P278949/FastCAR
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Swapped base R plots for ggplot

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MarijnBerg 2021-11-01 14:42:49 +01:00
parent a620b4e5d2
commit 55b27c57a6
1 changed files with 12 additions and 15 deletions
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27
R/FastCAR_Base.R
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@ -15,6 +15,7 @@ library(Seurat)
library(qlcMatrix)
library(pheatmap)
library(ggplot2)
library(gridExtra)
###############################################################################
@ -188,14 +189,15 @@ describe.correction.effect = function (allExpression, cellExpression, startPos,
# }
# describe the number of genes identified in the background
# and the number of genes failing the contaminiation chance threshold
# and the number of genes failing the contamination chance threshold
#
describe.ambient.RNA.sequence = function(fullCellMatrix, start, stop, by, contaminationChanceCutoff){
cutoffValue = seq(start, stop, by)
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)
ambientDescriptions = data.frame(nEmptyDroplets, genesInBackground, genesContaminating, cutoffValue)
rownames(ambientDescriptions) = seq(start, stop, by)
for(emptyCutoff in seq(start, stop, by)){
nEmpty = table((Matrix::colSums(fullCellMatrix) < emptyCutoff) &(Matrix::colSums(fullCellMatrix) > 0))[2]
@ -212,6 +214,7 @@ 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,
@ -227,22 +230,16 @@ plot.correction.effect.removal = function(correctionProfile){
plot.ambient.profile = function(ambientProfile){
par(mfrow = c(3,1))
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)))
p1 = ggplot(ambientProfile, aes(x=cutoffValue, y=genesInBackground)) + geom_point()
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")
p2= ggplot(ambientProfile, aes(x=cutoffValue, y=genesContaminating)) + geom_point()
p3 = ggplot(ambientProfile, aes(x=cutoffValue, y=nEmptyDroplets)) + geom_point()
grid.arrange(p1, p2, p3, nrow = 3)
}
# I noticed that the number of genes removed tends to even out over time