Hierarchical annotation of immune cells in scRNA-Seq data based on ssGSEA algorithm. Fork for large datasets with QOL improvements.
byarielm.fyi
1#' @title scImmuCC_main
2#' @description Calculation of gene enrichment scores and annotate cell type
3#' @details Input takes a count, genelist and filename, returns a data frame
4#' with cell annotation
5#' @param count Matrix with cell unique barcodes as column names and gene
6#' names as row names
7#' @param genematrix Data frame with cell types as column names
8#' @param ssGSEA_result Data frame, scImmuCC return result
9#' @param filename Custom file name, char
10#' @return Data frame with cell annotation
11#' @import GSVA
12#' @importFrom GSVA gsva
13
14scImmuCC_main <- function(count, genematrix, filename) {
15 genelist <- as.list(genematrix)
16 genelist <- lapply(genelist, function(x) x[!is.na(x)])
17
18 ssgsea <- ssgseaParam(
19 count,
20 genelist
21 )
22
23 ssgsea_score <- gsva(ssgsea)
24 score <- t(ssgsea_score)
25
26 barcodes <- c()
27 celltype <- c()
28 for (i in 1:length(score[, 1])) {
29 a1 <- score[i, ]
30 b1 <- as.data.frame(a1)
31 b1$celltype <- rownames(b1)
32 f <- b1[which(b1[, 1] == max(b1[, 1])), ]
33 barcodes <- c(barcodes, rownames(score)[i])
34 celltype <- c(celltype, rownames(f))
35 }
36
37 ssGSEA_result <- data.frame(barcodes = barcodes, cell_type = celltype)
38 head(ssGSEA_result)
39 write.csv(ssGSEA_result, paste(filename, "_scImmuCC_label.csv", sep = ""))
40}