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DESeq_GO_Analysis.R
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#!/usr/local/bin/Rscript
# CTR_myt25_0006
# Human trophoblast models RNASeq data
# TSC-3D, TSC-2D, TSC-EVT, TSC-SCT, TO and TO-EVT
#
# Analysis Performed by Russell S. Hamilton & Xiaohui Zhao
# Centre for Trophoblast Reseach, University of Cambridge, UK
# Copyright Russell S. Hamilton ([email protected]) & Xiaohui Zhao ([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/>.
#------------------------------------------------------------------------------
suppressPackageStartupMessages({
library("tidyr")
library("dplyr")
library("methods")
library("utils")
library("ggplot2")
library("ggrepel")
library("cowplot")
library("Matrix")
library("matrixStats")
library("useful")
library("reshape")
library("reshape2")
library("DESeq2")
library("biomaRt")
library("ggforce")
library("pheatmap")
library('RColorBrewer')
library("scales")
library("ggbeeswarm")
library("BiocParallel")
library("ggalt")
library("ComplexHeatmap")
library("biomaRt")
})
NUMCORES <- 3
register(MulticoreParam(NUMCORES))
Project <- "CTR_myt25_0006"
baseDir <- "/storage/CTR-Projects/CTR_myt25/CTR_myt25_0006"
setwd(baseDir)
print(baseDir)
TOPNUM <- 2000
l2fc <- 1
significance <- 0.05
elementTextSize <- 6
message("+-------------------------------------------------------------------------------+")
message("+ Use ensEMBL Annotations+")
message("+-------------------------------------------------------------------------------+")
# attributePages(mart)
# if(interactive()){
# mart = useMart("ensembl", dataset="hsapiens_gene_ensembl")
# attributeSummary(mart)}
#BiocManager::install('grimbough/biomaRt')
ensembl = useEnsembl(biomart="ENSEMBL_MART_ENSEMBL", dataset="hsapiens_gene_ensembl",host = 'ensembl.org')
ensEMBL2id <- getBM(attributes=c('ensembl_gene_id', 'external_gene_name', 'description',
'chromosome_name'), mart = ensembl, useCache = FALSE)
head(ensEMBL2id)
nrow(ensEMBL2id)
message("+-------------------------------------------------------------------------------+")
message("+ RNA-Seq ----------------------------+")
message("+-------------------------------------------------------------------------------+")
allLanes_counts <- read.table(paste0(baseDir, "/reverse_stranded_Analysis/",
"featurecounts.merged.counts.tsv"),
header=TRUE, row.names=1)
allLanes_counts <- allLanes_counts[ ,2:ncol(allLanes_counts)]
corner(allLanes_counts)
colnames(allLanes_counts)
meta.tbl.ori <- read.csv(paste0(baseDir, "/reverse_stranded_Analysis/SampleTable_4Lanes_2Run.csv"), header=T)
meta.tbl <- as.data.frame(colnames(allLanes_counts), drop=F)
colnames(meta.tbl) <- c("sampleID")
meta.tbl$sampleName <- meta.tbl$sampleID
meta.tbl$sampleName <- gsub(".L[0-9].*", "", meta.tbl$sampleName)
meta.tbl$origin <- meta.tbl$sampleName
meta.tbl$origin <- gsub("^[A-Z]*[0-9]*.", "", meta.tbl$origin)
meta.tbl$individual <- as.character(unlist(lapply(meta.tbl$sampleName, function(x) strsplit(x, split="_")[[1]][1])))
rownames(meta.tbl) <- meta.tbl$sampleID
meta.tbl$lane <- as.character(rep(c(1,2), length=272))
meta.tbl <- data.frame(meta.tbl[ ,2:ncol(meta.tbl)])
meta.tbl$Group <- ifelse(meta.tbl$origin=="EVT", "TSC-EVT", meta.tbl$origin)
meta.tbl$Group <- ifelse(meta.tbl$origin=="Okae_TOM", "TSC-3D", meta.tbl$Group)
meta.tbl$Group <- ifelse(meta.tbl$origin=="STB", "TSC-STC", meta.tbl$Group)
meta.tbl$Group <- ifelse(meta.tbl$origin=="TSC", "TSC-2D", meta.tbl$Group)
meta.tbl$Group <- ifelse(meta.tbl$origin=="org_EVT", "TO-EVT", meta.tbl$Group)
meta.tbl$Group <- ifelse(meta.tbl$origin=="org_TOM", "TO", meta.tbl$Group)
head(meta.tbl)
table( paste0(meta.tbl$origin) )
table( paste0(meta.tbl$individual) )
table( paste0(meta.tbl$individual, "_", meta.tbl$origin) )
write.csv(meta.tbl, file = paste0(baseDir, "/reverse_stranded_Analysis/SampleTable_4Lanes_2Run.csv"))
message("+-------------------------------------------------------------------------------+")
message("+ DESeq2 Processing +")
message("+-------------------------------------------------------------------------------+")
dds <- DESeqDataSetFromMatrix(countData=allLanes_counts, colData=meta.tbl, design=~Group)
dds <- DESeq(dds, parallel=TRUE)
# colapseReplicates
vsd <- vst(dds, blind=F)
colData(vsd)
message("+-------------------------------------------------------------------------------+")
message("+-------- Custom PCA ---------+")
message("+-------------------------------------------------------------------------------+")
pcaData <- plotPCA(vsd, ntop=TOPNUM, intgroup=c("Group"), returnData=TRUE)
percentVar <- round(100 * attr(pcaData, "percentVar"))
pdf(paste0(baseDir, "/reverse_stranded_Analysis/", Project, "_Fig.PCA.T2000.pdf"),width=10,height=10)
par(bg=NA)
ggplot(pcaData, aes(PC1, PC2, color=Group)) +
geom_mark_ellipse(aes(fill = NULL, color=paste0(Group), group=paste0(Group),
label=Group), alpha=0.1, label.fontsize = 6, label.buffer = unit(1, 'mm')) +
geom_point(size=2, alpha=0.75) +
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance")) +
coord_fixed() +
theme_bw() +
labs(title = paste0("PCA (Top ", TOPNUM, " Most Variable Genes)"),
subtitle = "2 Sequencing Runs, 4 lanes each (8 points per sample)",
caption = "CTR_myt25_0006: Margherita Turco" ) +
theme(text = element_text(size=12),
plot.subtitle=element_text(size=8, face="italic", color="darkgrey"),
legend.position='none', aspect.ratio=1)
dev.off()
resultsNames(vsd)
## [1] "Intercept" "origin_3D.fluffy_vs_2D.fluffy"
## [3] "origin_EVT_vs_2D.fluffy" "origin_Okae.TOM_vs_2D.fluffy"
## [5] "origin_org.EVT_vs_2D.fluffy" "origin_org.TOM_vs_2D.fluffy"
## [7] "origin_STB_vs_2D.fluffy" "origin_TSC_vs_2D.fluffy"
message("+-------------------------------------------------------------------------------+")
message("+--- Remove fluffy samples, 48 ---------+")
message("+-------------------------------------------------------------------------------+")
meta.tbl.rm <- meta.tbl[-grep("*.fluffy", meta.tbl$origin), ]
write.csv(meta.tbl.rm, file = paste0(baseDir, "/reverse_stranded_Analysis/SampleTable_4Lanes_2Run_rmfluffy.csv"), row.names=F)
allLanes_counts.rm <- allLanes_counts[,-grep("*.fluffy", colnames(allLanes_counts))]
dds.rm <- DESeqDataSetFromMatrix(countData=allLanes_counts.rm, colData=meta.tbl.rm, design=~Group)
dds.rm <- DESeq(dds.rm, parallel=TRUE)
# colapseReplicates
vsd.rm <- vst(dds.rm, blind=F)
colData(vsd.rm)
save(dds, dds.rm, dds.rm.collapse, file = paste0(baseDir, "/reverse_stranded_Analysis/",Project, "-OriginalData_DESeq_dds.RData"))
message("+-------------------------------------------------------------------------------+")
message("+-------- Custom PCA for Rm fluffy ---------+")
message("+-------------------------------------------------------------------------------+")
pcaData.rm <- plotPCA(vsd.rm, ntop=TOPNUM, intgroup=c("Group"), returnData=TRUE)
percentVar.rm <- round(100 * attr(pcaData.rm, "percentVar"))
pdf(paste0(baseDir, "/reverse_stranded_Analysis/", Project, "_Fig.PCA.rmFluffy.T2000.pdf"),width=10,height=10)
par(bg=NA)
ggplot(pcaData.rm, aes(PC1, PC2, color=Group)) +
geom_mark_ellipse(aes(fill = NULL, color=paste0(Group), group=paste0(Group),
label=Group), alpha=0.1, label.fontsize = 6, label.buffer = unit(1, 'mm')) +
geom_point(size=2, alpha=0.75) +
xlab(paste0("PC1: ",percentVar.rm[1],"% variance")) +
ylab(paste0("PC2: ",percentVar.rm[2],"% variance")) +
coord_fixed() +
theme_bw() +
labs(title = paste0("PCA (Top ", TOPNUM, " Most Variable Genes)"),
subtitle = "2 Sequencing Runs, 4 lanes each (8 points per sample)",
caption = "CTR_myt25_0006: Margherita Turco" ) +
theme(text = element_text(size=12),
plot.subtitle=element_text(size=8, face="italic", color="darkgrey"),
legend.position='none', aspect.ratio=1)
dev.off()
pdf(paste0(baseDir, "/reverse_stranded_Analysis/", Project, "_Fig.PCA.rmFluffy.T2000.rmLab.pdf"),width=10,height=10)
par(bg=NA)
ggplot(pcaData.rm, aes(PC1, PC2, color=Group)) +
geom_mark_ellipse(aes(fill = NULL, color=paste0(Group), group=paste0(Group)), alpha=0.1) +
geom_point(size=2, alpha=0.75) +
xlab(paste0("PC1: ",percentVar.rm[1],"% variance")) +
ylab(paste0("PC2: ",percentVar.rm[2],"% variance")) +
coord_fixed() +
theme_bw() +
labs(title = paste0("PCA (Top ", TOPNUM, " Most Variable Genes)"),
subtitle = "2 Sequencing Runs, 4 lanes each (8 points per sample)")+
theme(text = element_text(size=12),
plot.subtitle=element_text(size=8, face="italic", color="darkgrey"),
legend.position='none', aspect.ratio=1,
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank())
dev.off()
resultsNames(vsd.rm)
## [1] "Intercept" "origin_3D.fluffy_vs_2D.fluffy"
## [3] "origin_EVT_vs_2D.fluffy" "origin_Okae.TOM_vs_2D.fluffy"
## [5] "origin_org.EVT_vs_2D.fluffy" "origin_org.TOM_vs_2D.fluffy"
## [7] "origin_STB_vs_2D.fluffy" "origin_TSC_vs_2D.fluffy"
dds.rm.collapse <- collapseReplicates(dds.rm, dds.rm$sampleName, renameCols = TRUE)
meta.tbl.rm.collapse <- colData(dds.rm.collapse)
vsd.rm.collapse <- vst(dds.rm.collapse, blind=F)
colData(vsd.rm.collapse)
pcaData.rm.collapse <- plotPCA(vsd.rm.collapse, ntop=TOPNUM, intgroup=c("Group"), returnData=TRUE)
percentVar.rm.collapse <- round(100 * attr(pcaData.rm.collapse, "percentVar"))
pcaData.rm.collapse$newname <- unlist(lapply(as.character(pcaData.rm.collapse$name), function(x) strsplit(x, split="[_]")[[1]][1]))
pcaData.rm.collapse$origin <- meta.tbl.rm.collapse$origin
pdf(paste0(baseDir, "/reverse_stranded_Analysis/", Project, "-Fig2A.PCA.rmFluffy.T2000.collapsed.labels.pdf"),width=10,height=10)
par(bg=NA)
ggplot(pcaData.rm.collapse, aes(PC1, PC2, color=Group, label=newname)) +
geom_mark_ellipse(aes(fill = NULL, color=paste0(Group), group=paste0(Group),
label=Group), alpha=0.1, label.fontsize = 6, label.buffer = unit(1, 'mm')) +
geom_point(size=2, alpha=0.75) +
geom_text_repel(data = pcaData.rm.collapse, aes(label = newname)) +
xlab(paste0("PC1: ",percentVar.rm[1],"% variance")) +
ylab(paste0("PC2: ",percentVar.rm[2],"% variance")) +
coord_fixed() +
theme_bw() +
labs(title = paste0("PCA (Top ", TOPNUM, " Most Variable Genes)"),
subtitle = "2 Sequencing Runs, 4 lanes each (collapsed)",
caption = "CTR_myt25_0006: Margherita Turco" ) +
theme(text = element_text(size=12),
plot.subtitle=element_text(size=8, face="italic", color="darkgrey"),
legend.position='none', aspect.ratio=1)
dev.off()
message("+-------------------------------------------------------------------------------+")
message("+-------- Fig.2A PCA plot ---------+")
message("+-------------------------------------------------------------------------------+")
pdf(paste0(baseDir, "/reverse_stranded_Analysis/Fig.2A_PCA.pdf"),width=10,height=10)
par(bg=NA)
ggplot(pcaData.rm.collapse, aes(PC1, PC2, color=origin)) +
geom_mark_ellipse(aes(fill = NULL, color=paste0(origin), group=paste0(origin)), alpha=0.1) +
geom_point(size=2, alpha=0.75) +
xlab(paste0("PC1: ",percentVar.rm[1],"% variance")) +
ylab(paste0("PC2: ",percentVar.rm[2],"% variance")) +
coord_fixed() +
theme_bw() +
theme(text = element_text(size=12),
plot.subtitle=element_text(size=8, face="italic", color="darkgrey"),
legend.position='none', aspect.ratio=1)
dev.off()
message("+-------------------------------------------------------------------------------+")
message("+--- Pairwise DESeq Analysis, revised 05/05/21, add TSC2DvsTroph.org -----+")
message("+-------------------------------------------------------------------------------+")
control.names <- c("*_TSC_*", "*_org_TOM_*", "*_org_TOM_*", "TSC", "TSC", "*_org_EVT_*", "*_org_TOM_*", "*_org_TOM_*")
compare.names <- c("*_Okae_TOM.*", "*_Okae_TOM_*", "*_org_EVT_*", "EVT", "STB", "EVT", "STB", "*_TSC_*")
control.Tnames <- c("TSC", "org_TOM","org_TOM", "TSC", "TSC", "org_EVT", "org_TOM", "org_TOM")
compare.Tnames <- c("Okae_TOM", "Okae_TOM","org_EVT", "EVT", "STB", "EVT", "STB", "TSC")
control.TnamesN <- c("TSC-2D", "TO","TO", "TSC-2D", "TSC-2D", "TO-EVT", "TO", "TO")
compare.TnamesN <- c("TSC-3D", "TSC-3D","TO-EVT", "TSC-EVT", "TSC-SCT", "TSC-EVT", "TSC-SCT", "TSC-2D")
control.colors <- c("darkgoldenrod4", "cyan", "cyan", "darkgoldenrod4", "darkgoldenrod4", "chartreuse3", "cyan", "cyan")
compare.colors <- c("magenta", "magenta", "chartreuse3", "firebrick3", "deepskyblue1","firebrick3","deepskyblue1","darkgoldenrod4")
models <- paste0(compare.Tnames, "vs", control.Tnames)
modelsN <- paste0(compare.TnamesN, "vs", control.TnamesN)
message("+--- DESeq analysis and data outputs ---------+")
for(i in 1:length(models)){
## subset the counts matrix
colIndex <- c(grep(paste0("",control.names[i],""), colnames(allLanes_counts)),
grep(paste0("",compare.names[i],""), colnames(allLanes_counts)))
if(i==4){
colIndex1<- grep(paste0("",compare.names[3],""), colnames(allLanes_counts))
colIndex <- colIndex[colIndex%in%colIndex1==F]
}
if(i==6){
colIndex <- grep(paste0("",compare.names[4],""), colnames(allLanes_counts))
}
geneCounts <- allLanes_counts[, colIndex]
geneCounts <- geneCounts[, order(colnames(geneCounts))]
## subset the meta table
meta.tbl.sub <- subset(meta.tbl, origin==control.Tnames[i] | origin==compare.Tnames[i])
meta.tbl.sub <- meta.tbl.sub[order(rownames(meta.tbl.sub)),]
meta.tbl.sub$origin <- as.factor(meta.tbl.sub$origin)
meta.tbl.sub$origin <- relevel(meta.tbl.sub$origin, control.Tnames[i])
## DESeq analysis
dds.ori <- DESeqDataSetFromMatrix(countData=geneCounts, colData=meta.tbl.sub, design=~origin)
dds.ori <- DESeq(dds.ori, parallel=TRUE)
vsd.ori <- vst(dds.ori, blind=F)
colData(vsd.ori)
dds.collapse <- collapseReplicates(dds.ori, dds.ori$sampleName, renameCols = TRUE)
dds.collapse <- DESeq(dds.collapse, parallel=TRUE)
resultsNames(dds.collapse)
design(dds.collapse)
colData(dds.collapse)
vsd.collapse <- vst(dds.collapse, blind=F)
## DEGs list
res <- lfcShrink(dds=dds.collapse, coef=2, type="apeglm", parallel=TRUE)
res.sig <- as.data.frame(subset(res, abs(log2FoldChange) >= l2fc & padj < significance))
res.sig$external_gene_name <- rownames(res.sig)
res.sig.ann <- merge(res.sig, ensEMBL2id, by = "external_gene_name")
numsig <- dim(res.sig)[1]
numsigE <- length(unique(res.sig.ann$external_gene_name))
write.csv(res.sig, file = paste0(baseDir, "/reverse_stranded_Analysis/",Project, "-", models[i], "_l2fc1_p0.05_N", numsig, ".csv"),
row.names=F, quote=T)
## save RData for recalling
save(dds.ori, vsd.ori, dds.collapse, vsd.collapse, res, file = paste0(baseDir, "/reverse_stranded_Analysis/",Project, "-", models[i], "_DESeq_Object.RData"))
}
message("+--- PCA/Volcano/Heatmap plot customised function --------+")
customPCA_fn_group <- function(vsd.ori, vsd.collapse, dds.ori, dds.collapse, TOPNUM, ensEMBL2id, control,
controlN, treatN,ctrlColor,trtColor){
## origin vst
rv.ori <- rowVars(assay(vsd.ori))
select.ori <- order(rv.ori, decreasing = TRUE)[seq_len(min(TOPNUM, length(rv.ori)))]
pca.ori <- prcomp(t(assay(vsd.ori)[select.ori, ]))
pc1var.ori <- round(summary(pca.ori)$importance[2,1]*100, digits=2)
pc2var.ori <- round(summary(pca.ori)$importance[2,2]*100, digits=2)
pc1lab.ori <- paste0("PC1 (",as.character(pc1var.ori),"%)")
pc2lab.ori <- paste0("PC2 (",as.character(pc2var.ori),"%)")
sampleTBL.ori <- as.data.frame(colData(dds.ori))
Group <- ifelse(sampleTBL.ori$origin==control, controlN, treatN)
scores.ori <- data.frame(sampleName=rownames(sampleTBL.ori), pca.ori$x,
individual=sampleTBL.ori$individual,
origin=sampleTBL.ori$origin,
lane=sampleTBL.ori$lane,
Group=Group)
scores.ori$origin <- relevel(scores.ori$origin, paste0("", control,""))
## collapse vst
rv.collapse <- rowVars(assay(vsd.collapse))
select.collapse <- order(rv.collapse, decreasing = TRUE)[seq_len(min(TOPNUM, length(rv.collapse)))]
pca.collapse <- prcomp(t(assay(vsd.collapse)[select.collapse, ]))
pc1var.collapse <- round(summary(pca.collapse)$importance[2,1]*100, digits=2)
pc2var.collapse <- round(summary(pca.collapse)$importance[2,2]*100, digits=2)
pc1lab.collapse <- paste0("PC1 (",as.character(pc1var.collapse),"%)")
pc2lab.collapse <- paste0("PC2 (",as.character(pc2var.collapse),"%)")
sampleTBL.collapse <- as.data.frame(colData(dds.collapse))
Group <- ifelse(sampleTBL.collapse$origin==control, controlN, treatN)
scores.collapse <- data.frame(sampleName=rownames(sampleTBL.collapse), pca.collapse$x,
individual=sampleTBL.collapse$individual,
origin=as.factor(sampleTBL.collapse$origin),
Group=Group)
scores.collapse$origin <- relevel(scores.collapse$origin, paste0("", control,""))
plt.ori <- ggplot(scores.ori, aes(PC1, PC2, color=Group, fill=Group, label=individual, shape=lane)) +
geom_mark_ellipse(aes(fill = NULL, color=paste0(Group), group=paste0(Group), label=Group),alpha=0.1,
label.fontsize=10,label.buffer = unit(1, 'mm')) +
geom_point(size=2, alpha=0.75) +
scale_shape_manual(name="Lane", values = c(1, 2)) +
scale_color_manual(values=c(ctrlColor, trtColor)) +
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance")) +
coord_fixed() +
theme_bw() +
labs(title = paste0("Origin PCA (Top ", TOPNUM, " Most Variable Genes)"),
subtitle = "2 Sequencing Runs, 4 lanes each") +
theme(text = element_text(size=12),
plot.subtitle=element_text(size=8, face="italic", color="darkgrey"),
aspect.ratio=1, legend.position="none", panel.border = element_blank(),
panel.grid.major = element_blank(),panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"))
plt.collapse <- ggplot(scores.collapse, aes(PC1, PC2, color=Group, fill=Group, label=individual)) +
geom_mark_ellipse(aes(fill = NULL, color=paste0(Group), group=paste0(Group), label=Group),alpha=0.1,
label.fontsize=10,label.buffer = unit(1, 'mm')) +
geom_point(size=2, alpha=0.75) +
scale_color_manual(values=c(ctrlColor, trtColor)) +
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance")) +
coord_fixed() +
theme_bw() +
labs(title = paste0("Collapse PCA (Top ", TOPNUM, " Most Variable Genes)"),
subtitle = "2 Sequencing Runs, 4 lanes each") +
theme(text = element_text(size=12),
plot.subtitle=element_text(size=8, face="italic", color="darkgrey"),
legend.title = element_blank(),
legend.key = element_rect(fill = "white",colour = "white"),
legend.position='none',
aspect.ratio=1,
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"))
plt.collapse.rmL <- ggplot(scores.collapse, aes(PC1, PC2, color=Group, fill=Group)) +
geom_mark_ellipse(aes(fill = NULL, color=paste0(Group), group=paste0(Group), label=Group),alpha=0.1,
label.fontsize=10,label.buffer = unit(1, 'mm')) +
geom_point(size=2, alpha=0.75) +
scale_color_manual(values=c(ctrlColor, trtColor)) +
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance")) +
labs(title = paste0("Collapse PCA (Top ", TOPNUM, " Most Variable Genes)")) +
coord_fixed() +
theme_bw() +
theme(text = element_text(size=12),
plot.subtitle=element_text(size=8, face="italic", color="darkgrey"),
legend.title = element_blank(),
legend.key = element_rect(fill = "white",colour = "white"),
legend.position='right',
aspect.ratio=1,
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"))
loadings <- as.data.frame(pca.collapse$rotation)
loadings$external_gene_name <- rownames(loadings)
loadings <- merge(loadings, ensEMBL2id, by="external_gene_name")
pca.1 <- loadings[ order(loadings$PC1,decreasing=TRUE), ]
pca.1.25 <- pca.1[c(1:25),]
pca.1.25.plot <- ggplot(data=pca.1.25, aes(x=factor(external_gene_name,levels=unique(external_gene_name)), y=PC1)) +
geom_point(size = 3 ) + xlab("") +
theme_bw() +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
axis.text.x = element_text(angle = 90, hjust = 1, size=8))
pca.2 <- loadings[ order(loadings$PC2,decreasing=TRUE), ]
pca.2.25 <- pca.2[c(1:25),]
pca.2.25.plot <- ggplot(data=pca.2.25, aes(x=factor(external_gene_name,levels=unique(external_gene_name)), y=PC2)) +
geom_point(size = 3 ) + xlab("") +
theme()+
theme_bw() +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
axis.text.x = element_text(angle = 90, hjust = 1, size=8))
return(list(plt.ori, plt.collapse, plt.collapse.rmL, pca.1.25.plot, pca.2.25.plot) )
}
functionPlotDEVolcano <- function(results, sig_cut, logfc_cut, title, xrange, yrange, topN, xlabel, ylabel) {
results <- as.data.frame(results)
results <- results[order(-results$log2FoldChange),]
results$genes <- rownames(results)
results.up <- subset(results, log2FoldChange > logfc_cut & padj<= sig_cut)
results.down <- subset(results, log2FoldChange < logfc_cut & padj<= sig_cut)
volc.plt <- ggplot(data=results, aes(x=log2FoldChange, y=-log10(padj), label=genes)) +
geom_vline(xintercept = logfc_cut, colour="black", linetype = "dashed", alpha=0.5) +
geom_vline(xintercept = -(logfc_cut), colour="black", linetype = "dashed", alpha=0.5) +
geom_hline(yintercept = -log10(sig_cut), colour="black", linetype = "dashed", alpha=0.5) +
geom_point(data=subset(results, abs(log2FoldChange) < logfc_cut | padj > sig_cut), alpha=0.75, size=0.3, colour="grey") +
geom_point(data=subset(results, padj<=sig_cut & log2FoldChange >= logfc_cut), alpha=0.75, size=0.8, colour="red") +
geom_point(data=subset(results, padj<=sig_cut & log2FoldChange <= -(logfc_cut)), alpha=0.75, size=0.8, colour="blue") +
geom_text_repel( data= results.up[order(results.up$padj, -results.up$log2FoldChange),][1:topN,],
show.legend = FALSE, nudge_x=0.1, nudge_y=0.1, segment.size = 0.25, size=3 ) +
geom_text_repel( data= results.down[order(results.down$padj, results.down$log2FoldChange),][1:topN,],
show.legend = FALSE, nudge_x=0.1, nudge_y=0.1, segment.size = 0.25, size=3 ) +
xlab(xlabel) + ylab(ylabel) +
scale_x_continuous(limits=c(xrange[1],xrange[2]), breaks=seq(xrange[1],xrange[2],xrange[3])) +
scale_y_continuous(limits=c(yrange[1],yrange[2]), breaks=seq(yrange[1],yrange[2],yrange[3])) +
theme(aspect.ratio=1) +
ggtitle(title) +
theme_update(plot.title = element_text(size=16, face="bold", hjust=0.5),
axis.title.x = element_text(size=12, face= "bold"),
axis.text.x = element_text(size=12, face="bold"),
axis.title.y.left = element_text(size=12, face= "bold"),
axis.text.y = element_text(size=12, face="bold")) +
theme(panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
panel.background = element_rect(fill = "white", colour = NA))
return(volc.plt)
}
message("+--- PCA/Volcano/Heatmap plot --------+")
for(i in 1:8){
load(paste0(baseDir, "/reverse_stranded_Analysis/",Project, "-", models[i], "_DESeq_Object.RData"))
## PCA for origin and replicates collpase, PC1+PC2
pcaData.ori <- plotPCA(vsd.ori, ntop=TOPNUM, intgroup=c("origin"), returnData=TRUE)
percentVar <- round(100 * attr(pcaData.ori, "percentVar"))
pcaData.collapse <- plotPCA(vsd.collapse, ntop=TOPNUM, intgroup=c("origin"), returnData=TRUE)
percentVar.collapse <- round(100 * attr(pcaData.collapse, "percentVar"))
pcas <- customPCA_fn(vsd.ori, vsd.collapse, dds.ori, dds.collapse, TOPNUM, models[i],
ensEMBL2id, control.Tnames[i])
pdf(paste0(baseDir, "/reverse_stranded_Analysis/", Project, "_", models[i], "_ori_collapse_Fig.PCA.pdf"),width=10,height=10)
par(bg=NA)
plot_grid(pcas[[1]], pcas[[2]], pcas[[3]], pcas[[4]], nrow=2, align="hv")
dev.off()
## Volcano Plot
title <- paste0(compare.Tnames[i], " vs ", control.Tnames[i])
xrange <- c(-15,15,3)
if(i!=3){
yrange<- c(0, 70, 10)
}else{
yrange <-c(0,160,40)
}
topN <- 20
xlabel <- paste0("log2FC", "(", models[i],")")
ylabel <- bquote("-log"[10]~"(adj.p.value)")
plt_volt <- functionPlotDEVolcano(res, significance, l2fc, title, xrange, yrange, 20, xlabel, ylabel)
pdf(paste0(baseDir, "/reverse_stranded_Analysis/", Project, "-", models[i], "_Fig.Volcano.pdf"))
print(plt_volt)
dev.off()
}
message("+--- PCA plot for TSC-3D vs TO,PCA for origin and replicates collpase, PC1+PC2 --------+")
i=2
load(paste0(baseDir, "/reverse_stranded_Analysis/",Project, "-", models[i], "_DESeq_Object.RData"))
pcas <- customPCA_fn_group(vsd.ori, vsd.collapse, dds.ori, dds.collapse, TOPNUM,
ensEMBL2id, control.Tnames[i], control.TnamesN[i], compare.TnamesN[i],
control.colors[i], compare.colors[i])
pdf(paste0(baseDir, "/reverse_stranded_Analysis/SFig3B_", modelsN[i], "_ori_collapse_Fig.PCA.pdf"),width=12,height=10)
par(bg=NA)
plot_grid(pcas[[1]], pcas[[3]], pcas[[2]], pcas[[4]], nrow=2, align="hv")
dev.off()
message("+--- PCA plot for TSC-3D vs TO,Fig S5A ----+")
pdf(paste0(baseDir, "/reverse_stranded_Analysis/SFig3B_", modelsN[i], "_collapse_Fig.PCA.pdf"),width=6,height=10)
par(bg=NA)
plot_grid(pcas[[3]], pcas[[4]], nrow=2)
dev.off()
message("+---------------------------------------------------------------------------------+")
message("+------- Identify specific markers for each model against the others ---+")
message("+---------------------------------------------------------------------------------+")
metatbl.rm.new <- meta.tbl.rm
cellNames <- unique(meta.tbl.rm$origin)
newmodels <- paste0(cellNames, "vsOthers")
for(i in 1:length(cellNames)){
metatbl.rm.new$neworigin <- ifelse(metatbl.rm.new$origin==cellNames[i], cellNames[i], "Control")
dds.rm.new <- DESeqDataSetFromMatrix(countData=allLanes_counts.rm, colData=metatbl.rm.new, design=~neworigin)
dds.rm.new <- DESeq(dds.rm.new, parallel=TRUE)
vsd.rm.new <- vst(dds.rm.new, blind=F)
colData(vsd.rm.new)
# colapseReplicates
dds.collapse.rm.new <- collapseReplicates(dds.rm.new, dds.rm.new$sampleName, renameCols = TRUE)
dds.collapse.rm.new <- DESeq(dds.collapse.rm.new, parallel=TRUE)
resultsNames(dds.collapse.rm.new)
design(dds.collapse.rm.new)
colData(dds.collapse.rm.new)
vsd.collapse.rm.new <- vst(dds.collapse.rm.new, blind=F)
## DEGs list
res <- lfcShrink(dds=dds.collapse.rm.new, coef=2, type="apeglm", parallel=TRUE)
res.sig <- as.data.frame(subset(res, abs(log2FoldChange) >= l2fc & padj < significance))
res.sig$external_gene_name <- rownames(res.sig)
res.sig.ann <- merge(res.sig, ensEMBL2id, by = "external_gene_name")
numsig <- dim(res.sig)[1]
numsigE <- length(unique(res.sig.ann$external_gene_name))
print(numsigE)
write.csv(res.sig, file = paste0(baseDir, "/reverse_stranded_Analysis/",Project, "-", newmodels[i], "_l2fc1_p0.05_N", numsig, ".csv"),
row.names=F, quote=T)
## save RData for recalling
save(dds.rm.new, vsd.rm.new, dds.collapse.rm.new, vsd.collapse.rm.new, res,
file = paste0(baseDir, "/reverse_stranded_Analysis/",Project, "-", newmodels[i], "_DESeq_Object.RData"))
}
message("+---- Generate a heatmap with selected markers and also write csv file for all of the counts ------+")
MononuclearT <- c("LRP5", "GATA2", "GATA3", "KRT7", "TFAP2A", "TFAP2C", "EPCAM", "TEAD4", "TP63",
"ITGA6", "CDH1", "EGFR", "GCM1", "HLA-A", "HLA-B")
SyncytioT <- c("CGA", "CGB", "PSG1", "CSH1", "SDC1", "ERVW-1", "ERVFRD-1", "CD46", "TFRC",
"PGF", "PAPPA2", "GDF15")
ColumnNiche <- c("ITGA2", "PECAM1", "RARRES3", "SOX13", "EMP3", "ITGB6", "NOTCH1")
ExtravillousT <- c("ITGA5", "HLA-G", "MMP2", "CD9", "ITGA1", "LAIR2", "PLAC8", "MCAM", "NCAM1")
library(ComplexHeatmap)
load(paste0(baseDir, "/reverse_stranded_Analysis/",Project, "-OriginalData_DESeq_dds.RData"))
rld.collapse.rm <- rlogTransformation(dds.rm.collapse, blind=F)
vsd.collapse.rm <- vst(dds.rm.collapse, blind=F)
DEGs.rldmat <- as.data.frame(assay(rld.collapse.rm))
DEGs.vsdmat <- as.data.frame(assay(vsd.collapse.rm))
DEGs.rawdat <- counts(dds.rm.collapse, normalized = FALSE)
write.csv(DEGs.rldmat, file = paste0(baseDir, "/reverse_stranded_Analysis/",Project, "-RLDcounts_N33121_rmFluffy.csv"), row.names=T, quote=T)
write.csv(DEGs.rldmat, file = paste0(baseDir, "/reverse_stranded_Analysis/",Project, "-Rawcounts_N33121_rmFluffy.csv"), row.names=T, quote=T)
## prepare the RNASeq heatmap matrix
Allmarkers <- c(MononuclearT, SyncytioT, ColumnNiche, ExtravillousT )
DEGs.rldmat.sel <- DEGs.rldmat[rownames(DEGs.rldmat)%in%Allmarkers==T, ]
## PCAM1
TSC.mean <- rowMeans(DEGs.rldmat.sel[,grep("*_TSC", colnames(DEGs.rldmat.sel))])
TSC3D.mean <- rowMeans(DEGs.rldmat.sel[,grep("*_Okae_TOM", colnames(DEGs.rldmat.sel))])
TSCEVT.mean <- rowMeans(DEGs.rldmat.sel[,grep("*_EVT", colnames(DEGs.rldmat.sel))[1:5]])
TSCSTB.mean <- rowMeans(DEGs.rldmat.sel[,grep("*_STB", colnames(DEGs.rldmat.sel))])
TrophOrg.mean <- rowMeans(DEGs.rldmat.sel[,grep("*_org_TOM", colnames(DEGs.rldmat.sel))])
TrophOrgEVT.mean <- rowMeans(DEGs.rldmat.sel[,grep("*_org_EVT", colnames(DEGs.rldmat.sel))])
mplt.mat <- cbind(TSC.mean, TSC3D.mean, TSCEVT.mean, TSCSTB.mean, TrophOrg.mean, TrophOrgEVT.mean)
breaksList.deg = seq(0, 21, by = 1)
ScaleCols.deg <- colorRampPalette(colors = c("purple4","white","darkgreen"))(length(breaksList.deg))
cnames <- Allmarkers[Allmarkers %in% rownames(mplt.mat)]
mplt.mat.ord <- mplt.mat[cnames,]
mplt.mat.length <- unlist(lapply(list(MononuclearT, SyncytioT, ColumnNiche, ExtravillousT), function(x) length(x)))
colnames(mplt.mat.ord) <- c("TSC-2D", "TSC-3D", "TSC-EVT", "TSC-STB", "TO", "TO-EVT")
## Fig2B remove HLA-A and HLA-B
mplt.mat.sub <- mplt.mat.ord[-c(14:15),]
mplt.mat.sublen <- c(13,12,7,9)
pht_list.sub = Heatmap(mplt.mat.sub,
col = ScaleCols.deg,
name = "DEGs",
show_row_names=T,
show_column_names = T,
width = unit(6, "cm"),
heatmap_legend_param = list(title = "log2 Normalised \nExpression",
title_position = "topcenter",
title_gp = gpar(fontsize = 8),
labels_gp = gpar(fontsize = 6),
direction = "horizontal",
legend_width = unit(4, "cm")),
cluster_rows = T,
show_row_dend = T,
column_title="",
row_split=factor(rep(c("Mononuclear \nTrophoblast", "Syncytio\nTrophoblast", "Column \nNiche", "Extravillous \nTrophoblast"),
mplt.mat.sublen),level=c("Mononuclear \nTrophoblast", "Syncytio\nTrophoblast", "Column \nNiche", "Extravillous \nTrophoblast")),
row_order = c(1:dim(mplt.mat.sub)[1]),
column_order=factor(c("TSC-2D", "TSC-3D", "TSC-EVT", "TSC-STB", "TO", "TO-EVT"),
levels=c("TSC-2D", "TSC-3D", "TSC-EVT", "TSC-STB", "TO", "TO-EVT")),
column_names_side = "top",
cluster_row_slices = T,
row_title_rot = 90,
row_names_gp = gpar(fontsize = 8),
row_title_gp = gpar(col = "black", fontsize = 8),
column_names_gp = gpar(fontsize = 8),
cluster_columns = T,
column_dend_height = unit(1, "cm")
)
pdf(paste0(baseDir, "/reverse_stranded_Analysis/Fig2B_SelMarkers_Heatmap.pdf"))
draw(pht_list.sub, heatmap_legend_side = "bottom")
dev.off()
message("+------- Heatmap plot for each cell type selected top up-regulated 25 genes ------------------+")
selNum <- 25
selHeat.sigG <- list()
numsigs <- c(113,1037,835,1112,3355,892)
for(i in 1:length(cellNames)){
#load(file = paste0(baseDir, "/reverse_stranded_Analysis/",Project, "-", newmodels[i], "_DESeq_Object.RData"))
dat <- read.csv(file = paste0(baseDir, "/reverse_stranded_Analysis/",Project, "-", newmodels[i], "_l2fc1_p0.05_N", numsigs[i], ".csv"))
dat <- dat[order(-dat$log2FoldChange, -dat$padj), ]
datsub <- dat[1:selNum,]
selHeat.sigG[[i]]<- datsub$external_gene_name
selHeat.sigG
}
load(paste0(baseDir, "/reverse_stranded_Analysis/",Project, "-OriginalData_DESeq_dds.RData"))
rld.collapse.rm <- rlogTransformation(dds.rm.collapse, blind=F)
vsd.collapse.rm <- vst(dds.rm.collapse, blind=F)
DEGs.rldmat <- as.data.frame(assay(rld.collapse.rm))
DEGs.vsdmat <- as.data.frame(assay(vsd.collapse.rm))
groupNames <- c("TSC-EVT", "TSC-3D", "TSC-SCT", "TSC-2D", "TSC-EVT",
"TSC-3D", "TSC-SCT", "TSC-2D", "TSC-EVT", "TSC-3D",
"TSC-SCT", "TSC-2D", "TSC-EVT", "TSC-3D", "TSC-SCT",
"TSC-2D", "TSC-EVT", "TSC-3D", "TSC-SCT", "TSC-2D",
"TO-EVT", "TO", "TO-EVT", "TO", "TO-EVT", "TO", "TO-EVT", "TO")
sampleSN <- unlist(lapply(colnames(DEGs.vsdmat), function(x) strsplit(x, split="_")[[1]][1]))
selHeatmat <- list(); selHeatmatMean <- list()
for(i in 1:6){
selHeatmat[[i]] <- DEGs.vsdmat[rownames(DEGs.vsdmat)%in%selHeat.sigG[[i]], ]
colnames(selHeatmat[[i]]) <- paste0(groupNames, "_", sampleSN)
TSC_EVT <- rowMeans(selHeatmat[[i]][,grep("TSC-EVT_", colnames(selHeatmat[[i]]))])
TO_EVT <- rowMeans(selHeatmat[[i]][,grep("TO-EVT_", colnames(selHeatmat[[i]]))])
TSC_SCT <- rowMeans(selHeatmat[[i]][,grep("TSC-SCT_", colnames(selHeatmat[[i]]))])
TSC_2D <- rowMeans(selHeatmat[[i]][,grep("TSC-2D_", colnames(selHeatmat[[i]]))])
TSC_3D <- rowMeans(selHeatmat[[i]][,grep("TSC-3D_", colnames(selHeatmat[[i]]))])
TO <- rowMeans(selHeatmat[[i]][,grep("TO_", colnames(selHeatmat[[i]]))])
selHeatmatMean[[i]] <- cbind(TSC_EVT, TO_EVT, TSC_SCT, TSC_2D, TSC_3D, TO)
colnames(selHeatmatMean[[i]]) <- c("TSC-EVT", "TO-EVT", "TSC-SCT", "TSC-2D", "TSC-3D", "TO")
}
breaksList.deg = seq(4, 18, by = 1)
ScaleCols.deg <- colorRampPalette(colors = c("purple4","white","darkgreen"))(length(breaksList.deg))
pullheatmat <- rbind(selHeatmat[[6]], selHeatmat[[3]], selHeatmat[[2]], selHeatmat[[4]], selHeatmat[[1]], selHeatmat[[5]])
column.ord <- c(grep("TO_", colnames(pullheatmat)), grep("TSC-SCT", colnames(pullheatmat)), grep("TSC-3D", colnames(pullheatmat)),
grep("TSC-2D", colnames(pullheatmat)), grep("TSC-EVT", colnames(pullheatmat)), grep("TO-EVT", colnames(pullheatmat)))
pullheatmat <- pullheatmat[,column.ord]
ham.names <- unlist(lapply(colnames(pullheatmat), function(x) strsplit(x, "_")[[1]][1]))
ham <- HeatmapAnnotation(Group = ham.names,
col = list(Group = c("TO" = "cyan", "TSC-SCT" = "deepskyblue1", "TSC-3D" = "magenta",
"TSC-2D" = "darkgoldenrod4", "TSC-EVT"= "firebrick3", "TO-EVT"= "chartreuse3")),
annotation_name_side = "left")
allpht_list = Heatmap(as.matrix(pullheatmat),
col = ScaleCols.deg,
name = "Norm.Expr",
show_row_names=T,
show_column_names = T,
width = unit(10, "cm"),
top_annotation=ham,
cluster_rows = T,
show_row_dend = F,
column_title=NULL,
row_split=factor(rep(c("TO", "TSC-SCT", "TSC-3D", "TSC-2D", "TSC-EVT", "TO-EVT"), each=25),
levels=c("TO", "TSC-SCT", "TSC-3D", "TSC-2D", "TSC-EVT", "TO-EVT")),
row_order = c(1:dim(pullheatmat)[1]),
column_names_side = "bottom",
cluster_row_slices = T,
row_title_rot = 90,
row_names_gp = gpar(fontsize = 6),
row_title_gp = gpar(col = "black", fontsize = 8),
column_names_gp = gpar(fontsize =6, col= "black"),
cluster_columns = T,
column_split = rep(c("TO", "TSC-SCT", "TSC-3D", "TSC-2D", "TSC-3EVT", "TO-EVT"), c(4,5,5,5,5,4)),
#column_title_gp = gpar(fontsize =5.5, fill= rainbow(6)),
cluster_column_slices=T
)
pdf(paste0(baseDir, "/reverse_stranded_Analysis/SFig3A_Heatmap_sel25_upreg_new.pdf"), width=8, height=12)
draw(allpht_list, heatmap_legend_side = "right", merge_legend=T)
dev.off()
message("+-------------------------------------------------------------------------------+")
message("+--- GeneOntology Analysis --------+")
message("+-------------------------------------------------------------------------------+")
suppressPackageStartupMessages({
library("clusterProfiler")
library("DOSE")
library("GSEABase")
library("AnnotationHub")
library("org.Hs.eg.db")
library("BiocParallel")
library("gage")
library("gageData")
library("enrichplot")
library("reshape2")
library("enrichplot")
library("ggraph")
library("ggforce")
library("reactome.db")
library("ReactomePA")
library("Biostrings")
})
hub <- AnnotationHub()
query(hub, "Homo_sapiens")
register(MulticoreParam(2))
ensEMBL2id.GO <- getBM(attributes=c('ensembl_gene_id', 'external_gene_name', 'description', "entrezgene_id"), mart = ensembl)
message("+--- Data sorting for clusterProfiler input format --------------+")
sel.column.names <-c("entrezgene_id", "ensembl_gene_id", "external_gene_name", "log2FoldChange")
resdf.ckegg <- list(); resdf.cBP <- list(); resdf.cMF <- list(); resdf.cCC <- list(); resdf.cPathway <- list();
resdf.ego <- list(); resdf.ego.UP <- list(); resdf.ego.DOWN <- list();
resdf.KEGG <- list(); resdf.KEGG.UP <- list(); resdf.KEGG.DOWN <- list()
resdf.gse <- list(); resdf.gse.UP <- list(); resdf.gse.DOWN <- list();
resdf.KEGG.gse <- list(); resdf.KEGG.gse.UP <- list(); resdf.KEGG.gse.DOWN <- list();
for(i in 1:8){
load(paste0(baseDir, "/reverse_stranded_Analysis/",Project, "-", models[i], "_DESeq_Object.RData"))
resdf.dat <- as.data.frame(subset(res, abs(log2FoldChange)>=l2fc & padj < significance))
resdf.dat$external_gene_name <- rownames(resdf.dat)
resdf.ann <- merge(resdf.dat, ensEMBL2id.GO, by = "external_gene_name")
resdf <- resdf.ann[,colnames(resdf.ann)%in%sel.column.names==T]
resdf <- resdf[order(-resdf$log2FoldChange),c(4,3,1,2)]
colnames(resdf) <- c("ENTREZID", "ENSEMBL", "SYMBOL", "L2FC")
resdf <- resdf[-which(duplicated(resdf$SYMBOL)), ]
print(dim(resdf))
print(length(unique(resdf$SYMBOL)))
print(length(unique(resdf$ENTREZID)))
resdf.rmNA <- subset(resdf[!is.na(resdf$ENTREZID),])
resdf.rmNA <- resdf.rmNA %>%
mutate(rank = rank(L2FC, ties.method = "random"))
resdf.list <- resdf.rmNA$L2FC; names(resdf.list) <- resdf.rmNA$ENTREZID
resdf.list <- resdf.list[!duplicated(names(resdf.list))]
## split up/down regulated genes
resdf.up <- subset(resdf, L2FC > 0)
resdf.down <- subset(resdf, L2FC < 0)
resdf.up.rmNA <- subset(resdf.up[!is.na(resdf.up$ENTREZID),])
resdf.down.rmNA <- subset(resdf.down[!is.na(resdf.down$ENTREZID),])
resdf.up.rmNA <- resdf.up.rmNA %>%
mutate(rank = rank(L2FC, ties.method = "random"))
resdf.down.rmNA <- resdf.down.rmNA %>%
mutate(rank = rank(L2FC, ties.method = "random"))
resdf.uplist <- resdf.up.rmNA$L2FC; names(resdf.uplist) <- resdf.up.rmNA$ENTREZID
resdf.uplist <- resdf.uplist[!duplicated(names(resdf.uplist))]
resdf.downlist <- resdf.down.rmNA$L2FC; names(resdf.downlist) <- resdf.down.rmNA$ENTREZID
resdf.downlist <- resdf.downlist[!duplicated(names(resdf.downlist))]
## use CompareCluster to generate the up/down GO/KEGG pathways
resdf.gcsample <- list(X1=unique(resdf.up$ENTREZID[!is.na(resdf.up$ENTREZID)]),
X2=unique(resdf.down$ENTREZID[!is.na(resdf.down$ENTREZID)]))
save(resdf.gcsample, resdf, file = paste0(baseDir, "/reverse_stranded_Analysis/",Project,"-", models[i],"_GOData_list.RData"))
## Compare Up and Down regulated pathways for biological theme comparison
resdf.ckegg[[i]] <- compareCluster(geneCluster = resdf.gcsample, fun = "enrichKEGG",
organism="hsa",pvalueCutoff=0.05)
resdf.cBP[[i]] <- compareCluster(geneCluster = resdf.gcsample, fun = "enrichGO",
OrgDb=org.Hs.eg.db, pvalueCutoff=0.05,
pAdjustMethod="BH", ont="BP", readable=T)
resdf.cMF[[i]] <- compareCluster(geneCluster = resdf.gcsample, fun = "enrichGO",
OrgDb=org.Hs.eg.db,pvalueCutoff=0.05,
pAdjustMethod="BH", ont="MF", readable=T)
resdf.cCC[[i]] <- compareCluster(geneCluster = resdf.gcsample, fun = "enrichGO",
OrgDb=org.Hs.eg.db, pvalueCutoff=0.05,
pAdjustMethod="BH",ont="CC",readable=T)
#
resdf.cPathway[[i]] <- compareCluster(geneCluster = resdf.gcsample, fun = "enrichPathway",
pvalueCutoff=0.05, pAdjustMethod="BH", readable=T)
## enrich overrepresented pathways
resdf.ego[[i]] <- enrichGO(gene = resdf$SYMBOL, OrgDb = org.Hs.eg.db, keyType = 'SYMBOL',
ont = "ALL",pAdjustMethod = "BH",pvalueCutoff = 0.05 )
resdf.ego.UP[[i]] <- enrichGO(gene = resdf.up$SYMBOL, OrgDb = org.Hs.eg.db,keyType= 'SYMBOL',
ont = "ALL",pAdjustMethod = "BH", pvalueCutoff = 0.05 )
resdf.ego.DOWN[[i]] <- enrichGO(gene = resdf.down$SYMBOL, OrgDb = org.Hs.eg.db, keyType = 'SYMBOL',
ont = "ALL", pAdjustMethod = "BH", pvalueCutoff = 0.05 )
resdf.KEGG[[i]] <- enrichKEGG(resdf$ENTREZID, organism = "hsa")
resdf.KEGG[[i]] <- setReadable(resdf.KEGG[[i]], OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
resdf.KEGG.UP[[i]] <- enrichKEGG(resdf.up$ENTREZID, organism = "hsa")
resdf.KEGG.UP[[i]] <- setReadable(resdf.KEGG.UP[[i]], OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
resdf.KEGG.DOWN[[i]] <- enrichKEGG(resdf.down$ENTREZID, organism = "hsa")
resdf.KEGG.DOWN[[i]] <- setReadable(resdf.KEGG.DOWN[[i]], OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
## geneset enrichments
resdf.gse[[i]] <- gseGO(geneList = resdf.list, OrgDb = org.Hs.eg.db, ont = "ALL", nPermSimple = 10000,
minGSSize = 10, maxGSSize = 500, pvalueCutoff = 0.05, verbose = FALSE)
resdf.gse.UP[[i]] <- gseGO(geneList = resdf.uplist, OrgDb = org.Hs.eg.db, ont = "ALL", nPermSimple = 10000,
minGSSize = 10, maxGSSize = 500, pvalueCutoff = 0.05, verbose = FALSE)
resdf.gse.DOWN[[i]] <- gseGO(geneList = resdf.downlist, OrgDb = org.Hs.eg.db, ont = "ALL", nPermSimple = 10000,
minGSSize = 10, maxGSSize = 500, pvalueCutoff = 0.05, verbose = FALSE)
resdf.KEGG.gse[[i]] <- gseKEGG(geneList = resdf.list, organism = "hsa", keyType = "kegg",nPermSimple = 10000,
minGSSize = 10, maxGSSize = 800, pvalueCutoff = 0.05, pAdjustMethod = "BH")
resdf.KEGG.gse.UP[[i]] <- gseKEGG(geneList = resdf.uplist, organism = "hsa", keyType = "kegg",nPermSimple = 10000,
minGSSize = 10, maxGSSize = 800, pvalueCutoff = 0.05, pAdjustMethod = "BH")
resdf.KEGG.gse.DOWN[[i]] <- gseKEGG(geneList = resdf.downlist, organism = "hsa", keyType = "kegg", nPermSimple = 10000,
minGSSize = 10, maxGSSize = 800, pvalueCutoff = 0.05, pAdjustMethod = "BH")
}
save(resdf.ckegg, resdf.cBP, resdf.cMF, resdf.cCC, resdf.cPathway, resdf.ego, resdf.ego.UP, resdf.ego.DOWN,
resdf.KEGG, resdf.KEGG.UP, resdf.KEGG.DOWN, resdf.gse, resdf.gse.UP, resdf.gse.DOWN, resdf.KEGG.gse,
resdf.KEGG.gse.UP, resdf.KEGG.gse.DOWN,
file=paste0(baseDir, "/reverse_stranded_Analysis/",Project, "-GO_output_PathwayList_New_July.RData"))
message("+------------ TSC-3D vs TO GeneOntology semantic similarity Analysis -----------------------+")
message("+-- semantic similarity among GO terms rrvgo package (1.2.0), esp BP ---------------------------+")
message("+Reduce and visualize lists of Gene Ontology terms by identifying redudance based on semantic similarity.+")
library("pathview")
library("clusterProfiler")
library("enrichplot")
library("ggplot2")
library("org.Hs.eg.db")
library("rrvgo")
library("GOSemSim")
load(file=paste0(baseDir, "/reverse_stranded_Analysis/",Project, "-GO_output_PathwayList_New_July.RData"))
GOSeSimdata.BP <- godata(OrgDb = "org.Hs.eg.db", keytype = "ENTREZID", ont="BP", computeIC = TRUE)
GOSemSim_Data_function <- function(goterm, index, gofun, model, goName, threshcut, Project, max.overlap){
ego.all <- goterm[[index]]
ego.dat <- as.data.frame(ego.all)
ego.dat <- subset(ego.dat, ONTOLOGY==gofun)
ego.dat.dim <- dim(ego.dat)[1]
simMatrix <- calculateSimMatrix(ego.dat$ID, orgdb="org.Hs.eg.db", ont=gofun, semdata=GOSeSimdata.BP, method="Rel")
scores <- setNames(-log10(ego.dat$qvalue), ego.dat$ID)
reducedTerms <- reduceSimMatrix(simMatrix, scores, threshold=threshcut, orgdb="org.Hs.eg.db")
maxclust <- max(reducedTerms$cluster)
message("+--------- Supplementary data for Fig. 4A --------------+")
write.csv(reducedTerms, file = paste0(baseDir, "/reverse_stranded_Analysis/",Project, "-", model, "_GOSeSim_", goName, "_", gofun, "_N", ego.dat.dim, "_thresh_", threshcut, "_cluster", maxclust, "_summaryTable.csv"), row.names=F)
save(reducedTerms, simMatrix, file = paste0(baseDir, "/reverse_stranded_Analysis/",Project,"-", model,"_GOSeSim_", goName, "_", gofun, ".RData"))
options(ggrepel.max.overlaps = max.overlap)
message("+--------- Supplementary Fig for Fig. 4A --------------+")
pdf(paste0(baseDir, "/reverse_stranded_Analysis/",Project,"-", model, "_GOSeSim_", goName, "_", gofun, "_", ego.dat.dim, "_thresh_", threshcut, "_cluster", maxclust, "_scatterPlot.pdf"))
scatterPlot(simMatrix, reducedTerms, size="score", labelSize = 2)
dev.off()
}
allBP <- GOSemSim_Data_function(resdf.ego, 2, "BP", models[2], "allBP", 0.9, Project, 160)
BP.UP <- GOSemSim_Data_function(resdf.ego.UP, 2, "BP", models[2], "BPUP", 0.9, Project, 160)
BP.DW <- GOSemSim_Data_function(resdf.ego.DOWN, 2, "BP", models[2], "BPDW", 0.9, Project, 50)
message("+----------- Regenerate Fig.3A with big font size (biological pathway, reactome) -----------------------+")
cPathway <- resdf.cPathway[[2]]
cPathway@compareClusterResult$Cluster <- ifelse(cPathway@compareClusterResult$Cluster=="X1", "Up", "Down")
bold12.text <- element_text(face = "bold", size = 12)
pdf(paste0(baseDir, "/reverse_stranded_Analysis/Fig3A_Reactome_BiologicalPathway_UP36_DW15_DotPlot.pdf"), width= 10 , height=15)
dotplot(cPathway, showCategory = 40, includeAll = T, title ="TSC-3D vs TO", font.size=12) +
theme(axis.text.x = bold12.text, axis.text.y = bold12.text, title =element_text(size=12, face='bold'),plot.title = element_text(hjust=0.5))
dev.off()
message("+--------- Supplementary data for Fig. 3A --------------+")