change function names

satijalab · Jun 12, 2024 · c503f95 · c503f95
1 parent 8a2eb36
commit c503f95
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Showing 3 changed files with 27 additions and 20 deletions.
diff --git a/R/SEraster.R b/R/SEraster.R
@@ -67,7 +67,7 @@ createRasterizedObject <- function(input, out, name) {
   return(output)
 }
 
-#' rasterizeGeneExpression
+#' RunRasterizeGeneExpression
 #' 
 #' @description Function to rasterize feature x observation matrix in spatially-resolved 
 #' omics data represented as Seurat objects.
@@ -126,7 +126,7 @@ createRasterizedObject <- function(input, out, name) {
 #' @importFrom Matrix colSums
 #' 
 #' @export
-rasterizeGeneExpression <- function(
+RunRasterizeGeneExpression <- function(
   input, 
   assay_name = NULL, 
   image = NULL, 
@@ -219,7 +219,7 @@ rasterizeGeneExpression <- function(
   }
 }
 
-#' rasterizeCellType
+#' RunRasterizeCellType
 #' 
 #' @description Function to rasterize cell type labels in spatially-resolved 
 #' omics data represented as Seurat object class.
@@ -279,7 +279,7 @@ rasterizeGeneExpression <- function(
 #' 
 #' @export
 #' 
-rasterizeCellType <- function(
+RunRasterizeCellType <- function(
   input, 
   col_name, 
   assay_name = NULL, 
@@ -373,7 +373,7 @@ rasterizeCellType <- function(
   }
 }
 
-#' permutateByRotation
+#' RunPermutateByRotation
 #' 
 #' @description Function to permutate a given input Seurat object(s) 
 #' by rotating the x,y coordinates around the midrange point.
@@ -404,7 +404,7 @@ rasterizeCellType <- function(
 #' 
 #' @export
 #' 
-permutateByRotation <- function(input, n_perm = 1, verbose = FALSE) {
+RunPermutateByRotation <- function(input, n_perm = 1, verbose = FALSE) {
   angles <- seq(0, 360, by = 360/n_perm)[1:n_perm]
 
   if (verbose) {
@@ -428,6 +428,10 @@ permutateByRotation <- function(input, n_perm = 1, verbose = FALSE) {
       assay_name <- DefaultAssay(spe)
       pos_orig <- data.frame(GetTissueCoordinates(spe, scale = NULL))
       colnames(pos_orig) <- c("x", "y")
+      if("cell" %in% colnames(pos_orig)){
+        rownames(pos_orig) <- pos_orig$cell
+      }
+
       stopifnot("Column 1 and 2 of the spatialCoords slot should be named x and y, respectively." = colnames(pos_orig)[1:2] == c("x", "y"))
 
       lapply(angles, function(angle) {
@@ -452,6 +456,9 @@ permutateByRotation <- function(input, n_perm = 1, verbose = FALSE) {
     assay_name <- DefaultAssay(input)
     pos_orig <- GetTissueCoordinates(input, scale = NULL)
     colnames(pos_orig) <- c("x", "y")
+    if("cell" %in% colnames(pos_orig)){
+        rownames(pos_orig) <- pos_orig$cell
+    }
     stopifnot("Column 1 and 2 of the spatialCoords slot should be named x and y, respectively." = colnames(pos_orig)[1:2] == c("x", "y"))
 
     output_list <- list()

diff --git a/docs/SEraster.Rmd b/docs/SEraster.Rmd
@@ -76,7 +76,7 @@ For continuous variables such as gene expression or other molecular information,
 Let's try rasterizing the gene expression of the Xenium mouse brain dataset we loaded. 
 
 ```{r}
-rastGexp <- SeuratWrappers::rasterizeGeneExpression(xenium.obj, assay_name = "Xenium", resolution = 100)
+rastGexp <- RunRasterizeGeneExpression(xenium.obj, assay_name = "Xenium", resolution = 100)
 
 # check dimensions of spot by gene matrix after rasterization
 dim(rastGexp[['Xenium']])
@@ -116,7 +116,7 @@ ImageDimPlot(xenium.obj, group.by='seurat_clusters')
 ```
 
 ```{r}
-rastCt <- SeuratWrappers::rasterizeCellType(xenium.obj, col_name = "seurat_clusters", resolution = 200)
+rastCt <- RunRasterizeCellType(xenium.obj, col_name = "seurat_clusters", resolution = 200)
 
 # check the dimension of the cell-types-by-cells matrix after rasterizing cell-type labels
 dim(rastCt[['seurat_clusters']])
@@ -137,10 +137,10 @@ Let's see how the rasterized VisiumHD mouse brain dataset looks with various res
 
 ```{r}
 # rasterize at defined resolution
-rast2000 <- SeuratWrappers::rasterizeGeneExpression(visiumhd.obj, assay_name="Spatial.016um", resolution = 2000)
+rast2000 <- RunRasterizeGeneExpression(visiumhd.obj, assay_name="Spatial.016um", resolution = 2000)
 rast2000 <- NormalizeData(rast2000)
 
-rast1000 <- SeuratWrappers::rasterizeGeneExpression(visiumhd.obj, assay_name="Spatial.016um", resolution = 1000)
+rast1000 <- RunRasterizeGeneExpression(visiumhd.obj, assay_name="Spatial.016um", resolution = 1000)
 rast1000 <- NormalizeData(rast1000)
 
 # visualize
@@ -159,10 +159,10 @@ In addition to a single `Seurat` spatial object, `rasterizeGeneExpression` and `
 ```{r}
 # permutate
 DefaultAssay(xenium.obj) <- "Xenium"
-spe_list <- SeuratWrappers::permutateByRotation(xenium.obj, n_perm = 3)
+spe_list <- RunPermutateByRotation(xenium.obj, n_perm = 3)
 
 # rasterize permutated datasets at once
-out_list <- SeuratWrappers::rasterizeGeneExpression(spe_list, assay_name = "Xenium", resolution = 200)
+out_list <- RunRasterizeGeneExpression(spe_list, assay_name = "Xenium", resolution = 200)
 
 for (i in seq_along(out_list)) {
   # extract rotated angle

diff --git a/docs/SEraster.md b/docs/SEraster.md
@@ -1,6 +1,6 @@
 Getting Started With SEraster
 ================
-Compiled: May 30, 2024
+Compiled: June 07, 2024
 
 # Getting Started with SEraster
 
@@ -83,7 +83,7 @@ Let’s try rasterizing the gene expression of the Xenium mouse brain
 dataset we loaded.
 
 ``` r
-rastGexp <- SeuratWrappers::rasterizeGeneExpression(xenium.obj, assay_name = "Xenium", resolution = 100)
+rastGexp <- RunRasterizeGeneExpression(xenium.obj, assay_name = "Xenium", resolution = 100)
 
 # check dimensions of spot by gene matrix after rasterization
 dim(rastGexp[['Xenium']])
@@ -154,7 +154,7 @@ ImageDimPlot(xenium.obj, group.by='seurat_clusters')
 ![](SEraster_files/figure-gfm/unnamed-chunk-11-2.png)<!-- -->
 
 ``` r
-rastCt <- SeuratWrappers::rasterizeCellType(xenium.obj, col_name = "seurat_clusters", resolution = 200)
+rastCt <- RunRasterizeCellType(xenium.obj, col_name = "seurat_clusters", resolution = 200)
 
 # check the dimension of the cell-types-by-cells matrix after rasterizing cell-type labels
 dim(rastCt[['seurat_clusters']])
@@ -190,10 +190,10 @@ various resolutions using square pixels, which is specified by `shape`.
 
 ``` r
 # rasterize at defined resolution
-rast2000 <- SeuratWrappers::rasterizeGeneExpression(visiumhd.obj, assay_name="Spatial.016um", resolution = 2000)
+rast2000 <- RunRasterizeGeneExpression(visiumhd.obj, assay_name="Spatial.016um", resolution = 2000)
 rast2000 <- NormalizeData(rast2000)
 
-rast1000 <- SeuratWrappers::rasterizeGeneExpression(visiumhd.obj, assay_name="Spatial.016um", resolution = 1000)
+rast1000 <- RunRasterizeGeneExpression(visiumhd.obj, assay_name="Spatial.016um", resolution = 1000)
 rast1000 <- NormalizeData(rast1000)
 
 # visualize
@@ -235,10 +235,10 @@ vs. disease tissues.
 ``` r
 # permutate
 DefaultAssay(xenium.obj) <- "Xenium"
-spe_list <- SeuratWrappers::permutateByRotation(xenium.obj, n_perm = 3)
+spe_list <- RunPermutateByRotation(xenium.obj, n_perm = 3)
 
 # rasterize permutated datasets at once
-out_list <- SeuratWrappers::rasterizeGeneExpression(spe_list, assay_name = "Xenium", resolution = 200)
+out_list <- RunRasterizeGeneExpression(spe_list, assay_name = "Xenium", resolution = 200)
 
 for (i in seq_along(out_list)) {
   # extract rotated angle
@@ -288,7 +288,7 @@ sessionInfo()
     ## [13] hcabm40k.SeuratData_3.0.0       cbmc.SeuratData_3.1.4          
     ## [15] bonemarrowref.SeuratData_1.0.0  bmcite.SeuratData_0.3.0        
     ## [17] SeuratData_0.2.2.9001           SeuratWrappers_0.3.6           
-    ## [19] Seurat_5.1.0                    SeuratObject_5.0.2             
+    ## [19] Seurat_5.0.3.9922               SeuratObject_5.0.2             
     ## [21] sp_2.1-3                       
     ## 
     ## loaded via a namespace (and not attached):