Commiting just the actual package code

master
Marijn 3 years ago
commit cd967fa821

@ -0,0 +1,11 @@
Package: FastCAR
Type: Package
Title: What the Package Does (Title Case)
Version: 0.1.0
Author: Who wrote it
Maintainer: The package maintainer <yourself@somewhere.net>
Description: More about what it does (maybe more than one line)
Use four spaces when indenting paragraphs within the Description.
License: What license is it under?
Encoding: UTF-8
LazyData: true

@ -0,0 +1,143 @@
###############################################################################
# FastCAR package
# Marijn Berg
# m.berg@umcg.nl
###############################################################################
# FastCAR removes ambient RNA from 10X data by looking at the profile of the
# empty droplets and removing per gene the highest value found in the ambient
# RNA if it's found to contaminate more than a certain threshold of droplets
###############################################################################
# This file contains base functions
###############################################################################
library(Matrix)
library(Seurat)
###############################################################################
# had to make this function to efficiently modify sparse matrices on a per gene basis
# A dgCMatrix object has the following elements that matter for this function
# i: a sequence of the row locations of each non-zero element
# x: the non-zero values in the matrix
# p: the where in 'i' and 'x' a new column starts
# Dimnames: The names of the rows and columns
remove.background = function(geneCellMatrix, ambientRNAprofile){
for(gene in names(ambientProfile[ambientProfile > 0])){
# Determine the locations where the gene is not zero, therefore referenced in i
iLocs = which(geneCellMatrix@Dimnames[[1]] == gene)
# Determine the location of the actual values,
xLocs = which(geneCellMatrix@i == iLocs-1) # -1 because of 0 and 1 based counting systems
# Remove the contaminating RNA
geneCellMatrix@x[xLocs] = geneCellMatrix@x[xLocs] - ambientProfile[gene]
}
# correct for instances where the expression was corrected to below zero
geneCellMatrix@x[geneCellMatrix@x < 0] = 0
# remove the zeroes and return the corrected matrix
return(drop0(geneCellMatrix, is.Csparse = TRUE))
}
##############
determine.background.to.remove = function(fullCellMatrix, cellMatrix, 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]))
names(backGroundMax) = rownames(fullCellMatrix)
nCell = ncol(cellMatrix)
# droplets that are empty but not unused barcodes, unused barcodes have zero reads assigned to them.
nEmpty = table((Matrix::colSums(fullCellMatrix) < emptyDropletCutoff) &(Matrix::colSums(fullCellMatrix) > 0))[2]
# rowSum on a logical statement returns the number of TRUE occurences
occurences = rowSums(fullCellMatrix[,Matrix::colSums(fullCellMatrix) < emptyDropletCutoff] !=0)
#probability of a background read of a gene ending up in a cell
probabiltyCellContaminationPerGene = occurences / nEmpty
# if the probablity of a gene contaminating a cell is too low we set the value to zero so it doesn't get corrected
backGroundMax[probabiltyCellContaminationPerGene < contaminationChanceCutoff] = 0
return(backGroundMax)
}
##############
read.cell.matrix = function(cellFolderLocation){
cellMatrix = Read10X(cellFolderLocation)
return(cellMatrix)
}
##############
read.full.matrix = function(fullFolderLocation){
fullMatrix = Read10X(fullFolderLocation)
return(fullMatrix)
}
##############
write.corrected.matrix = function(correctedMatrix, folderLocation, correctedSignal){
dir.create(folderLocation)
writeMM(obj = correctedMatrix, file=paste(folderLocation, "/matrix.mtx", sep = ""))
# save genes and cells names
write(x = rownames(correctedMatrix), file = paste(folderLocation, "/genes.tsv", sep = ""))
write(x = colnames(correctedMatrix), file = paste(folderLocation, "/barcodes.tsv", sep = ""))
correctedSignal = correctedSignal[correctedSignal > 0]
write.table(list(correctedSignal), file = paste(folderLocation, "/genesCorrectedFor.csv", sep = ""), row.names = TRUE, col.names = FALSE)
}
# describe the number of genes identified in the background
# and the number of genes failing the contaminiation chance threshold
#
describe.ambient.RNA.sequence = function(fullCellMatrix, start, stop, by, contaminationChanceCutoff){
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)))
ambientDescriptions = data.frame(nEmptyDroplets, genesInBackground, genesContaminating)
rownames(ambientDescriptions) = seq(start, stop, by)
for(emptyCutoff in seq(start, stop, by)){
nEmpty = table((Matrix::colSums(fullCellMatrix) < emptyCutoff) &(Matrix::colSums(fullCellMatrix) > 0))[2]
occurences = rowSums(fullCellMatrix[,Matrix::colSums(fullCellMatrix) < emptyCutoff] !=0)
#probability of a background read of a gene ending up in a cell
probabiltyCellContaminationPerGene = occurences / nEmpty
nFailingThreshold = sum(probabiltyCellContaminationPerGene > contaminationChanceCutoff)
nGenes = sum(occurences != 0)
ambientDescriptions[as.character(emptyCutoff), c(1,2,3)] = c(nEmpty ,nGenes, nFailingThreshold)
}
return(ambientDescriptions)
}
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")
plot(as.numeric(rownames(ambientProfile)), ambientProfile[,2],
main = "Number of genes in ambient RNA",
xlab = "empty droplet UMI cutoff",
ylab = "Genes in empty droplets")
plot(as.numeric(rownames(ambientProfile)), ambientProfile[,3],
main = "number of genes to correct",
xlab = "empty droplet UMI cutoff",
ylab = "Genes identified as contamination")
}

@ -0,0 +1,41 @@
###############################################################################
# FastCAR package
# Marijn Berg
# m.berg@umcg.nl
###############################################################################
# FastCAR removes ambient RNA from 10X data by looking at the profile of the
# empty droplets and removing per gene the highest value found in the ambient
# RNA if it's found to contaminate more than a certain threshold of droplets
###############################################################################
# This script runs a simple correction with standard settings
library(Matrix)
library(Seurat)
library(qlcMatrix)
source("R/FastCAR_Base.R")
emptyDropletCutoff = 100 # Droplets with fewer than this number of UMIs are considered empty
contaminationChanceCutoff = 0.05 # This is the maximum fraction of droplets containing the contamination
cellExpressionFolder = c("/home/marijn/Analysis_Drive/R_Projects/scRNA_Background_correction/Cellranger_output/4951STDY7487591_ARMS054/filtered_feature_bc_matrix/")
fullMatrixFolder = c("/home/marijn/Analysis_Drive/R_Projects/scRNA_Background_correction/Cellranger_output/4951STDY7487591_ARMS054/raw_feature_bc_matrix/")
# This folder will contain the corrected cell matrix
correctedMatrixFolder = c("/home/marijn/Analysis_Drive/R_Projects/scRNA_Background_correction/Cellranger_output/4951STDY7487591_ARMS054/corrected_feature_bc_matrix")
# these are literally wrappers for the Seurat functions but it's good practice in case the function changes later
cellMatrix = read.cell.matrix(cellExpressionFolder)
fullMatrix = read.full.matrix(fullMatrixFolder)
###############################################################################
# This is an optional function that will show the effects of different cutoff values
# start, stop, and by all refer to number of UMI, it determines the background ate steps of by, from start to stop
ambProfile = describe.ambient.RNA.sequence(fullCellMatrix = fullMatrix, start = 10, stop = 500, by = 10, contaminationChanceCutoff = 0.05)
plot.ambient.profile(ambProfile)
###############################################################################
ambientProfile = determine.background.to.remove(fullMatrix, cellMatrix, emptyDropletCutoff, contaminationChanceCutoff)
cellMatrix = remove.background(cellMatrix, ambientProfile)
write.corrected.matrix(cellMatrix, correctedMatrixFolder, ambientProfile)

@ -0,0 +1,65 @@
###############################################################################
# FastCAR package
# Marijn Berg
# m.berg@umcg.nl
###############################################################################
# FastCAR removes ambient RNA from 10X data by looking at the profile of the
# empty droplets and removing per gene the highest value found in the ambient
# RNA if it's found to contaminate more than a certain threshold of droplets
###############################################################################
# This script contains functions to profile the Ambient RNA to suggest
# good settings to use to find genes to correct for
###############################################################################
library(Matrix)
library(Seurat)
library(qlcMatrix)
###############################################################################
# describe the number of genes identified in the background
# and the number of genes failing the contaminiation chance threshold
#
describe.ambient.RNA.sequence = function(fullCellMatrix, start, stop, by, contaminationChanceCutoff){
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)))
ambientDescriptions = data.frame(nEmptyDroplets, genesInBackground, genesContaminating)
rownames(ambientDescriptions) = seq(start, stop, by)
for(emptyCutoff in seq(start, stop, by)){
nEmpty = table((Matrix::colSums(fullCellMatrix) < emptyCutoff) &(Matrix::colSums(fullCellMatrix) > 0))[2]
occurences = rowSums(fullCellMatrix[,Matrix::colSums(fullCellMatrix) < emptyCutoff] !=0)
#probability of a background read of a gene ending up in a cell
probabiltyCellContaminationPerGene = occurences / nEmpty
nFailingThreshold = sum(probabiltyCellContaminationPerGene > contaminationChanceCutoff)
nGenes = sum(occurences != 0)
ambientDescriptions[as.character(emptyCutoff), c(1,2,3)] = c(nEmpty ,nGenes, nFailingThreshold)
}
return(ambientDescriptions)
}
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")
plot(as.numeric(rownames(ambientProfile)), ambientProfile[,2],
main = "Number of genes in ambient RNA",
xlab = "empty droplet UMI cutoff",
ylab = "Genes in empty droplets")
plot(as.numeric(rownames(ambientProfile)), ambientProfile[,3],
main = "number of genes to correct",
xlab = "empty droplet UMI cutoff",
ylab = "Genes identified as contamination")
}

@ -0,0 +1,102 @@
cutoffEmpty = 100 # set this to NA to determine dynamicallly
###############################################################################
# remove this or things will keep getting added to it
rm(biopsySeurat, backGroundOverviews)
for(folder in dataFolders[1]){
gc()
donor = str_sub(folder, -7,-1)
sample = str_sub(folder, 19)
sample = str_sub(sample, 1, -9)
allExpression = Read10X(paste(folder, "/raw_feature_bc_matrix", sep = ""))
colnames(allExpression) = paste(datasetInfo[sample, "prefix"], colnames(allExpression), sep = "")
# change barcode ids to match r object
if(is.na(cutoffEmpty)){
##########################################
# determine the number of genes in the empty droplets at various cutoffs
foundNumberOfGenes = vector(mode = "numeric", length = length(seq(kneePointStart, kneePointStop, kneePointSteps)))
names(foundNumberOfGenes) = as.character(seq(kneePointStart, kneePointStop, kneePointSteps))
for(cutoffValue in seq(kneePointStart, kneePointStop, kneePointSteps)){
nExpressedCells = Matrix::rowSums(allExpression[, Matrix::colSums(allExpression) < cutoffValue])
nExpressedCells[nExpressedCells > 0] = 1
foundNumberOfGenes[as.character(cutoffValue)]= sum(nExpressedCells)
}
write.table(foundNumberOfGenes, file = paste("Foundgenes_cutoff_", donor, ".csv"))
##########################################
# Find the point where increase in the number of genes decreases the most
cutoffEmpty = which(diff(foundNumberOfGenes) == max(diff(foundNumberOfGenes)[find_peaks(diff(foundNumberOfGenes))]))
# cutoffEmpty = as.numeric(names(which(diff(foundNumberOfGenes) == min(diff(foundNumberOfGenes)))))[1] # have to add this at the end, multiple changes can be the same value
##########################################
png(paste("Empty_droplets_content_", donor, ".png", sep = ""), height = 400, width = 800)
plot(as.numeric(names(foundNumberOfGenes)), foundNumberOfGenes, main = paste(donor, " unique genes per cutoff"), xlab = "cutoff <")
segments(cutoffEmpty, 0 ,cutoffEmpty, 25000)
dev.off()
}
# read this in as a full matrix, haven't figgered out how to do this on a sparse matrix
cellExpression = allExpression[,cellIDs[cellIDs %in% colnames(allExpression)]]
if(!exists("biopsySeurat")){
biopsySeurat <<- CreateSeuratObject(cellExpression, project = "Bronchial_Biopsy")
biopsySeurat[["orig.ident"]] = donor
biopsySeurat[["percent.mt"]] <- PercentageFeatureSet(biopsySeurat, pattern = "^MT-")
# biopsySeurat <- subset(biopsySeurat, subset = percent.mt < quantile(biopsySeurat@meta.data$percent.mt, c(.9)) )
}else{
tmpbiopsySeurat = CreateSeuratObject(cellExpression, project = "Bronchial_Biopsy")
tmpbiopsySeurat[["orig.ident"]] = donor
tmpbiopsySeurat[["percent.mt"]] <- PercentageFeatureSet(tmpbiopsySeurat, pattern = "^MT-")
# tmpbiopsySeurat <- subset(tmpbiopsySeurat, subset = percent.mt < quantile(tmpbiopsySeurat@meta.data$percent.mt, c(.9), na.rm = TRUE))
biopsySeurat = merge(biopsySeurat, tmpbiopsySeurat)
}
backgroundTotal = Matrix::rowSums(allExpression[,Matrix::colSums(allExpression) < cutoffEmpty])
backGroundMax = as.vector(rowMax(allExpression[names(backgroundTotal),Matrix::colSums(allExpression) < cutoffEmpty]))
nCell = ncol(cellExpression)
# droplets that are empty but not unused barcodes, unused barcodes have zero reads assigned to them.
nEmpty = table((Matrix::colSums(allExpression) < cutoffEmpty) &(Matrix::colSums(allExpression) > 0))[2]
occurences = rowSums(allExpression[,Matrix::colSums(allExpression) < cutoffEmpty] !=0)
#probability of a background read of a gene ending up in a cell
pCell = occurences / nEmpty
write.csv(pCell, file = paste("pCell_", donor, ".csv", sep = ""))
write.csv(backGroundMax, file = paste("bgMax_", donor, ".csv", sep = ""))
# set the background value for those genes that are unlikely to be a significant contaminant to 0 (zero)
backGroundMax[pCell < acceptableFractionContaminated] = 0
# remove the background
cellExpression = apply(cellExpression , 2, '-', backGroundMax)
cellExpression[cellExpression < 0] = 0
# cellExpression = as.sparse(cellExpression)
if(!exists("biopsySeuratAC")){
biopsySeuratAC <<- CreateSeuratObject(cellExpression, project = "AC_Bronchial_Biopsy")
biopsySeuratAC[["orig.ident"]] = donor
biopsySeuratAC[["percent.mt"]] <- PercentageFeatureSet(biopsySeuratAC, pattern = "^MT-")
# biopsySeuratAC <- subset(biopsySeuratAC, subset = percent.mt < quantile(biopsySeuratAC@meta.data$percent.mt, c(.9)) )
}else{
tmpbiopsySeuratAC = CreateSeuratObject(cellExpression, project = "AC_Bronchial_Biopsy")
tmpbiopsySeuratAC[["orig.ident"]] = donor
tmpbiopsySeuratAC[["percent.mt"]] <- PercentageFeatureSet(tmpbiopsySeuratAC, pattern = "^MT-")
# tmpbiopsySeuratAC <- subset(tmpbiopsySeuratAC, subset = percent.mt < quantile(tmpbiopsySeuratAC@meta.data$percent.mt, c(.9), na.rm = TRUE))
biopsySeuratAC = merge(biopsySeuratAC, tmpbiopsySeuratAC)
}
}

@ -0,0 +1,12 @@
\name{hello}
\alias{hello}
\title{Hello, World!}
\usage{
hello()
}
\description{
Prints 'Hello, world!'.
}
\examples{
hello()
}
Loading…
Cancel
Save