Fixed bug that caused incompatibility with biobase due to having the same function name.
Added new profiling functions.
This commit is contained in:
MarijnBerg
parent
0427dcc6ad
commit
a620b4e5d2
114
R/FastCAR_Base.R
114
R/FastCAR_Base.R
|
|
@ -13,6 +13,8 @@
|
|||
library(Matrix)
|
||||
library(Seurat)
|
||||
library(qlcMatrix)
|
||||
library(pheatmap)
|
||||
library(ggplot2)
|
||||
|
||||
###############################################################################
|
||||
|
||||
|
|
@ -46,10 +48,10 @@ remove.background = function(geneCellMatrix, ambientRNAprofile){
|
|||
}
|
||||
##############
|
||||
|
||||
determine.background.to.remove = function(fullCellMatrix, cellMatrix, emptyDropletCutoff, contaminationChanceCutoff){
|
||||
determine.background.to.remove = function(fullCellMatrix, emptyDropletCutoff, contaminationChanceCutoff){
|
||||
|
||||
# determines the highest expression value found for every gene in the droplets that we're sure don't contain cells
|
||||
backGroundMax = as.vector(rowMax(fullCellMatrix[,Matrix::colSums(fullCellMatrix) < emptyDropletCutoff]))
|
||||
backGroundMax = as.vector(qlcMatrix::rowMax(fullCellMatrix[,Matrix::colSums(fullCellMatrix) < emptyDropletCutoff]))
|
||||
names(backGroundMax) = rownames(fullCellMatrix)
|
||||
|
||||
# droplets that are empty but not unused barcodes, unused barcodes have zero reads assigned to them.
|
||||
|
|
@ -79,6 +81,93 @@ read.full.matrix = function(fullFolderLocation){
|
|||
return(fullMatrix)
|
||||
}
|
||||
|
||||
###############################################################################
|
||||
getExpressionThreshold = function(gene, expMat, percentile){
|
||||
orderedExpression = expMat[gene, order(expMat[gene,], decreasing = TRUE)]
|
||||
CS = cumsum(orderedExpression)
|
||||
return(orderedExpression[which(CS/max(CS) > percentile)[1]])
|
||||
}
|
||||
|
||||
###############################################################################
|
||||
celltypeSpecificityScore = function(gene, expMat){
|
||||
CS = cumsum(expMat[gene, order(expMat[gene,], decreasing = TRUE)])
|
||||
return((sum(CS/max(CS))/ncol(expMat)) )
|
||||
}
|
||||
|
||||
###############################################################################
|
||||
describe.correction.effect = function (allExpression, cellExpression, startPos, stopPos, byLength, contaminationChanceCutoff){
|
||||
|
||||
# Make somewhere to store all the data that needs to be returned to the user
|
||||
ambientScoreProfileOverview = data.frame(row.names = rownames(cellExpression))
|
||||
|
||||
# do a quick first run to see which genes get corrected at the highest setting
|
||||
ambientProfile = determine.background.to.remove(allExpression, cellExpression, stopPos, contaminationChanceCutoff)
|
||||
genelist = names(ambientProfile[ambientProfile > 0])
|
||||
|
||||
print(paste0("Calculating cell expression score for ", length(genelist), " genes"))
|
||||
ctsScores = vector(mode = "numeric", length = nrow(cellExpression))
|
||||
names(ctsScores) = rownames(cellExpression)
|
||||
|
||||
for(gene in genelist){
|
||||
ctsScores[gene] = celltypeSpecificityScore(gene, cellExpression)
|
||||
}
|
||||
|
||||
# loop over every threshold to test
|
||||
# Starts at the highest value so
|
||||
for(emptyDropletCutoff in seq(from = startPos, to = stopPos, by = byLength)){
|
||||
ambientProfile = determine.background.to.remove(allExpression, cellExpression, emptyDropletCutoff, contaminationChanceCutoff)
|
||||
|
||||
print(paste0("Profiling at cutoff ", emptyDropletCutoff))
|
||||
|
||||
ambientScoreProfile = data.frame(ambientProfile, ctsScores)
|
||||
#ambientScoreProfile = ambientScoreProfile[ambientScoreProfile$ctsScores > 0.85, ]
|
||||
ambientScoreProfile$stillOverAmbient = 0
|
||||
ambientScoreProfile$belowCellexpression = 0
|
||||
|
||||
genesToCheck = names(ambientProfile[ambientProfile > 0])
|
||||
if(exists("overAmbientGenes")){
|
||||
genesToCheck = overAmbientGenes
|
||||
}
|
||||
|
||||
print(paste0("Calculating profiles for ", length(genesToCheck), " genes"))
|
||||
|
||||
for(gene in genesToCheck){
|
||||
expThresh = getExpressionThreshold(gene, cellExpression, 0.95)
|
||||
|
||||
if(emptyDropletCutoff == startPos){
|
||||
ambientScoreProfile[gene, "belowCellexpression"] = table(cellExpression[gene,] > 0 & cellExpression[gene,] < expThresh)["TRUE"]
|
||||
}
|
||||
ambientScoreProfile[gene, "stillOverAmbient"] = table(cellExpression[gene,] > ambientScoreProfile[gene, "ambientProfile"] & cellExpression[gene,] < expThresh)["TRUE"]
|
||||
}
|
||||
|
||||
ambientScoreProfile[is.na(ambientScoreProfile)] = 0
|
||||
ambientScoreProfile$contaminationChance = ambientScoreProfile$stillOverAmbient / ambientScoreProfile$belowCellexpression
|
||||
ambientScoreProfile[is.na(ambientScoreProfile)] = 0
|
||||
|
||||
# Genes that have already been completely removed don't need to be checked at higher resolution
|
||||
overAmbientGenes = rownames(ambientScoreProfile[ambientScoreProfile$stillOverAmbient > 0,])
|
||||
|
||||
ambientScoreProfile[genelist,"AmbientCorrection"]
|
||||
|
||||
ambientScoreProfileOverview[names(ctsScores), "ctsScores"] = ctsScores
|
||||
if(emptyDropletCutoff == startPos){
|
||||
ambientScoreProfileOverview[rownames(ambientScoreProfile), "belowCellexpression"] = ambientScoreProfile$belowCellexpression
|
||||
}
|
||||
ambientScoreProfileOverview[rownames(ambientScoreProfile), paste0("stillOverAmbient", as.character(emptyDropletCutoff))] = ambientScoreProfile$stillOverAmbient
|
||||
ambientScoreProfileOverview[rownames(ambientScoreProfile), paste0("AmbientCorrection", as.character(emptyDropletCutoff))] = ambientScoreProfile$ambientProfile
|
||||
}
|
||||
ambientScoreProfileOverview = ambientScoreProfileOverview[!is.na(ambientScoreProfileOverview$ctsScores),]
|
||||
ambientScoreProfileOverview[is.na(ambientScoreProfileOverview)] = 0
|
||||
|
||||
ambientScoreProfileOverview[,paste0("Threshold_", seq(from = startPos, to = stopPos, by = byLength))] = ambientScoreProfileOverview[,paste0("stillOverAmbient", as.character(seq(from = startPos, to = stopPos, by = byLength)))] / ambientScoreProfileOverview$belowCellexpression
|
||||
ambientScoreProfileOverview[is.na(ambientScoreProfileOverview)] = 0
|
||||
|
||||
ambientScoreProfileOverview[,paste0("contaminationChance", as.character(seq(from = startPos, to = stopPos, by = byLength)))] = ambientScoreProfileOverview[,paste0("stillOverAmbient", as.character(seq(from = startPos, to = stopPos, by = byLength)))] / ambientScoreProfileOverview$belowCellexpression
|
||||
|
||||
return(ambientScoreProfileOverview)
|
||||
}
|
||||
|
||||
|
||||
##############
|
||||
# Turns out that cellranger output looks different from WriteMM output and Read10X can't read the latter
|
||||
# TODO
|
||||
|
|
@ -123,14 +212,27 @@ describe.ambient.RNA.sequence = function(fullCellMatrix, start, stop, by, contam
|
|||
return(ambientDescriptions)
|
||||
}
|
||||
|
||||
plot.correction.effect.chance = function(correctionProfile){
|
||||
pheatmap(correctionProfile[correctionProfile[,3] > 0, colnames(correctionProfile)[grep("contaminationChance", colnames(correctionProfile))]],
|
||||
cluster_cols = FALSE,
|
||||
treeheight_row = 0,
|
||||
main = "Chance of affecting DE analyses")
|
||||
}
|
||||
|
||||
plot.correction.effect.removal = function(correctionProfile){
|
||||
pheatmap(correctionProfile[(correctionProfile[,3] > 0) ,colnames(correctionProfile)[grep("AmbientCorrection", colnames(correctionProfile))]],
|
||||
cluster_cols = FALSE, treeheight_row = 0,
|
||||
main = "Counts removed from each cell")
|
||||
}
|
||||
|
||||
|
||||
plot.ambient.profile = function(ambientProfile){
|
||||
par(mfrow = c(3,1))
|
||||
plot(as.numeric(rownames(ambientProfile)), ambientProfile[,1],
|
||||
main = "Total number of empty droplets at cutoffs",
|
||||
xlab = "empty droplet UMI cutoff",
|
||||
ylab = "Number of empty droplets")
|
||||
ggplot(ambientProfile, aes(x=wt, y=genesInBackground)) + geom_point()
|
||||
|
||||
genesInBackground = vector(mode = "numeric", length = length(seq(start, stop, by)))
|
||||
genesContaminating = vector(mode = "numeric", length = length(seq(start, stop, by)))
|
||||
nEmptyDroplets = vector(mode = "numeric", length = length(seq(start, stop, by)))
|
||||
|
||||
plot(as.numeric(rownames(ambientProfile)), ambientProfile[,2],
|
||||
main = "Number of genes in ambient RNA",
|
||||
|
|
|
|||
Loading…
Reference in New Issue