Merge pull request #189 from zhanghao-njmu/develop

Update the CellQC function

NAMESPACE

@@ -384,7 +384,6 @@ importFrom(ggplot2,ggplot_gtable)
 importFrom(ggplot2,ggsave)
 importFrom(ggplot2,ggtitle)
 importFrom(ggplot2,guide_colorbar)
-importFrom(ggplot2,guide_colourbar)
 importFrom(ggplot2,guide_legend)
 importFrom(ggplot2,guide_none)
 importFrom(ggplot2,guides)

R/SCP-cellqc.R

@@ -255,9 +255,7 @@ isOutlier <- function(x, nmads = 2.5, constant = 1.4826, type = c("both", "lower
 
 #' Run cell-level quality control for single cell RNA-seq data.
 #'
-#' In CellQC, doublet-calling will be run first and then reject abnormal cell data based on median absolute deviation (MAD) outliers.
-#' After doublet-calling and outlier filtering, CellQC will perform general QC (gene count, UMI count, etc.)
-#' and reject cell data for non-species of interest based on the proportion and number of UMIs for the species.
+#' This function handles multiple quality control methods for single-cell RNA-seq data.
 #'
 #' @inheritParams RunDoubletCalling
 #' @param batch Name of the batch variable to split the Seurat object. Default is NULL.
@@ -284,15 +282,6 @@ isOutlier <- function(x, nmads = 2.5, constant = 1.4826, type = c("both", "lower
 #'
 #' @return Returns Seurat object with the QC results stored in the meta.data slot.
 #'
-#' @note
-#' General quality control usually uses metrics such as gene count, UMI count, etc.
-#' In addition, ribo content and mito content can be used as QC indicators:
-#' mito content can be used as an indication of apoptosis,
-#' with a general threshold of 20% and less than 10% mito content in high quality cells;
-#' ribo content is cell type dependent, with some cell types having even more than 50% ribo content;
-#' however, ribo content in single cell data can also indicate whether the empty droplets,
-#' and the ribo content in empty droplets is usually high and the mito content is low instead.
-#'
 #' @examples
 #' data("pancreas_sub")
 #' pancreas_sub <- RunCellQC(pancreas_sub)
@@ -307,7 +296,7 @@ isOutlier <- function(x, nmads = 2.5, constant = 1.4826, type = c("both", "lower
 #' table(pancreas_sub$CellQC)
 #'
 #' data("ifnb_sub")
-#' ifnb_sub <- RunCellQC(ifnb_sub, batch = "stim", UMI_threshold = 1000, gene_threshold = 550)
+#' ifnb_sub <- RunCellQC(ifnb_sub, split.by = "stim", UMI_threshold = 1000, gene_threshold = 550)
 #' CellStatPlot(
 #'   srt = ifnb_sub,
 #'   stat.by = c(
@@ -322,7 +311,7 @@ isOutlier <- function(x, nmads = 2.5, constant = 1.4826, type = c("both", "lower
 #' @importFrom Matrix colSums t
 #' @export
 #'
-RunCellQC <- function(srt, assay = "RNA", batch = NULL,
+RunCellQC <- function(srt, assay = "RNA", split.by = NULL,
                       qc_metrics = c("doublets", "outlier", "umi", "gene", "mito", "ribo", "ribo_mito_ratio", "species"),
                       return_filtered = FALSE,
                       db_method = "scDblFinder", db_rate = NULL, db_coefficient = 0.01,
@@ -364,16 +353,16 @@ RunCellQC <- function(srt, assay = "RNA", batch = NULL,
     srt@meta.data[[paste0("nFeature_", assay)]] <- colSums(srt[[assay]]@counts > 0)
   }
   srt_raw <- srt
-  if (!is.null(batch)) {
-    srtList <- SplitObject(srt, split.by = batch)
+  if (!is.null(split.by)) {
+    srtList <- SplitObject(srt, split.by = split.by)
   } else {
     srtList <- list(srt)
   }
 
   for (i in seq_along(srtList)) {
     srt <- srtList[[i]]
-    if (!is.null(batch)) {
-      cat("===", srt@meta.data[[batch]][1], "===\n")
+    if (!is.null(split.by)) {
+      cat("===", srt@meta.data[[split.by]][1], "===\n")
     }
     ntotal <- ncol(srt)
 

README.Rmd

@@ -314,12 +314,12 @@ plist <- list()
 
 for (method in integration_methods) {
   panc8_sub <- Integration_SCP(
-    srtMerge = panc8_sub, batch = "tech",
+    srtMerge = panc8_sub, batch = "tech", linear_reduction_dims_use = 1:50,
     integration_method = method, nonlinear_reduction = "umap"
   )
   plist[[method]] <- CellDimPlot(panc8_sub,
     title = method, group.by = c("celltype"), reduction = paste0(method, "UMAP2D"), theme_use = "theme_blank",
-    show_stat = F, xlab = "UMAP_1", ylab = "UMAP_2", legend.position = "none"
+    xlab = "UMAP_1", ylab = "UMAP_2", legend.position = "none"
   )
 }
 p <- plot_grid(plotlist = plist, ncol = 3)
@@ -335,7 +335,11 @@ panel_fix(grob, height = 2)
 ### Cell projection between single-cell datasets
 
 ```{r RunKNNMap}
-panc8_rename <- RenameFeatures(srt = panc8_sub, newnames = make.unique(capitalize(rownames(panc8_sub), force_tolower = TRUE)), assays = "RNA")
+panc8_rename <- RenameFeatures(
+  srt = panc8_sub,
+  newnames = make.unique(capitalize(rownames(panc8_sub[["RNA"]]), force_tolower = TRUE)),
+  assays = "RNA"
+)
 srt_query <- RunKNNMap(srt_query = pancreas_sub, srt_ref = panc8_rename, ref_umap = "SeuratUMAP2D")
 ProjectionPlot(
   srt_query = srt_query, srt_ref = panc8_rename,
@@ -447,7 +451,7 @@ EnrichmentPlot(
 
 > To ensure that labels are visible, you can adjust the size of the viewer panel on Rstudio IDE.
 
-```{r Enrichment_enrichmap, fig.height=9.5,fig.width=15}
+```{r Enrichment_enrichmap, fig.height=9.5, fig.width=15}
 EnrichmentPlot(
   srt = pancreas_sub, group_by = "CellType", group_use = "Ductal",
   plot_type = "enrichmap"

README.md

@@ -380,7 +380,11 @@ UMAP embeddings based on different integration methods in SCP:
 ### Cell projection between single-cell datasets
 
 ``` r
-panc8_rename <- RenameFeatures(srt = panc8_sub, newnames = make.unique(capitalize(rownames(panc8_sub), force_tolower = TRUE)), assays = "RNA")
+panc8_rename <- RenameFeatures(
+  srt = panc8_sub,
+  newnames = make.unique(capitalize(rownames(panc8_sub[["RNA"]]), force_tolower = TRUE)),
+  assays = "RNA"
+)
 srt_query <- RunKNNMap(srt_query = pancreas_sub, srt_ref = panc8_rename, ref_umap = "SeuratUMAP2D")
 ProjectionPlot(
   srt_query = srt_query, srt_ref = panc8_rename,