingleCellExperiment object cannot contain Ensembl IDs.") + assert(all(counts_matrix >= 0), + "Counts for SingleCellExperiment cannot be negative.") # Convert to tibble if not provided metadata_tbl <- metadata_tbl |> as_tibble() @@ -64,7 +63,7 @@ import_metadata_counts <- function( # Check whether count H5 directory has been generated all(!metadata_tbl$file_id_db %in% dir(original_dir)) |> check_true() |> - assert_that(msg = "The filename for count assay (file_id_db) already exists in the cache directory.") + assert("The filename for count assay (file_id_db) already exists in the cache directory.") # Check the metadata contains cell_, file_id_db, sample_ with correct types check_true("cell_" %in% colnames(metadata_tbl)) @@ -73,17 +72,17 @@ import_metadata_counts <- function( select(metadata_tbl, .data$file_id_db) |> class() |> check_character() # Check cell_ values in metadata_tbl is unique - (anyDuplicated(metadata_tbl$cell_) == 0 ) |> assert_that(msg = "Cell names (cell_) in the metadata must be unique.") + (anyDuplicated(metadata_tbl$cell_) == 0 ) |> assert("Cell names (cell_) in the metadata must be unique.") # Check cell_ values are not duplicated when join with parquet cells <- select(get_metadata(cache_directory = cache_dir), .data$cell_) |> as_tibble() (!any(metadata_tbl$cell_ %in% cells$cell_)) |> - assert_that(msg = "Cell names (cell_) should not clash with cells that already exist in the atlas.") + assert("Cell names (cell_) should not clash with cells that already exist in the atlas.") # Check age_days is either -99 or greater than 365 if (any(colnames(metadata_tbl) == "age_days")) { - assert_that(all(metadata_tbl$age_days==-99 | metadata_tbl$age_days> 365), - msg = "age_days should be either -99 for unknown or greater than 365.") + assert(all(metadata_tbl$age_days==-99 | metadata_tbl$age_days> 365), + "age_days should be either -99 for unknown or greater than 365.") } # Check sex capitalisation then convert to lower case @@ -105,7 +104,7 @@ import_metadata_counts <- function( # check metadata sample file ID match the count file ID in cache directory all(metadata_tbl |> pull(.data$file_id_db) %in% dir(original_dir)) |> - assert_that(msg = "The filename for count assay, which matches the file_id_db column in the metadata, already exists in the cache directory.") + assert("The filename for count assay, which matches the file_id_db column in the metadata, already exists in the cache directory.") # convert metadata_tbl to parquet if above checkpoints pass arrow::write_parquet(metadata_tbl, file.path(cache_dir, "metadata.parquet")) diff --git a/R/utils.R b/R/utils.R index 1674698..831765d 100644 --- a/R/utils.R +++ b/R/utils.R @@ -60,8 +60,6 @@ get_default_cache_dir <- function() { #' Clear the default cache directory #' @return A length one character vector. -#' @importFrom tools R_user_dir -#' @importFrom utils packageName #' @keywords internal #' @noRd clear_cache <- function() { diff --git a/man/sample_sce_obj.Rd b/man/sample_sce_obj.Rd index 1a983e4..ffd7952 100644 --- a/man/sample_sce_obj.Rd +++ b/man/sample_sce_obj.Rd @@ -14,6 +14,6 @@ HeOrganAtlasData Liver sample_sce_obj } \description{ -A sample SCE object from HeOrganAtlasData with transformation in metadata and assay +A sample SingleCellExperiment object from HeOrganAtlasData with transformation in metadata and assay } \keyword{datasets}

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