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1_mesenchyme.rmd
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1_mesenchyme.rmd
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---
title: "scSEQ analysis of the developing mesenchyme - revision"
author: Nick Negretti
date: 09/17/21
output: rmarkdown::github_document
---
# Analysis of the lung mesenchyme
## Load libraries and helper functions
```{r, results="hide", message = FALSE}
setwd("~/postdoc/code/devo_scseq_github")
renv::activate()
source("./helper_functions/globals.R")
source("./helper_functions/libraries.R")
opts_knit$set(root.dir = getwd())
source("./helper_functions/trajectory.R")
source("./helper_functions/cluster.R")
source("./helper_functions/colors.R")
source("./helper_functions/brackets.R")
source("./helper_functions/heatmaps.R")
N_WORKERS <- 12
plan("multiprocess", workers = N_WORKERS)
RhpcBLASctl::blas_set_num_threads(4)
RhpcBLASctl::omp_set_num_threads(4)
```
## Load data from merge and clean pipeline
```{r}
meso_from_split <- readRDS("./data/20221020_meso_devo_data_sct.rds")
meso_from_split$HTO_HTO_classification.global[is.na(meso_from_split$HTO_HTO_classification.global)] <- "Undefined"
meso <- subset(meso_from_split, HTO_HTO_classification.global != "Doublet")
```
```{r}
meso <- cluster_pca_umap(meso, dims_umap = 1:30, dims_neighbors = 1:30, cluster_res = 1.8)
```
## UMAP and clustering
```{r, fig.width = 12}
# Make the tiempoints an ordered factor, so they are displayed consistantly
meso$timepoint <- ordered(as.factor(meso$timepoint), unique(meso$timepoint))
p_cluster <- DimPlot(meso, label = TRUE) + umap_theme() +
theme(aspect.ratio=1)
p_time <- DimPlot(meso, group.by = "timepoint") + umap_theme() +
theme(aspect.ratio=1)
p_cluster + p_time + plot_annotation("Mesenchyme")
```
```{r}
marker_genes <- c("Hbb-bs", "Epcam", "Foxj1", "Scgb1a1", "Scgb3a2", "Abca3", "Hopx", "Col1a1", "Dcn", "Lum", "Acta2", "Wnt2", "Wnt5a", "Lgr6", "Pdgfra", "Pdgfrb", "Cspg4", "Wt1", "Pecam1", "Ccl21a", "Vwf", "Nrg1", "Plvap", "Car4", "Mki67", "Tnnt2", "Myl7", "Ascl1", "Ptprc")
DotPlot(meso, features = marker_genes) + theme(axis.text.x = element_text(angle = 45, hjust=1))
```
```{r}
# Drop anything with Pecam1 or Epcam expression
meso_sub <- subset(meso, idents = c(42, 47), invert = TRUE)
```
```{r}
meso_sub <- cluster_pca_umap(meso_sub, dims_umap = 1:30, dims_neighbors = 1:30, cluster_res = 1.8)
```
```{r}
marker_genes <- c("Hbb-bs", "Epcam", "Foxj1", "Scgb1a1", "Scgb3a2", "Abca3", "Hopx", "Col1a1", "Dcn", "Lum", "Acta2", "Wnt2", "Wnt5a", "Lgr6", "Pdgfra", "Pdgfrb", "Cspg4", "Wt1", "Pecam1", "Ccl21a", "Vwf", "Nrg1", "Plvap", "Car4", "Mki67", "Tnnt2", "Myl7", "Ascl1", "Ptprc")
DotPlot(meso_sub, features = marker_genes) + theme(axis.text.x = element_text(angle = 45, hjust=1))
```
```{r}
N_WORKERS <- 12
plan("multiprocess", workers = N_WORKERS)
filename <- "./data/20210802_meso_devo_data_sct_sub_v2.rds"
if (!file.exists(filename)) {
meso_sub <- SCTransform(meso_sub, variable.features.n = 2000,
batch_var = "orig.ident",
conserve.memory = TRUE,
vars.to.regress = c("percent.mt"),
method = 'glmGamPoi',
ncells = NULL)
meso_sub <- cluster_pca_umap(meso_sub, dims_umap = 1:15, dims_neighbors = 1:15, cluster_res = 1.2)
saveRDS(meso_sub, filename, compress = "bzip2")
} else {
meso_sub <- readRDS(filename)
}
```
```{r}
marker_genes <- c("Hbb-bs", "Epcam", "Foxj1", "Scgb1a1", "Scgb3a2", "Abca3", "Hopx", "Col1a1", "Dcn", "Lum", "Acta2", "Wnt2", "Wnt5a", "Lgr6", "Pdgfra", "Pdgfrb", "Cspg4", "Wt1", "Pecam1", "Ccl21a", "Vwf", "Nrg1", "Plvap", "Car4", "Mki67", "Tnnt2", "Myl7", "Ascl1", "Ptprc")
DotPlot(meso_sub, features = marker_genes) + theme(axis.text.x = element_text(angle = 45, hjust=1))
```
```{r}
marker_genes <- c("Wnt2", "Macf1", # Wnt2 fibroblasts 0
"Tgfbi", "Wnt5a", # Myofibroblasts 1
"Dcn", "Col1a1", # Adventitial fibroblast 2
"Top2a", "Mki67", # Prenatal Wnt2 3
"Cspg4", "Cox4i2", # Pericyte 4
"Ednrb", "Pdgfra", # Prenatal myo 5
"Wt1", "Upk3b", # Mesothelium 6
"Eln", "Acta2", # Smooth muscle 7
"Mpz", "Mal", # Neuron 8
"Tnnt2", "Actc1" # Cardiomyocyte
)
DotPlot(meso_sub, features = marker_genes) + theme(axis.text.x = element_text(angle = 45, hjust=1))
```
```{r, fig.width = 12}
p_cluster <- DimPlot(meso_sub, label = TRUE) + umap_theme() +
theme(aspect.ratio=1)
p_time <- DimPlot(meso_sub, group.by = "timepoint") + umap_theme() +
theme(aspect.ratio=1)
p_cluster + p_time + plot_annotation("Mesenchyme")
a <- 18
```
```{r}
meso_sub_reduced_res <- cluster_pca_umap(meso_sub, dims_umap = 1:15, dims_neighbors = 1:15, cluster_res = 0.6)
```
```{r, fig.width = 12}
p_cluster <- DimPlot(meso_sub_reduced_res, label = TRUE) + umap_theme() +
theme(aspect.ratio=1)
p_time <- DimPlot(meso_sub_reduced_res, group.by = "timepoint") + umap_theme() +
theme(aspect.ratio=1)
p_cluster + p_time + plot_annotation("Mesenchyme")
```
```{r}
meso_sub_subc <- FindSubCluster(meso_sub_reduced_res, 11, graph.name = "SCT_snn", subcluster.name = "sub.cluster", resolution = 0.1)
meso_sub_subc$sub.cluster <- as.factor(meso_sub_subc$sub.cluster)
levels(meso_sub_subc$sub.cluster) <- 1:length(levels(meso_sub_subc$sub.cluster))
Idents(meso_sub_subc) <- meso_sub_subc$sub.cluster
meso_sub_subc <- FindSubCluster(meso_sub_subc, 24, graph.name = "SCT_snn", subcluster.name = "sub.cluster", resolution = 0.1)
meso_sub_subc$sub.cluster <- as.factor(meso_sub_subc$sub.cluster)
levels(meso_sub_subc$sub.cluster) <- 1:length(levels(meso_sub_subc$sub.cluster))
Idents(meso_sub_subc) <- meso_sub_subc$sub.cluster
meso_sub_subc <- FindSubCluster(meso_sub_subc, 4, graph.name = "SCT_snn", subcluster.name = "sub.cluster", resolution = 0.1)
meso_sub_subc$sub.cluster <- as.factor(meso_sub_subc$sub.cluster)
levels(meso_sub_subc$sub.cluster) <- 1:length(levels(meso_sub_subc$sub.cluster))
Idents(meso_sub_subc) <- meso_sub_subc$sub.cluster
```
```{r, fig.width = 12}
p_cluster <- DimPlot(meso_sub_subc, label = TRUE, group.by = "sub.cluster") + umap_theme() +
theme(aspect.ratio=1)
p_time <- DimPlot(meso_sub_subc, group.by = "timepoint") + umap_theme() +
theme(aspect.ratio=1)
p_cluster + p_time + plot_annotation("Mesenchyme")
```
```{r}
marker_genes <- c("Wnt2", "Macf1", # Wnt2 fibroblasts 0
"Tgfbi", "Wnt5a", # Myofibroblasts 1
"Dcn", "Col1a1", # Adventitial fibroblast 2
"Top2a", "Mki67", # Prenatal Wnt2 3
"Cspg4", "Cox4i2", # Pericyte 4
"Ednrb", "Pdgfra", # Prenatal myo 5
"Wt1", "Upk3b", # Mesothelium 6
"Eln", "Acta2", # Smooth muscle 7
"Mpz", "Mal", # Neuron 8
"Tnnt2", "Actc1" # Cardiomyocyte
)
DotPlot(meso_sub_subc, features = marker_genes) + theme(axis.text.x = element_text(angle = 45, hjust=1))
```
```{r}
meso_relabel <- meso_sub_subc
meso_relabel$louvain <- as.factor(meso_sub_subc$sub.cluster)
meso_relabel$celltype <- as.factor(meso_sub_subc$sub.cluster)
# ?: 23, 31..eh, 35, 48
idxes <- levels(meso_relabel$celltype)
labels <- rep("Wnt2+ FB", length(levels(meso_relabel$celltype)))
labels[idxes %in% c(6, 7, 8, 10, 16, 21, 24, 26, 27, 28)] <- "Myofibroblast"
labels[idxes %in% c(2, 18)] <- "Prolif. Wnt2+ FB"
labels[idxes %in% c(12)] <- "Prolif. Myo FB"
labels[idxes %in% c(5, 20)] <- "Adventitial FB"
labels[idxes %in% c(3)] <- "Pericyte"
labels[idxes %in% c(19)] <- "Mesothelium"
labels[idxes %in% c(25)] <- "Smooth Muscle"
labels[idxes %in% c(29)] <- "Cardiomyocyte"
levels(meso_relabel$celltype) <- labels
```
```{r, fig.width = 12}
p_cluster <- DimPlot(meso_relabel, group.by = "celltype") + umap_theme() +
theme(aspect.ratio=1)
p_time <- DimPlot(meso_relabel, group.by = "timepoint") + umap_theme() +
theme(aspect.ratio=1)
p_cluster + p_time + plot_annotation("Mesenchyme")
```
```{r}
filename <- "./data/20221020_meso_labeled_v2.rds"
if (!file.exists(filename)) {
saveRDS(meso_relabel, filename, compress = "bzip2")
} else {
meso_relabel <- readRDS(filename)
}
```
```{r}
N_WORKERS <- 8
plan("multiprocess", workers = N_WORKERS)
filename <- "./data/20210802_meso_louvain_markers.rds"
if (!file.exists(filename)) {
meso_subcluster_markers <- parallelFindAllMarkers(meso_relabel)
names(meso_subcluster_markers) <- levels(Idents(meso_relabel))
saveRDS(meso_subcluster_markers, filename)
} else {
meso_subcluster_markers <- readRDS(filename)
}
wb_markers_names <- levels(Idents(meso_relabel))
wb_markers <- createWorkbook()
for (idx in seq_along(wb_markers_names)){
addWorksheet(wb_markers, wb_markers_names[idx])
writeData(wb_markers, wb_markers_names[idx], meso_subcluster_markers[[idx]], rowNames = TRUE)
}
saveWorkbook(wb_markers, file = "./data/20210802_meso_louvain_markers.xlsx", overwrite = TRUE)
## Markers by celltype
meso_relabel$celltype <- ordered(meso_relabel$celltype, c("Prolif. Wnt2+ FB",
"Wnt2+ FB",
"Prolif. Myo FB",
"Myofibroblast",
"Adventitial FB",
"Pericyte",
"Mesothelium",
"Smooth Muscle",
"Cardiomyocyte"
))
Idents(meso_relabel) <- meso_relabel$celltype
filename <- "./data/20210802_meso_markers_celltype.rds"
if (!file.exists(filename)) {
meso_celltype_markers <- parallelFindAllMarkers(meso_relabel)
names(meso_celltype_markers) <- levels(Idents(meso_relabel))
saveRDS(meso_celltype_markers, filename)
} else {
meso_celltype_markers <- readRDS(filename)
}
wb_markers_names <- levels(Idents(meso_relabel))
wb_markers <- createWorkbook()
for (idx in seq_along(wb_markers_names)){
addWorksheet(wb_markers, wb_markers_names[idx])
writeData(wb_markers, wb_markers_names[idx], meso_celltype_markers[[idx]], rowNames = TRUE)
}
saveWorkbook(wb_markers, file = "./data/20210802_meso_markers_celltype.xlsx", overwrite = TRUE)
```