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preProcessing.R
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preProcessing.R
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#' annotateGenes Annotate genes with genomic coordinates with reference to hg38 using Ensembl based annotation package
#'
#' @param mtx Count matrix with genes on row names (Ensemble or Symbol)
#'
#' @return Annotated matrix
#'
#' @examples
#' count_mtx_annot <- annotateGenes(count_mtx)
#' @export
annotateGenes <- function(mtx, organism = "human"){
library(dplyr)
if(organism == "human"){
edb <- EnsDB_Hsapiens_v86 #From EnsDb.Hsapiens.v86
}else{
edb <- EnsDb_Mmusculus_v79 #From EnsDb.Hsapiens.v86
}
chr <- 1:22
edb <- (edb[edb$seqnames %in% chr,c(1,2,3,6,7)])
edb$seqnames <- as.numeric(as.character(edb$seqnames))
if (any(rownames(mtx) %in% edb$gene_name)){
use_geneID <- "gene_name"
}else{
use_geneID <- "gene_id"
}
genes_inters <- intersect(rownames(mtx), edb[[use_geneID]])
mtx <- mtx[which(rownames(mtx) %in% genes_inters),]
edb <- edb[which(as.vector(edb[[use_geneID]]) %in% genes_inters),]
edb <- edb[!duplicated(edb$gene_name),]
edb <- edb[order(match(edb[[use_geneID]], rownames(mtx))),]
if(class(mtx)[1]=="dgCMatrix"){
mtx_annot <- cbind(edb, as.matrix(mtx))
}else{
mtx_annot <- cbind(edb, mtx)
}
return(mtx_annot)
}
#' preprocessingMtx Pre-processing steps: Cells with less than 200 genes and the genes expressed in less than 1% of cells are removed. Genes are annotated and sorted
#' according to genomic coordinates. Highly confident normal cells are sought in the matrix. Genes involved in the cell cycle pathway are removed. Log-Freeman–Tukey transformation to stabilize variance
#' and a polynomial dynamic linear modeling (DLM) to smooth out the outliers.
#'
#' @param count_mtx raw count matrix
#' @param ngenes_chr minimum number of genes per chromosome (optional)
#' @param perc_genes percentage of cells in which each gene is to be expressed (optional)
#' @param par_cores number of cores (optional)
#' @param SMOOTH Boolean value to perform smoothing (optional)
#' @param findConfident Boolean value to search for normal cells (default TRUE)
#' @param AdditionalGeneSets List of additional signatures to be used to search for normal cells (optional)
#' @param SCEVANsignatures Boolean value TRUE to use internal SCEVAN signatures for normal cells or FALSE to use only signatures specified in AdditionalGeneSets (default TRUE)
#'
#' @return
#' count_mtx_smooth processed and smoothed matrix
#' count_mtx_annot annotated matrix
#'
#' @examples
#' count_mtx_annot <- annotateGenes(count_mtx)
#' @export
preprocessingMtx <- function(count_mtx, sample, ngenes_chr=5, perc_genes=0.1, par_cores=20, findConfident = TRUE, AdditionalGeneSets = NULL, SCEVANsignatures = TRUE, organism = "human"){
set.seed(123)
print(paste(" raw data - genes: ", nrow(count_mtx), " cells: ", ncol(count_mtx), sep=""))
print("1) Filter: cells > 200 genes")
genes.raw <- apply(count_mtx, 2, function(x)(sum(x>0)))
if(sum(genes.raw> 200)==0) stop("none cells have more than 200 genes")
if(sum(genes.raw<100)>1){
count_mtx <- count_mtx[, -which(genes.raw< 200)]
print(paste("filtered out ", sum(genes.raw<=200), " cells past filtering ", ncol(count_mtx), " cells", sep=""))
}
der <- apply(count_mtx,1,function(x)(sum(x>0)))/ncol(count_mtx)
if( sum(der > perc_genes) > 7000){
print(paste0("2) Filter: genes > ", perc_genes*100, "% of cells"))
count_mtx <- count_mtx[which(der > perc_genes), ];
}else{
perc_genes <- perc_genes - 0.05
print("low data quality")
print(paste0("2) Filter: genes > ", perc_genes*100, "% of cells"))
count_mtx <- count_mtx[which(der > perc_genes), ];
}
print(paste(nrow(count_mtx)," genes past filtering", sep=""))
#norm_cell <- getConfidentNormalCells(count_mtx, par_cores = par_cores)
print("3) Annotations gene coordinates")
count_mtx_annot <- annotateGenes(count_mtx, organism)
count_mtx <- count_mtx_annot[,-c(1:5)]
rownames(count_mtx) <- count_mtx_annot$gene_name
if(findConfident){
norm_cell <- getConfidentNormalCells(count_mtx, sample, par_cores = par_cores, AdditionalGeneSets = AdditionalGeneSets, SCEVANsignatures = SCEVANsignatures, organism = organism)
}else{
norm_cell <- NULL
}
#rm(count_mtx)
count_mtx_annot <- count_mtx_annot[
with(count_mtx_annot, order(as.numeric(as.character(seqnames)), as.numeric(as.character(end))), decreasing = FALSE),
]
print(paste(nrow(count_mtx_annot)," genes annotated", sep=""))
print("4) Filter: genes involved in the cell cycle")
if(organism == "human"){
totChr <- 22
cellcycle <- reactome_cellcycle #From EnsDb.Hsapiens.v86
}else{
totChr <- 19
cellcycle <- reactome_cellcycle_Mmusculus #From EnsDb.Hsapiens.v86
}
HLAs <- count_mtx_annot$gene_name[grep("^HLA-", count_mtx_annot$gene_name)]
toRev <- which(count_mtx_annot$gene_name %in% c(as.vector(cellcycle), HLAs))
if(length(toRev)>0){
count_mtx_annot <- count_mtx_annot[-toRev, ]
}
print(paste(nrow(count_mtx_annot)," genes past filtering ", sep=""))
print(paste0("5) Filter: cells > ", ngenes_chr, "genes per chromosome "))
cellsFilt <- NULL
for(i in 6:ncol(count_mtx_annot)){
cellChr <- cbind(count_mtx_annot$seqnames, count_mtx_annot[,i])
cellChr <- cellChr[cellChr[,2]!=0,]
if(length(rle(cellChr[,1])$length)<totChr|min(rle(cellChr[,1])$length)< ngenes_chr){
cellsFilt <- c(cellsFilt, colnames(count_mtx_annot)[i])
}
}
if(length(cellsFilt)==(ncol(count_mtx_annot)-5)) stop("all cells are filtered")
if(length(cellsFilt)>0){
count_mtx_annot <-count_mtx_annot[, -which(colnames(count_mtx_annot) %in% cellsFilt)]
}
if((ncol(count_mtx_annot)-5)<15) stop("Bad sample low cells < 15")
print("6) Log Freeman Turkey transformation")
count_mtx_proc <- data.matrix(count_mtx_annot[, 6:ncol(count_mtx_annot)])
count_mtx_annot <- count_mtx_annot[, 1:5]
count_mtx_norm <- log(sqrt(count_mtx_proc)+sqrt(count_mtx_proc+1))
count_mtx_norm <- apply(count_mtx_norm,2,function(x)(x <- x-mean(x)))
colnames(count_mtx_norm) <- colnames(count_mtx_proc)
rm(count_mtx_proc)
print(paste("A total of ", ncol(count_mtx_norm), " cells, ", nrow(count_mtx_norm), " genes after preprocessing", sep=""))
rownames(count_mtx_norm) <- count_mtx_annot$gene_name
norm_cell <- norm_cell[names(norm_cell) %in% colnames(count_mtx_norm)]
res <- list(count_mtx_norm, count_mtx_annot, norm_cell, count_mtx)
names(res) <- c("count_mtx_norm", "count_mtx_annot", "norm_cell", "count_mtx")
return(res)
}