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dropseq_seurat_splitDEMs_Plots.R
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dropseq_seurat_splitDEMs_Plots.R
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#!/usr/local/bin/Rscript
#------------------------------------------------------------------------------
# DROPSEQ Analysis to accompany:
#
# Bacon et al, 2018
# Single-Cell Analysis Identifies Thymic Maturation Delay in Growth-Restricted
# Neonatal Mice
#
# Link to publication
# TO ADD ONCE AVAILABLE
#
# Script available from:
# https://github.com/CTR-BFX/2018_Bacon_Charnock-Jones
#
# CTR Code: CTR_DROPSEQ_0009
#
# Analysis Performed by Russell S. Hamilton
# CTR Bioinformatics
# Centre for Trophoblast Reseach, University of Cambridge, UK
# Copyright Russell S. Hamilton ([email protected])
#
#------------------------------------------------------------------------------
# License Information
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, version 3.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#------------------------------------------------------------------------------
#
# message("+--- Uncomment if packages need installing ---+")
#
#install.packages("devtools")
#library("devtools")
#curl -O http://r.research.att.com/libs/gfortran-4.8.2-darwin13.tar.bz2
#sudo tar fvxz gfortran-4.8.2-darwin13.tar.bz2 -C /
#install_github("satijalab/seurat")
#install.packages("dplyr", type = "source")
#devtools::install_github("lazappi/clustree", build_vignettes = TRUE)
message("+--- Loading in the libraries (start up messages supressed) ---+")
suppressPackageStartupMessages({
library("dplyr")
library("methods")
library("utils")
library("ggplot2")
library("ggraph")
library("cowplot")
library("Seurat")
library("Matrix")
library("useful")
library("reshape2")
library("biomaRt")
library("scran")
library("scater")
library("viridis")
library("clustree")
library("SingleCellExperiment")
library("pheatmap")
library("tidyr")
library("Cairo")
})
genecut <- 300
mincells <- 3
#resolution <- "0.6"
resolution <- "0.4_to_1.0"
slxID <- "SLX-7632"
normalisation <- "log2"
perplexity <- 50
methodRun <- "m1";
regression <- "umi_mt_cc"
message(paste0("Experiment = ", slxID))
message(paste0("Gene Cut = ", genecut))
message(paste0("Min Cells = ", mincells))
message(paste0("Perplexity = ", perplexity))
message(paste0("Resolution = ", resolution))
message(paste0("Normalisation = ", normalisation))
message(paste0("Method Run = ", methodRun))
message(paste0("Regression type = ", regression))
baseDir <- "/Users/rhamilto/Documents/CTR-DROPSEQ/CTR_DropSeq_0009/SplitTests"
setwd(baseDir)
message("+--- Set up some useful functions --+")
centroidFunction <- function(tSNEClusterTable){
return (data.frame( x=mean(tSNEClusterTable$tSNE_1),y=mean(tSNEClusterTable$tSNE_2))) }
meanCentroidDist <- function(tSNEClusterTable, centroid){
median <- median( apply(tSNEClusterTable,1,function(x,centroid)
{(sqrt((tSNEClusterTable$tSNE_1 - centroid[1])^2+(tSNEClusterTable$tSNE_2-centroid[2])^2))},centroid) )
return( round(median, 3) ) }
message("+--- Read in the Robj file ---+")
if( (methodRun == "m1") & (regression == "umi_mt_cc")){
load(paste0("SLX-7632.matrix.su.WTvar.genecut.300.res.", resolution, ".reg.umi_mt_cc.robj"), verbose=TRUE)
load(paste0("SLX-7632.matrix.WTvar.tsne.genecut.300.res.", resolution, ".reg.umi_mt_cc.robj"), verbose=TRUE)
matrix.su <- matrix.su.WTvar
matrix.tsne <- matrix.WTvar.tsne
unlink("matrix.su.WTvar")
unlink("matrix.tsneWTvar")
} else {
message("ERROR: No matching expt details (methodRun and/or regression")
}
message("+--- Set up some colour palets for consistency ---+")
gg_color_hue <- function(n) {
hues = seq(15, 375, length = n + 1)
hcl(h = hues, l = 65, c = 100)[1:n]
}
cluster_col_range = gg_color_hue(8)
experiment_col_range = gg_color_hue(2)
message("+---------------------------------------------------------------------------------+")
message("+--- Summary of genes / cells in the matrix ---+")
message("+---------------------------------------------------------------------------------+")
[email protected]$Experiment <- gsub("\\.[0-9]$", "", rownames([email protected]))
[email protected]$Experiment <- gsub("..{12}$","", [email protected]$Experiment)
[email protected]$Experiment <- gsub("SLX......","", [email protected]$Experiment)
[email protected]$Nextera <- [email protected]$Experiment
[email protected]$Experiment <- gsub("N701|N704|N705|N706","WT", [email protected]$Experiment)
[email protected]$Experiment <- gsub("N702|N703|N707", "KO", [email protected]$Experiment)
numrows <- nrow(matrix.su@data)
numcells <- ncol(matrix.su@data)
message( paste("+--- Number of cells=", numcells, " Number of genes=", numrows, " ---+", sep=""))
if(numcells < 100){ print("+--- Below 100 cells after filtering ---+"); return(numcells)}
message("+--- nUMI Summary (ALL) ---+")
summary( [email protected]$nUMI )
message("+--- nUMI Summary (WT) ---+")
summary( subset([email protected], Experiment == "WT")$nUMI )
message("+--- nUMI Summary (KO) ---+")
summary( subset([email protected], Experiment == "KO")$nUMI )
message("+--- nGene Summary (All) ---+")
summary( [email protected]$nGene )
message("+--- nGene Summary (WT) ---+")
summary( subset([email protected], Experiment == "WT")$nGene )
message("+--- nGene Summary (KO) ---+")
summary( subset([email protected], Experiment == "KO")$nGene )
table([email protected]$Experiment, [email protected]$Nextera)
table([email protected]$res.0.6, [email protected]$Nextera )
table([email protected]$res.0.6, [email protected]$Experiment )
# Reads Per Cell ALL
summary( colSums([email protected][, rownames([email protected]) ]) )
# Reads Per Cell WT
summary( colSums([email protected][, rownames(subset([email protected], Experiment == "WT")) ]) )
# Reads Per Cell KO
summary( colSums([email protected][, rownames(subset([email protected], Experiment == "KO")) ]) )
files <- list.files(path=paste0(baseDir, "/UMIStats"), pattern="*star_gene_exon_tagged.gatheredBarcodeDist.gene_umi_summary.txt", full.names=T, recursive=FALSE)
message(paste0("nextera", ",", "sum.umi", ",", "mean.umi", ",", "sum.umi.uniq", ",", "mean.umi.uniq", ",", "nrow(umi_summary)"))
lapply(files, function(x) {
nextera <- gsub(".*SLX.7632.*.N7", "N7", x)
nextera <- gsub(".star.*","", nextera)
umi_summary <- read.table(x, header=T)
rownames(umi_summary) <- gsub("^", paste0("SLX.7632.", nextera, "."), umi_summary$CellBarcodeSeq )
umi_summary <- umi_summary[rownames([email protected]), ]
umi_summary <- umi_summary %>% drop_na()
sum.umi <- Reduce('+', umi_summary$UMI)
mean.umi <- sum.umi/nrow(umi_summary)
sum.umi.uniq <- Reduce('+', umi_summary$UMIuniq)
mean.umi.uniq <- sum.umi.uniq/nrow(umi_summary)
message(paste0(nextera, ",", sum.umi, ",", mean.umi, ",", sum.umi.uniq, ",", mean.umi.uniq, ",", nrow(umi_summary)))
})
message("+---------------------------------------------------------------------------------+")
message("+--- Multi-resolution Plots (Supp Figure 3) ---+")
message("+---------------------------------------------------------------------------------+")
counter = 1
str(counter)
tSNE.plot.list <- list()
for(res in c( 0.2, 0.4, 0.6, 0.8, 1.0 ))
{
message(paste0("Creating tsne at ", res, " resolution ", counter ))
matrix.tsne <- as.data.frame(GetCellEmbeddings(object=matrix.su, reduction.type="tsne", dims.use=1:2))
matrix.tsne$Experiment <- gsub("\\.[0-9]$", "", rownames(matrix.tsne))
matrix.tsne$Experiment <- gsub("..{12}$","", matrix.tsne$Experiment)
matrix.tsne$Experiment <- gsub("SLX......","", matrix.tsne$Experiment)
matrix.tsne$Experiment <- gsub("N701|N704|N705|N706","WT", matrix.tsne$Experiment)
matrix.tsne$Experiment <- gsub("N702|N703|N707", "KO", matrix.tsne$Experiment)
reso <- paste0("res.", res)
clust <- [email protected] %>% dplyr::select(contains(reso))
colnames(clust) <- c("Cluster")
matrix.tsne$Cluster <- clust$Cluster
tsne.XX <- ggplot(matrix.tsne, aes(x=tSNE_1, y=tSNE_2, colour=Cluster)) +
geom_point(alpha=0.5, size=1) +
ggtitle(paste0("Resolution ", res )) +
xlab("") + ylab("")
clusters <- levels(factor(matrix.tsne$Cluster))
for(i in clusters){
centroid <- centroidFunction(matrix.tsne[matrix.tsne$Cluster == i,])
tsne.XX <- tsne.XX + annotate("text", x = centroid$x, y = centroid$y, label = paste0(i),size=8, alpha=0.6)
}
tsne.XX <- tsne.XX + coord_fixed() +
theme(text=element_text(size=16, family="sans"),
legend.position="none", line = element_blank(),
axis.text.x=element_blank(), axis.text.y=element_blank())
tSNE.plot.list[[counter]] <- tsne.XX
counter <- counter + 1
}
ctree <- clustree(matrix.su)
pdf(paste0("T-Cell.Figure.Supp6.pdf") ,width=10,height=20, onefile=FALSE)
par(bg=NA)
plot_grid(tSNE.plot.list[[1]], tSNE.plot.list[[2]], tSNE.plot.list[[3]], tSNE.plot.list[[4]], tSNE.plot.list[[5]], ctree, ncol=2, nrow=3)
dev.off()
matrix.su.sub <- matrix.su
matrix.su.sub <- FindClusters(object=matrix.su.sub, reduction.type = "pca", resolution = 0.3, print.output = FALSE)
[email protected] <- [email protected][ , -which(names([email protected]) %in% c("res.0.3", "res.0.8","res.1"))]
names([email protected])
message("+---------------------------------------------------------------------------------+")
message("+---0.6 resolution Plots ---+")
message("+---------------------------------------------------------------------------------+")
matrix.su <- FindClusters(object=matrix.su, reduction.type = "pca", resolution = 0.6, print.output = FALSE, force.recalc=TRUE)
matrix.su <- RunTSNE(matrix.su, dims.use = 1:10, do.fast = T)
matrix.tsne <- as.data.frame(GetCellEmbeddings(object=matrix.su, reduction.type="tsne", dims.use=1:2))
matrix.tsne$Experiment <- gsub("\\.[0-9]$", "", rownames(matrix.tsne))
matrix.tsne$Experiment <- gsub("..{12}$","", matrix.tsne$Experiment)
matrix.tsne$Experiment <- gsub("SLX......","", matrix.tsne$Experiment)
matrix.tsne$Experiment <- gsub("N701|N704|N705|N706","WT", matrix.tsne$Experiment)
matrix.tsne$Experiment <- gsub("N702|N703|N707", "KO", matrix.tsne$Experiment)
reso <- paste0("res.0.6")
clust <- [email protected] %>% dplyr::select(contains(reso))
colnames(clust) <- c("Cluster")
matrix.tsne$Cluster <- clust$Cluster
cellcycle <- [email protected] %>% dplyr::select(contains("Cell_cycle"))
colnames(cellcycle) <- c("Cell_cycle")
matrix.tsne$Cell_cycle <- cellcycle$Cell_cycle
matrix.tsne$Cluster <- gsub("^", "x", matrix.tsne$Cluster)
matrix.tsne$Cluster <- gsub("x4", "1", matrix.tsne$Cluster)
matrix.tsne$Cluster <- gsub("x0", "2", matrix.tsne$Cluster)
matrix.tsne$Cluster <- gsub("x1", "3", matrix.tsne$Cluster)
matrix.tsne$Cluster <- gsub("x3", "4", matrix.tsne$Cluster)
matrix.tsne$Cluster <- gsub("x5", "5", matrix.tsne$Cluster)
matrix.tsne$Cluster <- gsub("x2", "6", matrix.tsne$Cluster)
matrix.tsne$Cluster <- gsub("x7", "7", matrix.tsne$Cluster)
matrix.tsne$Cluster <- gsub("x6", "8", matrix.tsne$Cluster)
matrix.tsne$Experiment <- gsub("KO", "P0", matrix.tsne$Experiment)
matrix.tsne.csv <- matrix.tsne
matrix.tsne.csv$Nextera <- gsub("SLX.7632.", "", rownames(matrix.tsne.csv) )
matrix.tsne.csv$Nextera <- gsub("..{12}$","", matrix.tsne.csv$Nextera)
matrix.tsne.csv <- within(matrix.tsne.csv, rm("tSNE_1", "tSNE_2"))
write.csv(matrix.tsne.csv, file = paste0(slxID, ".", "Table.CellsByNexteraCellcycleExperiment.", regression, ".", methodRun, ".csv"))
table(matrix.tsne.csv$Experiment,matrix.tsne.csv$Nextera)
table(matrix.tsne.csv$Cell_cycle,matrix.tsne.csv$Nextera)
table(matrix.tsne.csv$Cluster,matrix.tsne.csv$Nextera)
message("+--- Explore WT vs P0 Differential Expression ---+")
matrix.su.WTKO <- matrix.su
# Apply paper cluster numbering
[email protected]$res.0.6 <- gsub("^", "x", [email protected]$res.0.6)
[email protected]$res.0.6 <- gsub("x4", "1", [email protected]$res.0.6)
[email protected]$res.0.6 <- gsub("x0", "2", [email protected]$res.0.6)
[email protected]$res.0.6 <- gsub("x1", "3", [email protected]$res.0.6)
[email protected]$res.0.6 <- gsub("x3", "4", [email protected]$res.0.6)
[email protected]$res.0.6 <- gsub("x5", "5", [email protected]$res.0.6)
[email protected]$res.0.6 <- gsub("x2", "6", [email protected]$res.0.6)
[email protected]$res.0.6 <- gsub("x7", "7", [email protected]$res.0.6)
[email protected]$res.0.6 <- gsub("x6", "8", [email protected]$res.0.6)
# Cluster Cell Assignments
[email protected]$cell_type <- [email protected]$res.0.6
[email protected]$cell_type <- gsub("1", "DN", [email protected]$cell_type)
[email protected]$cell_type <- gsub("2", "DP", [email protected]$cell_type)
[email protected]$cell_type <- gsub("3", "DP", [email protected]$cell_type)
[email protected]$cell_type <- gsub("4", "DP", [email protected]$cell_type)
[email protected]$cell_type <- gsub("5", "DP", [email protected]$cell_type)
[email protected]$cell_type <- gsub("6", "TMat", [email protected]$cell_type)
[email protected]$cell_type <- gsub("7", "Macrophage", [email protected]$cell_type)
[email protected]$cell_type <- gsub("8", "RBC", [email protected]$cell_type)
[email protected]$cluster_experiment <- paste0([email protected]$Experiment, "_", [email protected]$res.0.6 )
[email protected]$cell_type_experiment <- paste0([email protected]$Experiment, "_", [email protected]$cell_type )
head([email protected])
matrix.su.WTKO <- SetAllIdent(matrix.su.WTKO, id = "cluster_experiment")
sig_cut <- 0.01
logfc_cut <- 0.5
topN <- 10
functionPlotDEVolcanoWTP0 <- function(matrix, clusterID, clustA.lab, clustB.lab, sig_cut, logfc_cut, topN) {
clust.A.B <- FindMarkers(matrix, ident.1 = clustA.lab, ident.2 = clustB.lab, print.bar = TRUE)
clust.A.B <- clust.A.B[order(clust.A.B$avg_logFC, decreasing=TRUE),]
clust.A.labP0.corrected <- gsub("KO", "P0", clustA.lab)
clust.B.labP0.corrected <- gsub("KO", "P0", clustB.lab)
write.csv(subset(clust.A.B,clust.A.B$p_val_adj<=sig_cut), paste0("T-Cell.Table.", clust.A.labP0.corrected, ".vs.", clust.B.labP0.corrected, ".adjp.", sig_cut, ".csv"), quote = F)
clust.A.B$p_val_adj[clust.A.B$p_val_adj == 0] <- 2.225074e-308 # adj pval = 0 adjusted to minimum permitted value in R
clust.A.B <- clust.A.B[,c(2,5)]
colnames(clust.A.B) <- c("avg_logFC","p_val_adj")
clust.A.B$genes <- rownames(clust.A.B)
xlim_min <- -1.5
xlim_max <- 1.5
ylim_min <- 0
ylim_max <- 110
volc.plt <- ggplot(data=clust.A.B, aes(x=avg_logFC, y=-log(p_val_adj), label=genes)) +
geom_point(alpha=0.5, size=1, colour=ifelse(clust.A.B$p_val_adj<=sig_cut,"red","grey")) +
geom_text_repel( data= subset(clust.A.B, clust.A.B$avg_logFC > 0 & clust.A.B$p_val_adj<=sig_cut)[1:topN,], show.legend = FALSE, nudge_x=0.1, nudge_y=0.1, segment.size = 0.25, size=3 ) +
geom_text_repel( data= tail(subset(clust.A.B, clust.A.B$avg_logFC < 0 & clust.A.B$p_val_adj<=sig_cut),topN), show.legend = FALSE, nudge_x=0.1, nudge_y=0.1, segment.size = 0.25, size=3 ) +
xlab("Avg logFC") + ylab(bquote("-log"[10]~"(adj.p.value)")) +
scale_x_continuous(limits=c(xlim_min,xlim_max), breaks=seq(xlim_min,xlim_max,0.5)) + scale_y_continuous(limits=c(ylim_min,ylim_max), breaks=seq(ylim_min,ylim_max,25)) +
ggtitle(paste0("Cluster ", clusterID, " (WT/P0)")) + theme(aspect.ratio=1)
return(list(clust.A.B, volc.plt))
}
cluster.1.WT.vs.P0 <- functionPlotDEVolcanoWTP0(matrix.su.WTKO, 1, "WT_1", "KO_1", sig_cut, logfc_cut, topN)
cluster.2.WT.vs.P0 <- functionPlotDEVolcanoWTP0(matrix.su.WTKO, 2, "WT_2", "KO_2", sig_cut, logfc_cut, topN)
cluster.3.WT.vs.P0 <- functionPlotDEVolcanoWTP0(matrix.su.WTKO, 3, "WT_3", "KO_3", sig_cut, logfc_cut, topN)
cluster.4.WT.vs.P0 <- functionPlotDEVolcanoWTP0(matrix.su.WTKO, 4, "WT_4", "KO_4", sig_cut, logfc_cut, topN)
cluster.5.WT.vs.P0 <- functionPlotDEVolcanoWTP0(matrix.su.WTKO, 5, "WT_5", "KO_5", sig_cut, logfc_cut, topN)
cluster.6.WT.vs.P0 <- functionPlotDEVolcanoWTP0(matrix.su.WTKO, 6, "WT_6", "KO_6", sig_cut, logfc_cut, topN)
theme_set(theme_cowplot(font_size=10)) # reduce default font size
pdf("T-Cell.Figure.SuppS8A.pdf", width=12,height=8, onefile=FALSE)
par(bg=NA)
plot_grid(cluster.1.WT.vs.P0[[2]], cluster.2.WT.vs.P0[[2]], cluster.3.WT.vs.P0[[2]],
cluster.4.WT.vs.P0[[2]], cluster.5.WT.vs.P0[[2]], cluster.6.WT.vs.P0[[2]], nrow=2, ncol=3)
dev.off()
sig_cut <- 0.01
logfc_cut <- 0.5
topN <- 10
functionPlotDECorrelation <- function(clustA, clustB, clustA.lab, clustB.lab, sig_cut, logfc_cut, topN) {
print( head(clustA) )
colnames(clustA) <- c("avg_logFC.A", "p_val_adj.A", "genes.A")
colnames(clustB) <- c("avg_logFC.B", "p_val_adj.B", "genes.B")
clustA.topN <- subset(clustA, clustA$avg_logFC.A > 0 & clustA$p_val_adj.A<=sig_cut)[1:topN,]
clustA.botN <- tail(subset(clustA, clustA$avg_logFC.A < 0 & clustA$p_val_adj.A<=sig_cut),topN)
clustB.topN <- subset(clustB, clustB$avg_logFC.B > 0 & clustB$p_val_adj.B<=sig_cut)[1:topN,]
clustB.botN <- tail(subset(clustB, clustB$avg_logFC.B < 0 & clustB$p_val_adj.B<=sig_cut),topN)
compare.A.B.wt.ko <- merge(clustA, clustB, by='row.names', all=TRUE)
compare.A.B.wt.ko[ is.na(compare.A.B.wt.ko)] <- 0
compare.A.B.wt.ko$colour[(compare.A.B.wt.ko$p_val_adj.A <= sig_cut & compare.A.B.wt.ko$p_val_adj.B <= sig_cut)] <- "purple"
compare.A.B.wt.ko$colour[(compare.A.B.wt.ko$p_val_adj.A > sig_cut & compare.A.B.wt.ko$p_val_adj.B <= sig_cut)] <- "blue"
compare.A.B.wt.ko$colour[(compare.A.B.wt.ko$p_val_adj.A == 0 & compare.A.B.wt.ko$p_val_adj.B <= sig_cut)] <- "blue"
compare.A.B.wt.ko$colour[(compare.A.B.wt.ko$p_val_adj.A <= sig_cut & compare.A.B.wt.ko$p_val_adj.B > sig_cut)] <- "darkgreen"
compare.A.B.wt.ko$colour[(compare.A.B.wt.ko$p_val_adj.A <= sig_cut & compare.A.B.wt.ko$p_val_adj.B == 0)] <- "darkgreen"
compare.A.B.wt.ko$colour[(compare.A.B.wt.ko$p_val_adj.A > sig_cut & compare.A.B.wt.ko$p_val_adj.B > sig_cut)] <- ""
compare.A.B.wt.ko$colour[(compare.A.B.wt.ko$p_val_adj.A > sig_cut & compare.A.B.wt.ko$p_val_adj.B == 0 )] <- ""
compare.A.B.wt.ko$colour[(compare.A.B.wt.ko$p_val_adj.A == 0 & compare.A.B.wt.ko$p_val_adj.B > sig_cut)] <- ""
compare.A.B.wt.ko$label <- 0
compare.A.B.wt.ko[compare.A.B.wt.ko$Row.names %in% unlist(rownames(clustA.topN)), ]$label <- 1
compare.A.B.wt.ko[compare.A.B.wt.ko$Row.names %in% unlist(rownames(clustA.botN)), ]$label <- 1
compare.A.B.wt.ko[compare.A.B.wt.ko$Row.names %in% unlist(rownames(clustB.topN)), ]$label <- 1
compare.A.B.wt.ko[compare.A.B.wt.ko$Row.names %in% unlist(rownames(clustB.botN)), ]$label <- 1
print( head(compare.A.B.wt.ko) )
minFC <- -1.5 #min( compare.A.B.wt.ko$avg_logFC.A, compare.A.B.wt.ko$avg_logFC.B )
maxFC <- 1.5 #max( compare.A.B.wt.ko$avg_logFC.A, compare.A.B.wt.ko$avg_logFC.B )
cor.plt <- ggplot(data=compare.A.B.wt.ko, aes(x=avg_logFC.A, y=avg_logFC.B, colour=colour, label=Row.names)) +
geom_vline(xintercept = -(logfc_cut), linetype="dashed", colour="grey", size=0.25) +
geom_vline(xintercept = logfc_cut, linetype="dashed", colour="grey", size=0.25) +
geom_hline(yintercept = -(logfc_cut), linetype="dashed", colour="grey", size=0.25) +
geom_hline(yintercept = logfc_cut, linetype="dashed", colour="grey", size=0.25) +
geom_abline(intercept = 0, linetype="solid", colour="grey") +
geom_point(alpha=0.5, size=2) +
geom_text_repel( show.legend = FALSE, nudge_x=0.1, nudge_y=0.1, colour="black", segment.size = 0.25, size=3,
data= subset(compare.A.B.wt.ko, compare.A.B.wt.ko$label == 1) ) +
coord_fixed() +
scale_x_continuous(limits=c(minFC, maxFC), breaks=seq(minFC, maxFC,0.5)) + scale_y_continuous(limits=c(minFC, maxFC), breaks=seq(minFC, maxFC,0.5)) +
xlab(paste0("Avg logFC (Cluster ", clustA.lab, " WT Vs P0) ")) + ylab(paste0("Avg logFC (Cluster ", clustB.lab, " WT Vs P0) ")) +
scale_colour_manual(name="", values=c("purple"="purple", "blue"="blue", "darkgreen"="darkgreen", "grey"="grey"),
labels=c("purple"=paste0("clusters ", clustA.lab, " & ", clustB.lab), "blue"=paste0("cluster ", clustB.lab, " only"), "darkgreen"=paste0("cluster ", clustA.lab, " only"))) +
ggtitle(paste0("Cluster ", clustA.lab, " (WT/P0) Vs Cluster ", clustB.lab, " (WT/P0)")) +
theme(text=element_text(family="sans"), legend.position="bottom", aspect.ratio=1 )
return(cor.plt)
}
corr.plot.2.3 <- functionPlotDECorrelation(cluster.2.WT.vs.P0[[1]], cluster.3.WT.vs.P0[[1]], "2", "3", sig_cut, logfc_cut, topN)
corr.plot.2.4 <- functionPlotDECorrelation(cluster.2.WT.vs.P0[[1]], cluster.2.WT.vs.P0[[1]], "2", "4", sig_cut, logfc_cut, topN)
corr.plot.2.5 <- functionPlotDECorrelation(cluster.2.WT.vs.P0[[1]], cluster.2.WT.vs.P0[[1]], "2", "4", sig_cut, logfc_cut, topN)
corr.plot.3.4 <- functionPlotDECorrelation(cluster.3.WT.vs.P0[[1]], cluster.4.WT.vs.P0[[1]], "3", "4", sig_cut, logfc_cut, topN)
corr.plot.3.5 <- functionPlotDECorrelation(cluster.3.WT.vs.P0[[1]], cluster.5.WT.vs.P0[[1]], "3", "5", sig_cut, logfc_cut, topN)
corr.plot.4.5 <- functionPlotDECorrelation(cluster.4.WT.vs.P0[[1]], cluster.5.WT.vs.P0[[1]], "4", "5", sig_cut, logfc_cut, topN)
theme_set(theme_cowplot(font_size=12)) # reduce default font size
pdf("T-Cell.Figure.SuppS8B.pdf", width=16,height=11, onefile=FALSE)
par(bg=NA)
plot_grid(corr.plot.2.3, corr.plot.2.4, corr.plot.2.5, corr.plot.3.4, corr.plot.3.5, corr.plot.4.5, nrow=2, ncol=3 )
dev.off()
matrix.su.WTKO <- SetAllIdent(matrix.su.WTKO, id = "res.0.6")
sig_cut <- 0.01
logfc_cut <- 0.5
topN <- 10
functionPlotDEVolcano <- function(matrix, clustA.lab, clustB.lab, sig_cut, logfc_cut, topN) {
clust.A.B <- FindMarkers(matrix, ident.1 = clustA.lab, ident.2 = clustB.lab, print.bar = TRUE)
clust.A.B <- clust.A.B[order(clust.A.B$avg_logFC, decreasing=TRUE),]
clust.A.labP0.corrected <- gsub("KO", "P0", clustA.lab)
clust.B.labP0.corrected <- gsub("KO", "P0", clustB.lab)
write.csv(subset(clust.A.B,clust.A.B$p_val_adj<=sig_cut), paste0("T-Cell.Table.", clust.A.labP0.corrected, ".vs.", clust.B.labP0.corrected, ".adjp.", sig_cut, ".csv"), quote = F)
clust.A.B$p_val_adj[clust.A.B$p_val_adj == 0] <- 2.225074e-308 # adj pval = 0 adjusted to minimum permitted value in R
clust.A.B <- clust.A.B[,c(2,5)]
colnames(clust.A.B) <- c("avg_logFC","p_val_adj")
clust.A.B$genes <- rownames(clust.A.B)
xlim_min <- -3.5 #min(clust.A.B$avg_logFC)
xlim_max <- 3.5 #max(clust.A.B$avg_logFC)
ylim_min <- 0
ylim_max <- 750 #min(clust.A.B$p_val_adj)
volc.plt <- ggplot(data=clust.A.B, aes(x=avg_logFC, y=-log(p_val_adj), label=genes)) +
geom_point(alpha=0.5, size=1, colour=ifelse(clust.A.B$p_val_adj<=sig_cut,"red","grey")) +
geom_text_repel( data= subset(clust.A.B, clust.A.B$avg_logFC > 0 & clust.A.B$p_val_adj<=sig_cut)[1:topN,], show.legend = FALSE, nudge_x=0.1, nudge_y=0.1, segment.size = 0.25, size=2 ) +
geom_text_repel( data= tail(subset(clust.A.B, clust.A.B$avg_logFC < 0 & clust.A.B$p_val_adj<=sig_cut),topN), show.legend = FALSE, nudge_x=0.1, nudge_y=0.1, segment.size = 0.25, size=2 ) +
xlab("Avg logFC") + ylab(bquote("-log"[10]~"(adj.p.value)")) +
scale_x_continuous(limits=c(xlim_min,xlim_max), breaks=seq(xlim_min,xlim_max,1)) + scale_y_continuous(limits=c(ylim_min,ylim_max), breaks=seq(ylim_min,ylim_max,100)) +
ggtitle(paste0("Cluster ", clustA.lab, " vs ", clustB.lab)) + theme(aspect.ratio=1)
return(volc.plt)
}
cluster.1.vs.2 <- functionPlotDEVolcano(matrix.su.WTKO, 1, 2, sig_cut, logfc_cut, topN)
cluster.1.vs.3 <- functionPlotDEVolcano(matrix.su.WTKO, 1, 3, sig_cut, logfc_cut, topN)
cluster.1.vs.4 <- functionPlotDEVolcano(matrix.su.WTKO, 1, 4, sig_cut, logfc_cut, topN)
cluster.1.vs.5 <- functionPlotDEVolcano(matrix.su.WTKO, 1, 5, sig_cut, logfc_cut, topN)
cluster.1.vs.6 <- functionPlotDEVolcano(matrix.su.WTKO, 1, 6, sig_cut, logfc_cut, topN)
cluster.2.vs.3 <- functionPlotDEVolcano(matrix.su.WTKO, 2, 3, sig_cut, logfc_cut, topN)
cluster.2.vs.4 <- functionPlotDEVolcano(matrix.su.WTKO, 2, 4, sig_cut, logfc_cut, topN)
cluster.2.vs.5 <- functionPlotDEVolcano(matrix.su.WTKO, 2, 5, sig_cut, logfc_cut, topN)
cluster.2.vs.6 <- functionPlotDEVolcano(matrix.su.WTKO, 2, 6, sig_cut, logfc_cut, topN)
cluster.3.vs.4 <- functionPlotDEVolcano(matrix.su.WTKO, 3, 4, sig_cut, logfc_cut, topN)
cluster.3.vs.5 <- functionPlotDEVolcano(matrix.su.WTKO, 3, 5, sig_cut, logfc_cut, topN)
cluster.3.vs.6 <- functionPlotDEVolcano(matrix.su.WTKO, 3, 6, sig_cut, logfc_cut, topN)
cluster.4.vs.5 <- functionPlotDEVolcano(matrix.su.WTKO, 4, 5, sig_cut, logfc_cut, topN)
cluster.4.vs.6 <- functionPlotDEVolcano(matrix.su.WTKO, 4, 6, sig_cut, logfc_cut, topN)
cluster.5.vs.6 <- functionPlotDEVolcano(matrix.su.WTKO, 5, 6, sig_cut, logfc_cut, topN)
pdf("T-Cell.Figure.SuppS7.pdf", width=15,height=15, onefile=FALSE)
par(bg=NA)
plot_grid(cluster.1.vs.2, cluster.1.vs.3, cluster.1.vs.4, cluster.1.vs.5, cluster.1.vs.6,
NULL, cluster.2.vs.3, cluster.2.vs.4, cluster.2.vs.5, cluster.2.vs.6,
NULL, NULL, cluster.3.vs.4, cluster.3.vs.5, cluster.3.vs.6,
NULL, NULL, NULL, cluster.4.vs.5, cluster.4.vs.6,
NULL, NULL, NULL, NULL, cluster.5.vs.6,
nrow=5, ncol=5)
dev.off()
matrix.su.WTKO <- SetAllIdent(matrix.su.WTKO, id = "cell_type_experiment")
sig_cut <- 0.01
logfc_cut <- 0.5
topN <- 10
functionPlotDEVolcanoCellTypeWTP0 <- function(matrix, clusterID, clustA.lab, clustB.lab, sig_cut, logfc_cut, topN) {
clust.A.B <- FindMarkers(matrix, ident.1 = clustA.lab, ident.2 = clustB.lab, print.bar = TRUE)
clust.A.B <- clust.A.B[order(clust.A.B$avg_logFC, decreasing=TRUE),]
clust.A.labP0.corrected <- gsub("KO", "P0", clustA.lab)
clust.B.labP0.corrected <- gsub("KO", "P0", clustB.lab)
write.csv(subset(clust.A.B,clust.A.B$p_val_adj<=sig_cut), paste0("T-Cell.Table.", clust.A.labP0.corrected, ".vs.", clust.B.labP0.corrected, ".adjp.", sig_cut, ".csv"), quote = F)
clust.A.B$p_val_adj[clust.A.B$p_val_adj == 0] <- 2.225074e-308 # adj pval = 0 adjusted to minimum permitted value in R
clust.A.B <- clust.A.B[,c(2,5)]
colnames(clust.A.B) <- c("avg_logFC","p_val_adj")
clust.A.B$genes <- rownames(clust.A.B)
xlim_min <- -1.5
xlim_max <- 1.5
ylim_min <- 0
ylim_max <- 400
volc.plt <- ggplot(data=clust.A.B, aes(x=avg_logFC, y=-log(p_val_adj), label=genes)) +
geom_point(alpha=0.5, size=1, colour=ifelse(clust.A.B$p_val_adj<=sig_cut,"red","grey")) +
geom_text_repel( data= subset(clust.A.B, clust.A.B$avg_logFC > 0 & clust.A.B$p_val_adj<=sig_cut)[1:topN,], show.legend = FALSE, nudge_x=0.1, nudge_y=0.1, segment.size = 0.25, size=3 ) +
geom_text_repel( data= tail(subset(clust.A.B, clust.A.B$avg_logFC < 0 & clust.A.B$p_val_adj<=sig_cut),topN), show.legend = FALSE, nudge_x=0.1, nudge_y=0.1, segment.size = 0.25, size=3 ) +
xlab("Avg logFC") + ylab(bquote("-log"[10]~"(adj.p.value)")) +
scale_x_continuous(limits=c(xlim_min,xlim_max), breaks=seq(xlim_min,xlim_max,0.5)) + scale_y_continuous(limits=c(ylim_min,ylim_max), breaks=seq(ylim_min,ylim_max,50)) +
ggtitle(paste0("Cluster ", clusterID, " (WT/P0)")) + theme(aspect.ratio=1)
return(list(clust.A.B, volc.plt))
}
clust.DN.wt.ko <- functionPlotDEVolcanoCellTypeWTP0(matrix.su.WTKO, "DN", "WT_DN", "KO_DN", sig_cut, logfc_cut, topN)
clust.DP.wt.ko <- functionPlotDEVolcanoCellTypeWTP0(matrix.su.WTKO, "DP", "WT_DP", "KO_DP", sig_cut, logfc_cut, topN)
clust.TMat.wt.ko <- functionPlotDEVolcanoCellTypeWTP0(matrix.su.WTKO, "TMat", "WT_TMat", "KO_TMat", sig_cut, logfc_cut, topN)
theme_set(theme_cowplot(font_size=10)) # reduce default font size
pdf("T-Cell.Figure.7B.pdf", width=12,height=5, onefile=FALSE)
par(bg=NA)
plot_grid(clust.DN.wt.ko[[2]], clust.DP.wt.ko[[2]], clust.TMat.wt.ko[[2]], nrow=1, ncol=3)
dev.off()
corr.plot.DN.DP.wt.ko <- functionPlotDECorrelation(clust.DN.wt.ko[[1]], clust.DP.wt.ko[[1]], "DN", "DP", sig_cut, logfc_cut, topN)
corr.plot.DP.TMat.wt.ko <- functionPlotDECorrelation(clust.DP.wt.ko[[1]], clust.TMat.wt.ko[[1]], "DP", "TMat", sig_cut, logfc_cut, topN)
theme_set(theme_cowplot(font_size=8)) # reduce default font size
pdf("T-Cell.Figure.7C.pdf", width=10,height=5, onefile=FALSE)
par(bg=NA)
plot_grid(corr.plot.DN.DP.wt.ko, corr.plot.DP.TMat.wt.ko, nrow=1, ncol=2 )
dev.off()
matrix.su.WTKO <- SetAllIdent(matrix.su.WTKO, id = "cell_type")
sig_cut <- 0.01
logfc_cut <- 0.5
topN <- 10
functionPlotDEVolcanoCellType <- function(matrix, clustA.lab, clustB.lab, sig_cut, logfc_cut, topN) {
clust.A.B <- FindMarkers(matrix, ident.1 = clustA.lab, ident.2 = clustB.lab, print.bar = TRUE)
clust.A.B <- clust.A.B[order(clust.A.B$avg_logFC, decreasing=TRUE),]
clust.A.labP0.corrected <- gsub("KO", "P0", clustA.lab)
clust.B.labP0.corrected <- gsub("KO", "P0", clustB.lab)
write.csv(subset(clust.A.B,clust.A.B$p_val_adj<=sig_cut), paste0("T-Cell.Table.", clust.A.labP0.corrected, ".vs.", clust.B.labP0.corrected, ".adjp.", sig_cut, ".csv"), quote = F)
clust.A.B$p_val_adj[clust.A.B$p_val_adj == 0] <- 2.225074e-308 # adj pval = 0 adjusted to minimum permitted value in R
print( subset(clust.A.B, clust.A.B$avg_logFC > 0 & clust.A.B$p_val_adj<=sig_cut)[1:topN,] )
print( tail(subset(clust.A.B, clust.A.B$avg_logFC < 0 & clust.A.B$p_val_adj<=sig_cut),topN) )
clust.A.B <- clust.A.B[,c(2,5)]
colnames(clust.A.B) <- c("avg_logFC","p_val_adj")
clust.A.B$genes <- rownames(clust.A.B)
xlim_min <- -3
xlim_max <- 2.5
ylim_min <- 0
ylim_max <- 750
volc.plt <- ggplot(data=clust.A.B, aes(x=avg_logFC, y=-log(p_val_adj), label=genes)) +
geom_point(alpha=0.5, size=1, colour=ifelse(clust.A.B$p_val_adj<=sig_cut,"red","grey")) +
geom_text_repel( data= subset(clust.A.B, clust.A.B$avg_logFC > 0 & clust.A.B$p_val_adj<=sig_cut)[1:topN,], show.legend = FALSE, nudge_x=0.1, nudge_y=0.1, segment.size = 0.25, size=3 ) +
geom_text_repel( data= tail(subset(clust.A.B, clust.A.B$avg_logFC < 0 & clust.A.B$p_val_adj<=sig_cut),topN), show.legend = FALSE, nudge_x=0.1, nudge_y=0.1, segment.size = 0.25, size=3 ) +
xlab("Avg logFC") + ylab(bquote("-log"[10]~"(adj.p.value)")) +
scale_x_continuous(limits=c(xlim_min,xlim_max), breaks=seq(xlim_min,xlim_max,1.0)) + scale_y_continuous(limits=c(ylim_min,ylim_max), breaks=seq(ylim_min,ylim_max,100)) +
ggtitle(paste0("Cluster ", clustA.lab, " Vs ", clustB.lab)) + theme(aspect.ratio=1)
return(list(clust.A.B, volc.plt))
}
clust.DN.DP <- functionPlotDEVolcanoCellType(matrix.su.WTKO, "DN", "DP", sig_cut, logfc_cut, topN)
clust.DP.TMat <- functionPlotDEVolcanoCellType(matrix.su.WTKO, "DP", "TMat", sig_cut, logfc_cut, topN)
theme_set(theme_cowplot(font_size=10)) # reduce default font size
pdf("T-Cell.Figure.7A.pdf", width=12,height=5, onefile=FALSE)
par(bg=NA)
plot_grid(clust.DN.DP[[2]], clust.DP.TMat[[2]], nrow=1, ncol=2)
dev.off()
message("+--- Giant Heatmap ---+")
clustertableAll <- FindAllMarkers(matrix.su, only.pos = FALSE, do.print = TRUE, print.bar=TRUE)
head(clustertableAll)
# Explore the ribosomal protein content
clustertableAll.RP <- subset(clustertableAll, grepl(glob2rx("Rp*") , gene) )
clustertableAll.RP <- subset(clustertableAll.RP, abs(avg_logFC) > 0.7)
# Extract the top 20 differential genes per cluster
clustertableAll.top20 <- as.data.frame( clustertableAll %>% group_by(cluster) %>% top_n(n = 20, wt = abs(avg_logFC)) )
write.csv(clustertableAll.top20, file = "T-Cell.Table.Supp2.csv")
clustertableAll.top20.RP <- subset(clustertableAll.top20, grepl(glob2rx("Rp*") , gene) )
min.ave_logFC <- min(abs(clustertableAll.top20$avg_logFC))
clustertableAll.original <- clustertableAll
# Remove RBCs = cluster 6
clustertableAll <- subset(clustertableAll, cluster != 6)
# Remove Macrophages = cluster 7
clustertableAll <- subset(clustertableAll, cluster != 7)
unique(clustertableAll$cluster)
#Get the top 20 gene names per cluster
markers.use <- as.data.frame( clustertableAll %>% group_by(cluster) %>% top_n(n = 20, wt = abs(avg_logFC)) )$gene
length(markers.use)
length(unique(markers.use))
message(paste0( "log2 FC range: ", min(clustertableAll$avg_logFC), " to ", max(clustertableAll$avg_logFC)))
clusterAll.data.annot <- as.data.frame( [email protected][ , c("Cell_cycle", "res.0.6")])
colnames(clusterAll.data.annot) <- c("Cell_cycle", "Cluster")
clusterAll.data.annot$Genotype <- gsub("\\.[0-9]$", "", rownames(clusterAll.data.annot))
clusterAll.data.annot$Genotype <- gsub("..{12}$","", clusterAll.data.annot$Genotype)
clusterAll.data.annot$Genotype <- gsub("SLX......","", clusterAll.data.annot$Genotype)
clusterAll.data.annot$Genotype <- gsub("N701|N704|N705|N706","WT", clusterAll.data.annot$Genotype)
clusterAll.data.annot$Genotype <- gsub("N702|N703|N707", "P0", clusterAll.data.annot$Genotype)
#
# Apply Wendi's cluster numbering
#
clusterAll.data.annot$Cluster <- gsub("^", "x", clusterAll.data.annot$Cluster)
clusterAll.data.annot$Cluster <- gsub("x4", "1", clusterAll.data.annot$Cluster)
clusterAll.data.annot$Cluster <- gsub("x0", "2", clusterAll.data.annot$Cluster)
clusterAll.data.annot$Cluster <- gsub("x1", "3", clusterAll.data.annot$Cluster)
clusterAll.data.annot$Cluster <- gsub("x3", "4", clusterAll.data.annot$Cluster)
clusterAll.data.annot$Cluster <- gsub("x5", "5", clusterAll.data.annot$Cluster)
clusterAll.data.annot$Cluster <- gsub("x2", "6", clusterAll.data.annot$Cluster)
clusterAll.data.annot$Cluster <- gsub("x7", "7", clusterAll.data.annot$Cluster)
clusterAll.data.annot$Cluster <- gsub("x6", "8", clusterAll.data.annot$Cluster)
cells.2.keep <- rownames(subset(clusterAll.data.annot, clusterAll.data.annot$Cluster != 8 & clusterAll.data.annot$Cluster != 7))
clusterAll.data.annot <- subset(clusterAll.data.annot, clusterAll.data.annot$Cluster != 8 & clusterAll.data.annot$Cluster != 7)
clusterAll.data.annot <- clusterAll.data.annot[c("Cell_cycle", "Genotype", "Cluster")]
head(clusterAll.data.annot)
clusterAll.data.annot$Cell_cycle <- gsub("G2M", "G2/M", clusterAll.data.annot$Cell_cycle)
head(clusterAll.data.annot)
#ScaleCols <- colorRampPalette(colors = c("white","blue"))(255)
ScaleCols <- colorRampPalette(c("slategrey1", "blue", "navyblue"))(5)
AnnotCols <- list( Cluster=c("1"=cluster_col_range[1], "2"=cluster_col_range[2], "3"=cluster_col_range[3], "4"=cluster_col_range[4], "5"=cluster_col_range[5], "6"=cluster_col_range[6]),
Genotype=c(WT="#32CD32", P0="#BF3EFF"), Cell_cycle=c("G1"="#00BA38", "S"="magenta", "G2/M"="orange"))
clusterAll.scaledata <- [email protected][markers.use, cells.2.keep]
corner(clusterAll.scaledata)
clusterAll.data.annot.clust.ord <- clusterAll.data.annot[ with(clusterAll.data.annot, order(Genotype, Cell_cycle, decreasing=TRUE)), ]
clusterAll.data.annot.clust.ord <- clusterAll.data.annot.clust.ord[ with(clusterAll.data.annot.clust.ord, order(Cluster, decreasing=FALSE)), ]
clusterAll.data.annot.clust.ord <- clusterAll.data.annot.clust.ord[c("Cluster", "Genotype", "Cell_cycle" )]
clusterAll.scaledata <- clusterAll.scaledata[ , rownames(clusterAll.data.annot.clust.ord)]
corner(clusterAll.scaledata)
clusterAll.scaledata <- clusterAll.scaledata[!duplicated(clusterAll.scaledata), ]
colgaps.indi <- c( nrow(subset(clusterAll.data.annot, Cluster==1)), nrow(subset(clusterAll.data.annot, Cluster==2)),
nrow(subset(clusterAll.data.annot, Cluster==3)), nrow(subset(clusterAll.data.annot, Cluster==4)),
nrow(subset(clusterAll.data.annot, Cluster==5)), nrow(subset(clusterAll.data.annot, Cluster==6)) )
colgaps <- c(colgaps.indi[1],
colgaps.indi[1]+colgaps.indi[2],
colgaps.indi[1]+colgaps.indi[2]+colgaps.indi[3],
colgaps.indi[1]+colgaps.indi[2]+colgaps.indi[3]+colgaps.indi[4],
colgaps.indi[1]+colgaps.indi[2]+colgaps.indi[3]+colgaps.indi[4]+colgaps.indi[5] )
message(paste0( "clusterAll.data range: ", min(clusterAll.scaledata), " to ", max(clusterAll.scaledata)))
pdf("T-Cell.Figure.3.pdf", width=27,height=25, onefile=FALSE)
par(bg=NA)
pheatmap(clusterAll.scaledata, show_colnames = FALSE, cluster_rows=TRUE, cluster_cols=FALSE, annotation=clusterAll.data.annot, annotation_colors=AnnotCols,
fontsize=28, fontsize_row=20, col=ScaleCols, gaps_col=colgaps, cutree_rows=8)
dev.off()
clustertableAll <- clustertableAll.original
message("+--- TSNE WT vs KO ---+")
tsne.wtko <- ggplot(data=as.data.frame(matrix.tsne), aes(x=tSNE_1, y=tSNE_2, colour=Experiment)) +
geom_point(alpha=0.5, size=2) + scale_colour_manual("", values = c(WT="#32CD32", P0="#BF3EFF"), limits=c("P0", "WT")) +
geom_density_2d(data=as.data.frame(matrix.tsne), aes(x=tSNE_1, y=tSNE_2, group=Cluster), linetype='dashed', colour="black", bins=4, alpha=0.2)
clusters <- levels(factor(matrix.tsne$Cluster))
for(i in clusters){
centroid <- centroidFunction(matrix.tsne[matrix.tsne$Cluster == i,])
centroid_label_size <- 25
if(i == 7){ centroid[1] <- centroid[1]-3; centroid[2] <- centroid[2]+2.5; centroid_label_size <- 10}
if(i == 8){ centroid[1] <- centroid[1]-3; centroid[2] <- centroid[2]+2.5; centroid_label_size <- 10}
tsne.wtko <- tsne.wtko + annotate("text", x = centroid$x, y = centroid$y, label = paste0(i),size=centroid_label_size, alpha=0.6)
}
tsne.wtko <- tsne.wtko + xlab("") + ylab("") + coord_fixed() +
theme(text=element_text(size=16, family="sans"),
legend.position=c(0.01, 0.2), legend.text=element_text(size=30), legend.title=element_text(size=20), line = element_blank(),
legend.key.height = unit(1.25, "cm"), axis.text.x=element_blank(), axis.text.y=element_blank()) + guides(colour = guide_legend(override.aes = list(size=5)))
tsne.wtko
pdf("T-Cell.Figure.6A.pdf", width=10,height=10, onefile=FALSE)
par(bg=NA)
tsne.wtko
dev.off()
message("+--- TSNE Cell Cycle ---+")
tsne.cc <- ggplot(data=as.data.frame(matrix.tsne), aes(x=tSNE_1, y=tSNE_2, colour=Cell_cycle)) +
geom_point(alpha=0.5, size=2) +
scale_colour_manual(name="", limits=c("G1", "S", "G2M"), labels=c("G1", "S", "G2/M"), values=(c("G1"="#00BA38", "S"="magenta","G2M"="orange"))) +
geom_density_2d(data=matrix.tsne, aes(x=tSNE_1, y=tSNE_2, group=Cluster, alpha=..level..), linetype='dashed', colour="black", bins=3, alpha=0.5)
clusters <- levels(factor(matrix.tsne$Cluster))
for(i in clusters){
centroid <- centroidFunction(matrix.tsne[matrix.tsne$Cluster == i,])
centroid_label_size <- 25
if(i == 7){ centroid[1] <- centroid[1]-3; centroid[2] <- centroid[2]+2.5; centroid_label_size <- 10}
if(i == 8){ centroid[1] <- centroid[1]-3; centroid[2] <- centroid[2]+2.5; centroid_label_size <- 10}
tsne.cc <- tsne.cc + annotate("text", x = centroid$x, y = centroid$y, label = paste0(i),size=centroid_label_size, alpha=0.6)
}
tsne.cc <- tsne.cc + xlab("") + ylab("") + coord_fixed() +
theme(text=element_text(size=16, family="sans"),
legend.position=c(0.01, 0.2), legend.text=element_text(size=30), legend.title=element_text(size=20), line = element_blank(),
legend.key.height = unit(1.25, "cm"), axis.text.x=element_blank(), axis.text.y=element_blank()) + guides(colour = guide_legend(override.aes = list(size=5)))
tsne.cc
pdf("T-Cell.Figure.4A.pdf", width=10,height=10, onefile=FALSE)
par(bg=NA)
tsne.cc
dev.off()
message("+--- TSNE Ribosomal Protein ---+")
rp.list <- c("Rpl37", "Rps26", "Rpl41", "Rps29", "Rps28", "Rps15a", "Rps27", "Rpl38", "Rpl36a", "Rpl23", "Rpl39", "Rps21", "Rpl22l1")
rp.cluster.ave.exp <- AverageExpression(object = matrix.su, use.scale=TRUE)
rp.data.exp <- as.data.frame(rp.cluster.ave.exp[rp.list,])
rp.data.exp$gene <- rownames(rp.data.exp)
rp.data.exp.mlt <- melt(rp.data.exp)
colnames(rp.data.exp.mlt) <- c("gene", "cluster", "ave.exp.scale")
rp.ave.exp.per.cluster <- aggregate(ave.exp.scale ~ cluster, rp.data.exp.mlt, mean)
RP.scaled <- as.data.frame(t([email protected][rp.list, ]))
RP.scaled$RP.ave <- rowMeans(RP.scaled)
matrix.rp.tsne <- as.data.frame(matrix.tsne)
matrix.rp.tsne$RP.ave.exp <- RP.scaled$RP.ave
matrix.rp.tsne$RP.ave.exp <- as.numeric(as.character(matrix.rp.tsne$RP.ave.exp))
ScaleCols <- colorRampPalette(colors = c("lightgrey", "blue"))(255)
tsne.rp <- ggplot(data=matrix.rp.tsne, aes(x=tSNE_1, y=tSNE_2)) +
geom_point(aes(colour=RP.ave.exp), alpha=0.99, size=2) +
scale_colour_gradient2(name="Ribosomal Protein\nAve. Expression", low="white", mid=("lightsteelblue1"), high="blue", midpoint=-0.1) +
geom_density_2d(data=matrix.rp.tsne, aes(x=tSNE_1, y=tSNE_2, group=Cluster, alpha=..level..), linetype='dashed', colour="black", bins=3, alpha=0.5)
clusters <- levels(factor(matrix.rp.tsne$Cluster))
for(i in clusters){
centroid <- centroidFunction(matrix.rp.tsne[matrix.rp.tsne$Cluster == i,])
centroid_label_size <- 25
if(i == 7){ centroid[1] <- centroid[1]-3; centroid[2] <- centroid[2]+2.5; centroid_label_size <- 10}
if(i == 8){ centroid[1] <- centroid[1]-3; centroid[2] <- centroid[2]+2.5; centroid_label_size <- 10}
tsne.rp <- tsne.rp + annotate("text", x = centroid$x, y = centroid$y, label = paste0(i),size=centroid_label_size, alpha=0.6)
}
tsne.rp <- tsne.rp + xlab("") + ylab("") + coord_fixed() +
theme(text=element_text(size=16, family="sans"),
legend.position=c(0.82,0.85), legend.text=element_text(size=15), legend.title=element_text(size=15), line = element_blank(),
legend.key.height = unit(1.05, "cm"), axis.text.x=element_blank(), axis.text.y=element_blank()) +
guides(colour = guide_legend(reverse=T, override.aes = list( size=5)))
tsne.rp
pdf("T-Cell.Figure.5B.pdf", width=10,height=10, onefile=FALSE)
par(bg=NA)
tsne.rp
dev.off()
message("+--- TSNE cell cycle colouring plus split by experiment ---+")
matrix.tsne.custom <- data.frame( (matrix.tsne) ,stringsAsFactors = FALSE)
matrix.tsne.custom$Cell_cycle <- as.character(matrix.tsne.custom$Cell_cycle)
matrix.tsne.custom$wtcols <- matrix.tsne.custom$Cell_cycle
matrix.tsne.custom$wtcols[ grepl("P0", matrix.tsne.custom$Experiment)] <- "P0"
matrix.tsne.custom$p0cols <- matrix.tsne.custom$Cell_cycle
matrix.tsne.custom$p0cols[ grepl("WT", matrix.tsne.custom$Experiment)] <- "WT"
head(matrix.tsne.custom)
tsne.m1.umi_mt_cc.wt.leg <- ggplot(data=as.data.frame(matrix.tsne.custom), aes(x=tSNE_1, y=tSNE_2, colour=Cell_cycle)) +
geom_point(alpha=0.5, size=2) +
scale_colour_manual(name="Cell Cycle", limits=c("G1", "S", "G2M"), labels=c("G1", "S", "G2/M"), values=(c("G1"="#00BA38", "S"="magenta","G2M"="orange")))
tsne.m1.umi_mt_cc.wt <- ggplot(data=as.data.frame(matrix.tsne.custom), aes(x=tSNE_1, y=tSNE_2, colour=wtcols)) +
geom_density_2d(data=as.data.frame(matrix.tsne), aes(x=tSNE_1, y=tSNE_2, group=Cluster), linetype='dashed', colour="black", bins=10, alpha=0.2) +
geom_point(alpha=0.5, size=2) + coord_fixed() +
ggtitle("WT (With Cell Cycle Regression)") +
xlab("tSNE 1") + ylab("tSNE 2") +
theme(legend.position="none") +
scale_colour_manual(name="Cell Cycle", values=(c("S"="magenta", "G1"="#00BA38","G2M"="orange", "P0"="grey")))
tsne.m1.umi_mt_cc.p0 <- ggplot(data=as.data.frame(matrix.tsne.custom), aes(x=tSNE_1, y=tSNE_2, colour=p0cols)) +
geom_density_2d(data=as.data.frame(matrix.tsne), aes(x=tSNE_1, y=tSNE_2, group=Cluster), linetype='dashed', colour="black", bins=10, alpha=0.2) +
geom_point(alpha=0.5, size=2) + coord_fixed() +
ggtitle("P0 (With Cell Cycle Regression)") +
xlab("tSNE 1") + ylab("tSNE 2") +
theme(legend.position="none") +
scale_colour_manual(name="Cell Cycle", values=(c("S"="magenta", "G1"="#00BA38","G2M"="orange", "WT"="grey" )))
load(paste0("SLX-7632.matrix.su.WTvar.genecut.300.res.", resolution, ".reg.umi_mt.robj"), verbose=TRUE)
load(paste0("SLX-7632.matrix.WTvar.tsne.genecut.300.res.", resolution, ".reg.umi_mt.robj"), verbose=TRUE)
matrix.m1.umi_mt.su <- matrix.su.WTvar
matrix.m1.umi_mt.tsne <- matrix.WTvar.tsne
unlink("matrix.su.WTvar")
unlink("matrix.tsneWTvar")
matrix.m1.umi_mt.tsne$Experiment <- gsub("\\.[0-9]$", "", rownames(matrix.m1.umi_mt.tsne))
matrix.m1.umi_mt.tsne$Experiment <- gsub("..{12}$","", matrix.m1.umi_mt.tsne$Experiment)
matrix.m1.umi_mt.tsne$Experiment <- gsub("SLX......","", matrix.m1.umi_mt.tsne$Experiment)
matrix.m1.umi_mt.tsne$Experiment <- gsub("N701|N704|N705|N706","WT", matrix.m1.umi_mt.tsne$Experiment)
matrix.m1.umi_mt.tsne$Experiment <- gsub("N702|N703|N707", "KO", matrix.m1.umi_mt.tsne$Experiment)
reso <- paste0("res.0.6")
clust.m1.umi_mt <- [email protected] %>% dplyr::select(contains(reso))
colnames(clust.m1.umi_mt) <- c("Cluster")
matrix.m1.umi_mt.tsne$Cluster <- clust.m1.umi_mt$Cluster
cellcycle.m1.umi_mt <- [email protected] %>% dplyr::select(contains("Cell_cycle"))
colnames(cellcycle.m1.umi_mt) <- c("Cell_cycle")
matrix.m1.umi_mt.tsne$Cell_cycle <- cellcycle.m1.umi_mt$Cell_cycle
matrix.m1.umi_mt.tsne$Cluster <- gsub("^", "x", matrix.m1.umi_mt.tsne$Cluster)
matrix.m1.umi_mt.tsne$Cluster <- gsub("x4", "1", matrix.m1.umi_mt.tsne$Cluster)
matrix.m1.umi_mt.tsne$Cluster <- gsub("x0", "2", matrix.m1.umi_mt.tsne$Cluster)
matrix.m1.umi_mt.tsne$Cluster <- gsub("x1", "3", matrix.m1.umi_mt.tsne$Cluster)
matrix.m1.umi_mt.tsne$Cluster <- gsub("x3", "4", matrix.m1.umi_mt.tsne$Cluster)
matrix.m1.umi_mt.tsne$Cluster <- gsub("x5", "5", matrix.m1.umi_mt.tsne$Cluster)
matrix.m1.umi_mt.tsne$Cluster <- gsub("x2", "6", matrix.m1.umi_mt.tsne$Cluster)
matrix.m1.umi_mt.tsne$Cluster <- gsub("x7", "7", matrix.m1.umi_mt.tsne$Cluster)
matrix.m1.umi_mt.tsne$Cluster <- gsub("x6", "8", matrix.m1.umi_mt.tsne$Cluster)
matrix.m1.umi_mt.tsne$Experiment <- gsub("KO", "P0", matrix.m1.umi_mt.tsne$Experiment)
matrix.m1.umi_mt.tsne <- data.frame( (matrix.m1.umi_mt.tsne) ,stringsAsFactors = FALSE)
matrix.m1.umi_mt.tsne$Cell_cycle <- as.character(matrix.m1.umi_mt.tsne$Cell_cycle)
matrix.m1.umi_mt.tsne$wtcols <- matrix.m1.umi_mt.tsne$Cell_cycle
matrix.m1.umi_mt.tsne$wtcols[ grepl("P0", matrix.m1.umi_mt.tsne$Experiment)] <- "P0"
matrix.m1.umi_mt.tsne$p0cols <- matrix.m1.umi_mt.tsne$Cell_cycle
matrix.m1.umi_mt.tsne$p0cols[ grepl("WT", matrix.m1.umi_mt.tsne$Experiment)] <- "WT"
head(matrix.m1.umi_mt.tsne)
tsne.m1.umi_mt.wt <- ggplot(data=as.data.frame(matrix.m1.umi_mt.tsne), aes(x=tSNE_1, y=tSNE_2, colour=wtcols)) +
geom_density_2d(data=as.data.frame(matrix.m1.umi_mt.tsne), aes(x=tSNE_1, y=tSNE_2, group=Cluster), linetype='dashed', colour="black", bins=10, alpha=0.2) +
geom_point(alpha=0.5, size=2) + coord_fixed() +
ggtitle("WT (Without Cell Cycle Regression)") +
xlab("tSNE 1") + ylab("tSNE 2") + xlim(-40,50) +
theme(legend.position="none") +
scale_colour_manual(name="Cell Cycle", values=(c("S"="magenta", "G1"="#00BA38","G2M"="orange", "P0"="grey")))
tsne.m1.umi_mt.p0 <- ggplot(data=as.data.frame(matrix.m1.umi_mt.tsne), aes(x=tSNE_1, y=tSNE_2, colour=p0cols)) +
geom_density_2d(data=as.data.frame(matrix.m1.umi_mt.tsne), aes(x=tSNE_1, y=tSNE_2, group=Cluster), linetype='dashed', colour="black", bins=10, alpha=0.2) +
geom_point(alpha=0.5, size=2) + coord_fixed() +
ggtitle("P0 (Without Cell Cycle Regression)") +