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benchmark.R
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benchmark.R
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# This file includes functions in the data preprocessing of the benchmarking process.
# TODO: document the functions
options(stringsAsFactors = FALSE)
set.seed(24)
require(limma); require(dplyr); require(pheatmap); require(Matrix); require(sparseMatrixStats)
# Added parameters: sampleCT = FALSE, propsample = TRUE, pct.var=30
# Pseudo bulk generator
Generator <- function(sce, phenoData, sampleCT = FALSE, propsample = TRUE, pct.var=30, Num.mixtures = 1000,
pool.size = 100, min.percentage = 1, max.percentage = 99, seed = 24, mode = 2){
CT = unique(phenoData$cellType)
?stopifnot(length(CT) >= 2)
set.seed(seed)
require(dplyr)
require(gtools)
cell.distribution = data.frame(table(phenoData$cellType),stringsAsFactors = FALSE)
colnames(cell.distribution) = c("CT","max.n")
Tissues = list()
Proportions = list()
if(mode == 1){
for(y in 1:Num.mixtures){
#Only allow feasible mixtures based on cell distribution
while(!exists("P")){
if(sampleCT){
num.CT.mixture = sample(x = 3:length(CT),1) # more than 3 cell types to fit a curve.
#num.CT.mixture = sample(x = round(length(CT)*0.5):length(CT),1)
selected.CT = sample(CT, num.CT.mixture, replace = FALSE)
}else{
num.CT.mixture = length(CT)
selected.CT = CT
}
if(propsample){
x = round(runif(num.CT.mixture, 100-pct.var, 100+pct.var))/100.0
P = cell.distribution[selected.CT,]$max.n*x
}else{
P = runif(num.CT.mixture, min.percentage, max.percentage)
}
P = round(P/sum(P), digits = log10(pool.size)) #sum to 1
P = data.frame(CT = selected.CT, expected = P, stringsAsFactors = FALSE)
missing.CT = CT[!CT %in% selected.CT]
missing.CT = data.frame(CT = missing.CT, expected = rep(0, length(missing.CT)), stringsAsFactors = FALSE)
P = rbind.data.frame(P, missing.CT)
potential.mix = merge(P, cell.distribution)
potential.mix$size = potential.mix$expected * pool.size
# if( !all(potential.mix$max.n >= potential.mix$size) | sum(P$expected) != 1){
if(sum(P$expected) != 1){
rm(list="P")
}
}
# Using info in P to build T simultaneously
chosen_cells <- sapply(which(P$expected != 0), function(x){
n.cells = P$expected[x] * pool.size
# P$n[x] = n.cells #
# chosen = sample(phenoData$cellID[phenoData$cellType == P$CT[x]],
# n.cells)
chosen = sample(phenoData$cellID[phenoData$cellType == P$CT[x]],
n.cells, replace = TRUE)
chosen
}) %>% unlist()
T <- Matrix::rowSums(sce[,as.vector(chosen_cells)]) %>% as.data.frame()
# T <- Matrix::rowSums(sce[,colnames(sce) %in% chosen_cells]) %>% as.data.frame()
colnames(T) = paste("mix",y,sep="")
P = P[,c("CT","expected")]
P$mix = paste("mix",y,sep="")
Tissues[[y]] <- T
Proportions[[y]] <- P
rm(list=c("T","P","chosen_cells","missing.CT"))
}
}
else if(mode ==2){
for(y in 1:Num.mixtures){
chosen_cells = sample(phenoData$cellID, pool.size, replace = TRUE)
T <- Matrix::rowSums(sce[,chosen_cells]) %>% as.data.frame()
P1 = phenoData$cellType
names(P1)<-phenoData$cellID
P = data.frame(table(P1[chosen_cells]),stringsAsFactors = FALSE)
colnames(P) = c("CT","expected")
colnames(T) = paste("mix",y,sep="")
P$mix = paste("mix",y,sep="")
Tissues[[y]] <- T
Proportions[[y]] <- P
}
}
P = do.call(rbind.data.frame, Proportions)
T = do.call(cbind.data.frame, Tissues)
P = data.table::dcast(P, CT ~ mix,
value.var = "expected",
fun.aggregate = sum) %>% data.frame(.,row.names = 1)
P = P[,gtools::mixedsort(colnames(P))]
return(list(T = T, P = P))
}
Transformation <- function(matrix, option){
#############################################################
########## DATA TRANSFORMATION (on full data) ##########
if(option=="none"){
matrix = matrix
}else if(option=="log"){
matrix = log1p(matrix)
}else if(option=="sqrt"){
matrix = sqrt(matrix)
}else if(option=="vst"){
#DEBUG# This function does not work for sparse matrix and may cause memory issue.
matrix = DESeq2::varianceStabilizingTransformation(as.matrix(matrix))
# matrix = DESeq2::varianceStabilizingTransformation(matrix)
}
return(matrix)
}
Scaling <- function(matrix, option, phenoDataC=NULL){
########## Remove rows & columns full of zeroes ##########
#Error: NMF::nmf - Input matrix x contains at least one null or NA-filled row.
matrix = matrix[rowSums(matrix)!=0,]
#OLS with error if all elements within a row are equal (e.g. all 0, or all a common value after log/sqrt/vst transformation)
matrix = matrix[!apply(matrix, 1, function(x) var(x) == 0),]
matrix = matrix[,colSums(matrix)!=0]
if(option=="column"){
matrix = apply(matrix,2,function(x) x/sum(x))
} else if(option=="row"){
matrix = t(apply(matrix,1,function(x) x/sum(x)))
} else if(option=="mean"){
matrix = apply(matrix,2,function(x) x - mean(x))
} else if(option=="column_z-score"){
matrix = scale(matrix, center = TRUE, scale = TRUE)
} else if(option=="global_z-score"){
#DEBUG# global_z-score desifies sparseMatrix and may cause memory issue.
# matrix = (matrix - mean(as.matrix(matrix))) / sd(as.matrix(matrix))
matrix = (matrix - mean(matrix)) / sd(matrix)
} else if(option=="column_min-max"){
matrix = apply(matrix, 2, function(x) (x - min(x))/(max(x) - min(x)))
} else if(option=="global_min-max"){
matrix = (matrix - min(matrix))/(max(matrix) - min(matrix))
} else if (option=="LogNormalize"){
#DEBUG# Seurat::LogNormalize desifies sparseMatrix and may cause memory issue.
#matrix = as.matrix(expm1(Seurat::LogNormalize(matrix, display.progress = FALSE))) #for v2.1
matrix = as.matrix(expm1(Seurat::LogNormalize(matrix, verbose = FALSE))) #for v3
} else if (option=="QN"){
#DEBUG# QN requires matrix format input and may cause memory issue.
matrix_rownames <- rownames(matrix); matrix_colnames <- colnames(matrix)
matrix = preprocessCore::normalize.quantiles(as.matrix(matrix))
rownames(matrix) <- matrix_rownames; colnames(matrix) <- matrix_colnames
} else if (option=="TMM"){# CPM counts coming from TMM-normalized library sizes; https://support.bioconductor.org/p/114798/
if(!is.null(phenoDataC)){#use CT info for scRNA-seq
Celltype = as.character(phenoDataC$cellType[phenoDataC$cellID %in% colnames(matrix)])
} else {
Celltype = colnames(matrix)
}
matrix <- edgeR::DGEList(counts=matrix, group=Celltype)
CtrlGenes <- grep("ERCC-",rownames(data))
if(length(CtrlGenes)>1){
spikes <- data[CtrlGenes,]
spikes <- edgeR::calcNormFactors(spikes, method = "TMM")
matrix$samples$norm.factors <- spikes$samples$norm.factors
} else {
matrix <- edgeR::calcNormFactors(matrix, method = "TMM")
}
matrix <- edgeR::cpm(matrix)
} else if (option=="UQ"){
if(!is.null(phenoDataC)){#use CT info for scRNA-seq
Celltype = as.character(phenoDataC$cellType[phenoDataC$cellID %in% colnames(matrix)])
} else {
Celltype = colnames(matrix)
}
matrix <- edgeR::DGEList(counts=matrix, group=Celltype)
CtrlGenes <- grep("ERCC-",rownames(data))
if(length(CtrlGenes)>1){
spikes <- data[CtrlGenes,]
spikes <- edgeR::calcNormFactors(spikes, method = "upperquartile")
matrix$samples$norm.factors <- spikes$samples$norm.factors
} else {
matrix <- edgeR::calcNormFactors(matrix, method = "upperquartile")
}
matrix <- edgeR::cpm(matrix)
} else if (option=="median_ratios"){#requires integer values
if(!is.null(phenoDataC)){#use CT info for scRNA-seq
Celltype = as.character(phenoDataC$cellType[phenoDataC$cellID %in% colnames(matrix)])
} else {
Celltype = colnames(matrix)
}
metadata <- data.frame(Celltype=Celltype)
CtrlGenes <- grep("ERCC-",rownames(matrix))
matrix = DESeq2::DESeqDataSetFromMatrix(matrix, colData = metadata, design = ~ Celltype)
if(length(CtrlGenes)>1 & sum(rowSums(DESeq2::counts(matrix[CtrlGenes,]) != 0) >= 0.5*(ncol(matrix))) >= 2){
dds <- DESeq2::estimateSizeFactors(matrix, type = "ratio", controlGenes = CtrlGenes)
} else {
dds <- DESeq2::estimateSizeFactors(matrix, type = "ratio")
}
matrix <- DESeq2::counts(dds, normalized=TRUE)
} else if (option=="TPM"){
require(SingleR)
data(human_lengths)
# Doesn't work with Ensembl IDs:
if(length(grep("ENSG000",rownames(matrix))) > 100){
suppressMessages(library("AnnotationDbi"))
suppressMessages(library("org.Hs.eg.db"))
temp = mapIds(org.Hs.eg.db,keys=names(human_lengths),column="ENSEMBL",keytype="SYMBOL",multiVals="first")
names(human_lengths) = as.character(temp)
}
matrix = SingleR::TPM(counts = matrix, lengths = human_lengths)
rownames(matrix) = toupper(rownames(matrix))
detach(package:SingleR, unload=TRUE)
####################################################################################
## scRNA-seq specific
} else if (option=="SCTransform"){# SCTransform = RNBR
#Model formula is y ~ log_umi
##following line needed to solve "Wrong R type for mapped matrix"
#https://github.com/ChristophH/sctransform/issues/24
matrix = as(matrix, "dgCMatrix")
# matrix = sctransform::vst(matrix, return_corrected_umi=TRUE, show_progress = FALSE)$umi_corrected
matrix = sctransform::vst(matrix, return_corrected_umi=TRUE, verbosity = FALSE)$umi_corrected
# matrix = as(matrix, "matrix")
} else if (option=="scran"){
# sf = scran::computeSumFactors(as.matrix(matrix), clusters=NULL)
# sce = SingleCellExperiment::SingleCellExperiment(assays = list(counts=as.matrix(matrix)))
# sizeFactors(sce) <-sf
# sce = scater::normalize(sce,exprs_values = "counts", return_log = FALSE)
# matrix = normcounts(sce)
sce = SingleCellExperiment::SingleCellExperiment(assays = list(counts=matrix))
sce <- scran::computeSumFactors(sce, clusters=NULL)
sce <- scater::logNormCounts(sce, log=FALSE)
matrix = sce@assays@data$normcounts
} else if (option=="scater"){
size_factors = scater::librarySizeFactors(matrix)
# matrix <- scater::normalizeCounts(as.matrix(matrix), size_factors = size_factors, return_log = FALSE)
matrix <- scater::normalizeCounts(matrix, size_factors = size_factors)
} else if (option=="Linnorm"){
#DEBUG# 1. It is not compatible with log transformed datasets.
#DEBUG# 2. Linnorm will densify the matrix and may cause memory issue.
# matrix = expm1(Linnorm::Linnorm(as.matrix(matrix))) #Main function contains log1p(datamatrix)
matrix = expm1(Linnorm::Linnorm(matrix))
}
return(matrix)
}
########################################################################
read_data<-function(dataset){
ad = readRDS(dataset)
if (class(ad)== "Seurat") {
# print("input of class SeuratObject")
data = ad@assays$RNA@counts
pData = [email protected]
} else if (class(ad) =="ExpressionSet"){
# print("input of class ExpressionSet")
data = ad@assayData$exprs
pData = ad@phenoData@data
} else {
print("the format of the input data is neither Seurat nor ExpressionSet")
}
rownames(pData)<-NULL
original_cell_names = colnames(data)
colnames(data) <- as.character(pData$cellType[match(colnames(data),pData$cellID)])
cell_counts = table(colnames(data))
return(list('data'=data, 'pData'=pData, 'original_cell_names'=original_cell_names, 'cell_counts'=cell_counts))
}
# Read bulk and pseudobulk data
read_bulk<-function(dataset){
data = readRDS(dataset)
if (class(data)== "list") {
return(data)
} else{
return(list('data'=data))
}
}
QC<-function(X){
# filter features
keep <- which(Matrix::rowSums(data > 0) >= 3)
X$data = X$data[keep,]
# Keep CTs with >= 4 cells after QC
to_keep = names(X$cell_counts)[X$cell_counts >= 4]
X$pData <- X$pData[X$pData$cellType %in% to_keep,]
to_keep = which(colnames(X$data) %in% to_keep)
X$data <- X$data[,to_keep]
X$original_cell_names <- X$original_cell_names[to_keep]
return(X)
}
ref_mat<-function(train){
cellType <- colnames(train)
group = list()
for(i in unique(cellType)){
group[[i]] <- which(cellType %in% i)
}
#C should be made with the mean (not sum) to agree with the way markers were selected
C = lapply(group,function(x) Matrix::rowMeans(train[,x]))
C = round(do.call(cbind.data.frame, C))
refProfiles.var = lapply(group,function(x) train[,x])
# refProfiles.var = lapply(refProfiles.var, function(x) matrixStats::rowSds(Matrix::as.matrix(x)))
refProfiles.var = lapply(refProfiles.var, function(x) sparseMatrixStats::rowSds(x))
refProfiles.var = round(do.call(cbind.data.frame, refProfiles.var))
rownames(refProfiles.var) <- rownames(train)
return(list('C'=C, 'ref'=refProfiles.var))
}
# This function is used in the `marker_selection` function.
NormalizationTMM <- function(data){
data <- edgeR::DGEList(data)
CtrlGenes <- grep("ERCC-",rownames(data))
if(length(CtrlGenes)>1){
spikes <- data[CtrlGenes,]
spikes <- edgeR::calcNormFactors(spikes, method = "TMM")
data$samples$norm.factors <- spikes$samples$norm.factors
} else {
data$samples$lib.size[data$samples$lib.size<1] = 100
data <- edgeR::calcNormFactors(data, method = "TMM")
}
return(data)
}
marker.fc <- function(fit2, cont.matrix, log2.threshold = 1, output_name = "markers"){
topTable_RESULTS = limma::topTable(fit2, coef = 1:ncol(cont.matrix), number = Inf, adjust.method = "BH", p.value = 0.05, lfc = log2.threshold)
if(dim(topTable_RESULTS)[1]<1){
topTable_RESULTS = limma::topTable(fit2, coef = 1:ncol(cont.matrix), number = Inf, adjust.method = "BH",
p.value = 0.05, lfc = 0.1)
log2.threshold = 0.1
}
AveExpr_pval <- topTable_RESULTS[,(ncol(topTable_RESULTS)-3):ncol(topTable_RESULTS)]
topTable_RESULTS <- topTable_RESULTS[,1:(ncol(topTable_RESULTS)-4)]
if(length(grep("ERCC-",topTable_RESULTS$gene)) > 0){ topTable_RESULTS <- topTable_RESULTS[-grep("ERCC-",topTable_RESULTS$gene),] }
markers <- apply(topTable_RESULTS,1,function(x){
temp = sort(x)
((temp[ncol(topTable_RESULTS)] - temp[ncol(topTable_RESULTS)-1]) >= log2.threshold) | (abs(temp[1] - temp[2]) >= log2.threshold)
})
topTable_RESULTS = topTable_RESULTS[markers,]
markers <- cbind.data.frame(rownames(topTable_RESULTS),
t(apply(topTable_RESULTS, 1, function(x){
temp = max(x)
if(temp < log2.threshold){
temp = c(min(x),colnames(topTable_RESULTS)[which.min(x)])
} else {
temp = c(max(x),colnames(topTable_RESULTS)[which.max(x)])
}
temp
})))
colnames(markers) <- c("gene","log2FC","CT")
markers$log2FC = as.numeric(as.character(markers$log2FC))
markers <- markers %>% dplyr::arrange(CT,desc(log2FC))
markers$AveExpr <- AveExpr_pval$AveExpr[match(markers$gene,rownames(AveExpr_pval))]
markers$gene <- as.character(markers$gene)
markers$CT <- as.character(markers$CT)
#write.table(markers, file = output_name, row.names = FALSE, col.names = TRUE, sep = "\t", quote = FALSE)
return(markers)
}
marker_strategies <- function(marker_distrib, marker_strategy, C){
set.seed(4)
if(marker_strategy == "all"){
#using all markers that were found
markers = marker_distrib
} else if (marker_strategy == "pos_fc"){
# using only markers with positive FC (=over-expressed in cell type of interest)
markers = marker_distrib %>% dplyr::filter(log2FC > 0) %>% as.data.frame()
} else if (marker_strategy == "top_50p_logFC"){
# top 50% of markers (per CT) based on logFC
markers = marker_distrib %>% dplyr::filter(log2FC > 0) %>% dplyr::arrange(CT, desc(log2FC)) %>% group_by(CT) %>% dplyr::top_n(ceiling(n()*0.5), wt=log2FC) %>% as.data.frame()
} else if (marker_strategy == "bottom_50p_logFC"){
# bottom 50% of markers based on logFC
markers = marker_distrib %>% dplyr::filter(log2FC > 0) %>% dplyr::arrange(CT, desc(log2FC)) %>% group_by(CT) %>% dplyr::top_n(floor(n()*-0.5), wt=log2FC) %>% as.data.frame()
} else if (marker_strategy == "top_50p_AveExpr"){
# top 50% of markers based on average gene expression (baseline expression)
markers = marker_distrib %>% dplyr::filter(log2FC > 0) %>% dplyr::arrange(CT, desc(AveExpr)) %>% group_by(CT) %>% dplyr::top_n(ceiling(n()*0.5), wt=log2FC) %>% as.data.frame()
} else if (marker_strategy == "bottom_50p_AveExpr"){
# low 50% based on average gene expression.
markers = marker_distrib %>% dplyr::filter(log2FC > 0) %>% dplyr::arrange(CT, desc(AveExpr)) %>% group_by(CT) %>% dplyr::top_n(floor(n()*-0.5), wt=log2FC) %>% as.data.frame()
} else if (marker_strategy == "top_n2"){
# using the top 2 genes/CT with highest log2FC
markers = marker_distrib %>% dplyr::filter(log2FC > 0) %>% dplyr::arrange(CT, desc(log2FC)) %>% group_by(CT) %>% dplyr::top_n(2, wt=log2FC) %>% as.data.frame()
} else if (marker_strategy == "random5"){
# using 5 random markers for each different cell types
markers = marker_distrib[1:(ncol(C)*5),]
markers$CT = rep(colnames(C),5) #labelling purposes: important for semi-supervised
markers$gene = sample(rownames(C), nrow(markers), replace = FALSE)
}
return(markers)
}
marker_selection<-function(train){
#-------------------------------------------------------
#Normalization of "train" followed by marker selection
#for marker selection, keep genes where at least 30% of cells within a cell type have a read/UMI count different from 0
cellType = colnames(train)
keep <- sapply(unique(cellType), function(x) {
CT_hits = which(cellType %in% x)
size = ceiling(0.3*length(CT_hits))
Matrix::rowSums(train[,CT_hits,drop=FALSE] != 0) >= size
})
train = train[Matrix::rowSums(keep) > 0,]
train2 = NormalizationTMM(train)
# INITIAL CONTRASTS for marker selection WITHOUT taking correlated CT into account
#[compare one group with average expression of all other groups]
annotation = factor(colnames(train2))
design <- model.matrix(~0+annotation)
colnames(design) <- unlist(lapply(strsplit(colnames(design),"annotation"), function(x) x[2]))
cont.matrix <- matrix((-1/ncol(design)),nrow=ncol(design),ncol=ncol(design))
colnames(cont.matrix) <- colnames(design)
diag(cont.matrix) <- (ncol(design)-1)/ncol(design)
v <- limma::voom(train2, design=design, plot=FALSE)
fit <- limma::lmFit(v, design)
fit2 <- limma::contrasts.fit(fit, cont.matrix)
fit2 <- limma::eBayes(fit2, trend=TRUE)
markers = marker.fc(fit2, cont.matrix=cont.matrix, log2.threshold = log2(2))
return(markers)
}
prepare_train<-function(train, train_cell_names){
# Generate phenodata for reference matrix C
train_cellID = train
colnames(train_cellID) = train_cell_names
# reference matrix (C) + refProfiles.var from TRAINING dataset
cellType <- colnames(train)
group = list()
for(i in unique(cellType)){
group[[i]] <- which(cellType %in% i)
}
#C should be made with the mean (not sum) to agree with the way markers were selected
C = lapply(group, function(x) if(class(train[,x])=='numeric') train[,x] else Matrix::rowMeans(train[,x]) )
C = round(do.call(cbind.data.frame, C))
refProfiles.var = lapply(group,function(x) train[,x])
# refProfiles.var = lapply(refProfiles.var, function(x) matrixStats::rowSds(Matrix::as.matrix(x)))
# refProfiles.var = lapply(refProfiles.var, function(x) sparseMatrixStats::rowSds(x))
refProfiles.var = lapply(refProfiles.var, function(x) if(class(x)=='numeric') x else sparseMatrixStats::rowSds(x))
refProfiles.var = round(do.call(cbind.data.frame, refProfiles.var))
rownames(refProfiles.var) <- rownames(train)
markers = marker_selection(train)
return(list('train_cellID'=train_cellID, 'C'=C, 'ref' = refProfiles.var, 'markers'=markers))
}