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bulkRNASeq_Tomato_Corada.R
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---
title: "Bonanomi_Bulk"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# set working diretory
prj = 'BonanomiD_1292_RNASeq'
PI = 'Bonanomi'
#setwd(paste("/Users/maurizio.aurora/Dropbox (HSR Global)/WORKSPACE/",
# PI,"/",prj,
# "/7_bioinfo/",
# sep=''))
# load libraries
suppressMessages(library("edgeR"))
suppressMessages(library("data.table"))
suppressMessages(library("DESeq2"))
suppressMessages(library("openxlsx"))
suppressMessages(library("dplyr"))
suppressMessages(library("ggplot2"))
suppressMessages(library("RColorBrewer"))
suppressMessages(library("enrichR"))
suppressMessages(library("grid"))
suppressMessages(library("gridExtra"))
suppressMessages(library('VennDiagram'))
suppressMessages(library("venn"))
suppressMessages(library("cowplot"))
suppressMessages(library("ggpubr"))
suppressMessages(library("viridis"))
suppressMessages(library('pals'))
suppressMessages(library("RColorBrewer"))
suppressMessages(library("wesanderson"))
suppressMessages(library("patchwork"))
suppressMessages(library("magick"))
suppressMessages(library('philentropy'))
suppressMessages(library('IntClust'))
suppressMessages(library('pheatmap'))
suppressMessages(library("assertr"))
suppressMessages(library("remotes"))
suppressMessages(library("GeneOverlap"))
suppressMessages(library('stringr'))
suppressMessages(library("ggrepel"))
suppressMessages(library("sva"))
#setwd("/Users/maurizio.aurora/Dropbox (HSR Global)/WORKSPACE/Bonanomi/Bonanomi_1287_scRNA_injury/7_bioinfo/literature/Corada_embryo")
#Re-analyze Corada's dataset GSE122564 + Our bulkRNAseq KO vs WT Cherry (7DPI)
#Corada M, Orsenigo F, Bhat GP, Conze LL et al.
#Fine-Tuning of Sox17 and Canonical Wnt Coordinates the Permeability Properties of the Blood-Brain Barrier.
#Circ Res 2019 Feb 15;124(4):511-525. PMID: 30591003
#Load Corada's data
test=read.table("GSM3473961_EC-E15.5_WT_1_ReadsPerGene.norm.txt", header = T)
test1=read.table("GSM3473962_EC-E15.5_WT_2_ReadsPerGene.norm.txt", header = T)
test2=read.table("GSM3473963_EC-E15.5_WT_3_ReadsPerGene.norm.txt", header = T)
test3=read.table("GSM3473979_EC-Adult_WT_1_ReadsPerGene.norm.txt", header = T)
test4=read.table("GSM3473980_EC-Adult_WT_2_ReadsPerGene.norm.txt", header = T)
test5=read.table("GSM3473981_EC-Adult_WT_3_ReadsPerGene.norm.txt", header = T)
test$E15.5_WT1 = round(test$TPM)
counts = test[,c("gene_name","gene_id","gene_biotype", "chrom", "strand","txstart","txend","length", "E15.5_WT1")]
counts$E15.5_WT2 = round(test1$TPM)
counts$E15.5_WT3 = round(test2$TPM)
counts$Adult_WT1 = round(test3$TPM)
counts$Adult_WT2 = round(test4$TPM)
counts$Adult_WT3 = round(test5$TPM)
filename_xls <- 'counts_corada.xlsx'
write.xlsx(counts,
file= filename_xls,
row.names = F,
asTable = T)
#Load our data
# setwd("/Users/maurizio.aurora/Dropbox (HSR Global)/WORKSPACE/Bonanomi/Bonanomi_1287_scRNA_injury/7_bioinfo/public_data/Corada_embryo")
# path to the count files (2 seq runs)
filecount_1 = '/Users/maurizio.aurora/Dropbox (HSR Global)/WORKSPACE/Bonanomi/BonanomiD_1292_RNASeq/7_bioinfo/combined_samples_1133_1292/raw_not_corrected_counts_tables_1133_1292/1292/all.counts.gz'
filecount_2 ='/Users/maurizio.aurora/Dropbox (HSR Global)/WORKSPACE/Bonanomi/BonanomiD_1292_RNASeq/7_bioinfo/combined_samples_1133_1292/raw_not_corrected_counts_tables_1133_1292/1133/all.counts.gz'
# Min number of replica in each group
Nreplica= 3
# import metadata
metadata_d = read.xlsx("/Users/maurizio.aurora/Dropbox (HSR Global)/WORKSPACE/Bonanomi/Bonanomi_1287_scRNA_injury/7_bioinfo/public_data/Corada_embryo/metadata_corada.xlsx")
# import Corada's counts
coradas_filecount = read.xlsx("/Users/maurizio.aurora/Dropbox (HSR Global)/WORKSPACE/Bonanomi/Bonanomi_1287_scRNA_injury/7_bioinfo/public_data/Corada_embryo/counts_corada.xlsx")
coradas_filecount$Geneid = coradas_filecount$gene_name
fCounts_corada = coradas_filecount[,c("Geneid", "E15.5_WT1", "E15.5_WT2","E15.5_WT3","Adult_WT1","Adult_WT2","Adult_WT3")]
# import our counts
annotation <- c('GeneID','Chr','Start','End','Strand','Length')
fCounts_1 <- read.delim(file=filecount_1, header=TRUE, check.names = F)
fCounts_2 <- read.delim(file=filecount_2, header=TRUE, check.names = F)
# merge all counts
fCounts_temp <- merge(fCounts_1, fCounts_2, by=c("Geneid","Chr","Start","End","Length","Strand"))
fCounts <- merge(fCounts_temp, fCounts_corada, by=c("Geneid"))
fCountsData_not_adj <- fCounts[
,
-which(
tolower(names(fCounts))
%in%
tolower(annotation))]
fCountsAnnotation <- fCounts[
,
which(
tolower(names(fCounts))
%in%
tolower(annotation))]
geneidColname <- 'Geneid'
geneidIdx <- which(tolower(annotation) %in% tolower(geneidColname))
rownames(fCountsData_not_adj) <- fCounts[[geneidIdx]]
# Reordering counts matrix to have samples ordered as in metadata
# it assumes that the first column in metadata is sample name
fCountsData_not_adj <- fCountsData_not_adj[,match(metadata[,1], colnames(fCountsData_not_adj))]
row.names(metadata_d) = metadata_d$sample
cov1=as.factor(metadata_d$nerve)
cov2=as.factor(metadata_d$dataset)
exp_batch=as.factor(metadata_d$exp)
#covar_mat <- cbind(cov1, cov2, cov3)
covar_mat <- cbind(cov2, cov2)
fCountsData_adjusted_p = fCountsData_not_adj[,row.names(metadata_d)]
# use CombatSeq to correct strong batch effect that was difficult to correct with only DEseq2 covariates in exp design formula.
fCountsData_adjusted_adjusted <- ComBat_seq(as.matrix(fCountsData_adjusted_p), batch=exp_batch, group=NULL, full_mod=FALSE) #combined
fCountsData_adjusted_d=fCountsData_adjusted_adjusted
# save tables as a multisheet excel
SAVE_variable <- list()
filename_xls <- paste('COUNTS_bonanomi_corada',prj,'.xlsx', sep='')
variable2save_names <- c('all_counts', 'expGenes_counts','expGenes_LogCPM', 'expGenes_LogRPKM', 'expGenes_RPKM')
# all_counts
y <- DGEList(counts=fCountsData_adjusted_d, genes = fCountsAnnotation)
SAVE_variable[[variable2save_names[1]]] <- as.data.frame(y$counts)
# expGENES_counts
keep <- rowSums(cpm(y)>1)>=Nreplica
table(keep)
yf <- y[keep,]
SAVE_variable[[variable2save_names[2]]] <- as.data.frame(yf$counts)
#CPM
SAVE_variable[[variable2save_names[3]]] <- as.data.frame(cpm(yf, log=T))
#RPKM log
SAVE_variable[[variable2save_names[4]]] <- as.data.frame(rpkm(yf, log=T, gene.length =yf$genes$Length))
#RPKM not log
SAVE_variable[[variable2save_names[5]]] <- as.data.frame(rpkm(yf, log=F, gene.length =yf$genes$Length))
write.xlsx(SAVE_variable,
file = filename_xls,
row.names = T,
asTable = T,
sheetName =variable2save_names)
y <- DGEList(counts=fCountsData_adjusted_d, genes = fCountsAnnotation)
fCountsRPKM = rpkm(y, log=T, gene.length =y$genes$Length)
keep <- rowSums(cpm(y)>1)>=Nreplica
# make preliminary plots on top 500 most variable genes
#PCA in Fig2 D
yf <- y[keep,]
nrow(yf)
N=500
vary <- apply(fCountsRPKM[keep,],1,var)
vary_s <- sort(vary, decreasing = T)
TOP_N <- names(vary_s[1:N])
yTOP <- y[TOP_N,]
fCountsRPKMTOP <- fCountsRPKM[TOP_N,]
#PCA parameters
pcx = 1
pcy = 2
centering = TRUE
scaling = TRUE
# PCA
pca = prcomp(t(fCountsRPKMTOP), center=centering, scale=scaling)
var = round(matrix(((pca$sdev^2)/(sum(pca$sdev^2))), ncol=1)*100,1)
score = as.data.frame(pca$x)
# plot paramters
xlab = paste("PC", pcx, " (",var[pcx],"%)", sep="")
ylab = paste("PC", pcy, " (",var[pcy],"%)", sep="")
cum = var[pcx]+var[pcy]
names = rownames(pca$x)
score$exp = metadata_d$exp
score$nerve = metadata_d$nerve
score$sampleID = metadata_d$sample
pca<- ggplot(score, aes(x=score[,pcx], y=score[,pcy],
color=nerve))+
geom_label_repel(data= score, aes(x=score[,pcx], y=score[,pcy],
color=nerve, label = ""),
size = 5, box.padding = unit(0.55, "lines"), point.padding = unit(0.55, "lines"),
segment.color = 'grey50') +
geom_point(size= 12)+
labs(x=xlab, y=ylab, title=paste("PC",pcx," vs PC",pcy," scoreplot",sep="")) +
geom_hline(yintercept=0, linetype="dashed", color = "darkgrey") +
geom_vline(xintercept=0, linetype="dashed", color = "darkgrey") +
theme(plot.title = element_text(color="black", size=26, face="bold.italic"),
axis.text.x = element_text(angle = 0, face = "bold", color = "black", size=22, hjust =.5),
axis.title.x = element_text(face = "bold", color = "black", size = 24),
axis.text.y = element_text(angle = 0, face = "bold", color = "black", size=22),
axis.title.y = element_text(face = "bold", color = "black", size = 24),
legend.text = element_text(face = "bold", color = "black", size = 18),
legend.position="right",
panel.background = element_rect(fill = "white",colour = "black", size = 1, linetype = "solid")) +
scale_color_manual(values=c("#33691E", "#FFAB91", 'purple',"#B388FF",'#4C9900'))
#Fig2 D
options(repr.plot.width=13, repr.plot.height=7)
pdf('PCA_top500rpkm_Corada_newcolors_squeezed.pdf', width = 8, height = 10)
pca
dev.off()
#Heatmap in Fig S2 A
#load selected genes
subset = readLines("/Users/maurizio.aurora/Documents/corada_genes.txt")
filtered = fCountsRPKMTOP[rownames(fCountsRPKMTOP) %in% subset ,]
annotation_column <- metadata_d[,2:(dim(metadata_d)[2])]
row.names(annotation_column) <- metadata_d[,1]
annotation_column_ = annotation_column[, c('dataset', 'nerve')]
ann_colors = list(nerve = c( 'adult' = '#33691E',
'crush_d14' = '#FFAB91',
'crush_d7' = 'purple',
'embryo' = '#B388FF',
'intact' = '#4C9900'),
dataset = c('Bonanomi' = '#FFD600','Corada' = '#FF6F00'))
crp <- colorRampPalette(c('dodgerblue4','white','darkred'))
df1 <- annotation_column_ %>%
arrange(factor(nerve, levels = c("intact", "crush_d7", "crush_d14", "embryo", "adult")))
re_filtered <- filtered[, rownames(df1)]
#Fig S2 A
HP <- pheatmap::pheatmap(re_filtered,
scale = 'row',
annotation_col = annotation_column_,
cluster_rows = T,
cluster_cols = F,
annotation_colors = ann_colors,
show_rownames=F,
show_colnames=F,
treeheight_col = 0,
treeheight_row = 0,
display_numbers = F,
border_color=NA,
col=colorRampPalette(rev(brewer.pal(n = 11, name =
"RdYlBu")))(100),
filename = 'Heatmap_rpkm_Corada_filtered.pdf',
width = 5, height = 5)
#metadata
f = "nerve"
seqc_pvalue = 0.01
dgeResults = list()
comparison = list(intact_vs_embryo = c("intact","embryo"),
crush_d14_vs_embryo = c("crush_d14","embryo"),
crush_d7_vs_embryo = c("crush_d7","embryo"),
intact_vs_adult = c("intact","adult"),
crush_d14_vs_adult = c("crush_d14","adult"),
crush_d7_vs_adult = c("crush_d7","adult"),
crush_d14_vs_crush_d7 = c("crush_d14","crush_d7"),
crush_d14_vs_intact = c("crush_d14","intact"),
crush_d7_vs_intact = c("crush_d7","intact"),
embryo_vs_adult = c("embryo","adult")
)
metadata_d[metadata_d$nerve %in% comparison[[1]],]
fCountsData_adjusted_s = fCountsData_adjusted_d[,row.names(metadata_d)]
colnames(fCountsData_adjusted_d)
row.names(metadata_d) = metadata_d$sample
for (i in names(comparison)) {
metadata_s = metadata_d[metadata_d$nerve %in% comparison[[i]],]
fCountsData_adjusted_s = fCountsData_adjusted_d[,row.names(metadata_s)]
dds <- DESeqDataSetFromMatrix(
countData = fCountsData_adjusted_s,
colData = metadata_s,
design = as.formula('~nerve'))
filter <- rowSums(cpm(counts(dds)) >= 1) >= Nreplica
table(filter)
ddsFiltered <- dds[filter,]
dga <- DESeq(
object = ddsFiltered,
test = "Wald",
fitType = "parametric",
betaPrior = FALSE,
minReplicatesForReplace = Inf)
alpha = 0.05
print(paste(comparison[[i]][1],"_vs_",comparison[[i]][2],sep=''))
dgeResults.tmp <- results(dga,
contrast = c(f,comparison[[i]][1],comparison[[i]][2]),
cooksCutoff = Inf,
independentFiltering = TRUE,
alpha = alpha,
pAdjustMethod = "BH")
summary(dgeResults.tmp)
head(dgeResults.tmp$pvalue)
dgeResults[[i]] <- dgeResults.tmp[order(dgeResults.tmp$pvalue, decreasing = F),]
#PCA
vsd <- vst(dga, blind=FALSE)
main.factor = "nerve"
pcaData <- plotPCA(vsd, intgroup=c(main.factor),returnData=TRUE)
pcaData$exp=metadata_s$exp
percentVar <- round(100 * attr(pcaData, "percentVar"))
#ggplot(pcaData, aes(PC1, PC2, color=Plxnd1, shape=exp))
PCA = ggplot(pcaData, aes(PC1, PC2, color=nerve, shape=exp)) +
geom_label_repel(data= pcaData, aes(PC1, PC2, color=nerve, label = str_sub(name,-5,-1)),
size = 6, box.padding = unit(0.55, "lines"), point.padding = unit(0.55, "lines"),
segment.color = 'grey50') +
geom_point(size=6) +
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance")) +
ggtitle(paste("PCA",i)) +
theme(plot.title = element_text(hjust = 0.5)) +
geom_hline(yintercept=0, linetype="dashed", color = "darkgrey") +
geom_vline(xintercept=0, linetype="dashed", color = "darkgrey") +
theme(plot.title = element_text(color="black", size=26, face="bold.italic"),
axis.text.x = element_text(angle = 0, face = "bold", color = "black", size=22, hjust =.5),
axis.title.x = element_text(face = "bold", color = "black", size = 24),
axis.text.y = element_text(angle = 0, face = "bold", color = "black", size=22),
axis.title.y = element_text(face = "bold", color = "black", size = 24),
legend.text = element_text(face = "bold", color = "black", size = 12),
legend.position="right",
panel.background = element_rect(fill = "white",colour = "black", size = 1, linetype = "solid")) +
scale_color_manual(values = c('salmon','grey'))
pdf(paste('pca_',i,'.pdf',sep=''),width=8, height=6)
print(PCA)
dev.off()
seqcUP = row.names(dgeResults[[i]])[dgeResults[[i]]$pvalue <= seqc_pvalue &
!is.na(dgeResults[[i]]$padj)&
dgeResults[[i]]$log2FoldChange > 1]
if (length(seqcUP) > 0) {
# print heatmap
annotation_column <- metadata_s[,2:(dim(metadata_s)[2])]
row.names(annotation_column) <- metadata_s[,1]
options(repr.plot.width=12, repr.plot.height=10)
crp <- colorRampPalette(c('dodgerblue4','white','darkred'))
colors = crp(255)
head(cpm(counts(dga)))
length(seqcUP)
HP <- pheatmap::pheatmap(cpm(counts(dga))[seqcUP,],
scale = 'row',
annotation_col = annotation_column,
cluster_rows = T,
cluster_cols = T,
show_rownames = F,
cutree_cols = 2,
fontsize = 12, fontsize_row = 10, fontsize_col = 14,
display_numbers = F,
col=colors,
filename = paste('Heatmap_seqcUP',i,'.pdf'),
width = 10, height = 11 )
}
}
f = 'DGE_results_Corada'
dir.create(f, showWarnings=TRUE, recursive=TRUE)
lapply(
names(dgeResults),
function(x) write.table(
data.table(
data.frame(dgeResults[[x]]),
keep.rownames=geneidColname),
file.path(f, paste(x, ".tsv", sep="")),
append=F,
row.names=F,
col.names=T,
quote=F,
sep="\t"))
dgeResults_table = list()
dgeResults_table = lapply(
names(dgeResults),
function(x)
data.table(
data.frame(dgeResults[[x]]),
keep.rownames=geneidColname))
names(dgeResults_table) = names(dgeResults)
write.xlsx(dgeResults_table,
file = 'DGE_results_Corada.xlsx',
row.names = F,
asTable = T,
startRow = 1,
sheetName = str_sub(names(dgeResults),1,31))
#plots
n.label = 20
FDR = T
pvalue = 0.01
for (nerve in names(dgeResults)) {
results = as.data.frame(dgeResults[[nerve]])
results$DE = 'unm'
if (!FDR) {
if (length(rownames(results[results$pvalue < pvalue & !is.na(results$padj) & results$log2FoldChange > 1,]))>0) {
results[results$pvalue < pvalue & !is.na(results$padj) & results$log2FoldChange > 1,]$DE = 'up'}
if (length(rownames(results[results$pvalue < pvalue & !is.na(results$padj) & results$log2FoldChange < -1,]))>0) {
results[results$pvalue < pvalue & !is.na(results$padj) & results$log2FoldChange < .1,]$DE = 'down' }
} else {
if (length(rownames(results[results$padj < alpha & !is.na(results$padj) & results$log2FoldChange > 0,]))>0) {
results[results$padj < alpha & !is.na(results$padj) & results$log2FoldChange > 0,]$DE = 'up'}
if (length(rownames(results[results$padj < alpha & !is.na(results$padj) & results$log2FoldChange < 0,]))>0) {
results[results$padj < alpha & !is.na(results$padj) & results$log2FoldChange < 0,]$DE = 'down' }
}
if (length(rownames(results[results$pvalue < pvalue & !is.na(results$padj) & results$log2FoldChange > 1,]))>0) {
results[results$pvalue < pvalue & !is.na(results$padj) & results$log2FoldChange > 1,]$DE = 'SEQCup'}
if (length(rownames(results[results$pvalue < pvalue & !is.na(results$padj) & results$log2FoldChange < -1,]))>0) {
results[results$pvalue < pvalue & !is.na(results$padj) & results$log2FoldChange < -1,]$DE = 'SEQCdown' }
if (length(rownames(results[results$padj < alpha & !is.na(results$padj) & results$log2FoldChange > 0,]))>0) {
results[results$padj < alpha & !is.na(results$padj) & results$log2FoldChange > 0,]$DE = 'FDRup'}
if (length(rownames(results[results$padj < alpha & !is.na(results$padj) & results$log2FoldChange < 0,]))>0) {
results[results$padj < alpha & !is.na(results$padj) & results$log2FoldChange < 0,]$DE = 'FDRdown' }
results$DE <- factor(x = results$DE, levels = c("unm", "FDRdown","FDRup", 'SEQCdown','SEQCup'))
mycolors = c('grey','dodgerblue4','darkred','dodgerblue2','coral'); names(mycolors) = levels(results$DE)
results$DE2 = 'unm'; results[results$DE!='unm',]$DE2 = 'mod'
results$DE2 <- factor(x = results$DE2, levels = c("mod","unm"))
mysize = c(3,2); names(mysize) = levels(results$DE2)
myalpha = c(1,0.2); names(mysize) = unique(results$DE2)
# label N genes
N = min(n.label, length(rownames(results[results$DE == 'FDRup',])))
up_label = rownames(results[results$DE == 'FDRup',])[1:N]
N = min(n.label, length(rownames(results[results$DE == 'FDRdown',])))
down_label = rownames(results[results$DE == 'FDRdown',])[1:N]
MAplot = ggplot(results) +
geom_point(aes(x=baseMean, y=log2FoldChange, color = DE, alpha = DE2), size = 3) +
geom_point(data = subset(results, DE2 == 'mod'),
aes(x=baseMean, y=log2FoldChange, color = DE, alpha = DE2), size = 3) +
xlim(c(0,1.e5)) +
scale_x_continuous(trans='log10') +
ggtitle(paste("MAPlot,", nerve)) +
scale_color_manual(values = mycolors) +
#scale_size_manual(values = mysize) +
scale_alpha_manual(values = myalpha) +
geom_hline(yintercept=0, linetype="dashed", color = "darkgrey") +
geom_vline(xintercept=0, linetype="dashed", color = "darkgrey") +
theme(plot.title = element_text(color="black", size=12, face="bold.italic"),
axis.text.x = element_text(angle = 90, face = "bold", color = "black", size=16, hjust =1),
axis.title.x = element_text(face = "bold", color = "black", size = 16),
axis.text.y = element_text(angle = 0, face = "bold", color = "black", size=16),
axis.title.y = element_text(face = "bold", color = "black", size = 16),
legend.text = element_text(face = "bold", color = "black", size = 16),
legend.title = element_text(face = "bold", color = "black", size = 0),
legend.position="right",
panel.background = element_rect(fill = "white",colour = "black", size = 1, linetype = "solid")) +
labs(x = "Mean expression", y = "log2 fold change")
print(MAplot)
pdf(paste(f,'/','MAplot_',nerve,'.pdf',sep=''),width=8, height=6.5)
plot(MAplot)
dev.off()
# Vulcano plot
VP = ggplot(results) +
geom_point(aes(x=log2FoldChange, y=-log10(pvalue), color = DE, alpha = DE2), size =3) +
geom_point(data = subset(results, DE2 == 'mod'),
aes(x=log2FoldChange, y=-log10(pvalue), color = DE, alpha = DE2), size =3) +
ggtitle(paste("Vulcano Plot,", nerve)) +
scale_color_manual(values = mycolors) +
scale_size_manual(values = mysize) +
scale_alpha_manual(values = myalpha) +
geom_hline(yintercept=0, linetype="dashed", color = "darkgrey") +
geom_vline(xintercept=0, linetype="dashed", color = "darkgrey") +
#geom_label_repel(data= results[up_label,], aes(x = log2FoldChange, y = -log10(pvalue), color = DE),
geom_label_repel(data= results[down_label,], aes(x = log2FoldChange, y = -log10(pvalue), color = DE),
#geom_label_repel(data= results["nerve",], aes(x = log2FoldChange, y = -log10(pvalue), color = DE),
label = row.names(results[down_label,]), size = 3,
#label = row.names(results[up_label,]), size = 3,
#label = row.names(results["nerve",]), size = 3,
box.padding = unit(0.35, "lines"), point.padding = unit(0.5, "lines"),segment.color = 'grey50') +
theme(plot.title = element_text(color="black", size=12, face="bold.italic"),
axis.text.x = element_text(angle = 90, face = "bold", color = "black", size=16, hjust =1),
axis.title.x = element_text(face = "bold", color = "black", size = 16),
axis.text.y = element_text(angle = 0, face = "bold", color = "black", size=16),
axis.title.y = element_text(face = "bold", color = "black", size = 16),
legend.text = element_text(face = "bold", color = "black", size = 16),
legend.title = element_text(face = "bold", color = "black", size = 0),
legend.position="right",
panel.background = element_rect(fill = "white",colour = "black", size = 1, linetype = "solid")) +
labs(x = "log2 fold change", y = "-log10 p-value")
#pdf(paste('VulcanoPlot_Up',Plxnd1,'.pdf',sep=''),width=8, height=6.5)
pdf(paste('VulcanoPlot_Dw_',nerve,'.pdf',sep=''),width=8, height=6.5)
plot(VP)
dev.off()
# Vulcano plot
VP0 = ggplot(results) +
geom_point(aes(x=log2FoldChange, y=-log10(pvalue), color = DE, alpha = DE2), size =3) +
geom_point(data = subset(results, DE2 == 'mod'),
aes(x=log2FoldChange, y=-log10(pvalue), color = DE, alpha = DE2), size =3) +
ggtitle(paste("Vulcano Plot,", nerve)) +
scale_color_manual(values = mycolors) +
scale_size_manual(values = mysize) +
scale_alpha_manual(values = myalpha) +
geom_hline(yintercept=0, linetype="dashed", color = "darkgrey") +
geom_vline(xintercept=0, linetype="dashed", color = "darkgrey") +
geom_label_repel(data= results[up_label,], aes(x = log2FoldChange, y = -log10(pvalue), color = DE),
#geom_label_repel(data= results[down_label,], aes(x = log2FoldChange, y = -log10(pvalue), color = DE),
#geom_label_repel(data= results["nerve",], aes(x = log2FoldChange, y = -log10(pvalue), color = DE),
#label = row.names(results[down_label,]), size = 3,
label = row.names(results[up_label,]), size = 3,
#label = row.names(results["nerve",]), size = 3,
box.padding = unit(0.35, "lines"), point.padding = unit(0.5, "lines"),segment.color = 'grey50') +
theme(plot.title = element_text(color="black", size=12, face="bold.italic"),
axis.text.x = element_text(angle = 90, face = "bold", color = "black", size=16, hjust =1),
axis.title.x = element_text(face = "bold", color = "black", size = 16),
axis.text.y = element_text(angle = 0, face = "bold", color = "black", size=16),
axis.title.y = element_text(face = "bold", color = "black", size = 16),
legend.text = element_text(face = "bold", color = "black", size = 16),
legend.title = element_text(face = "bold", color = "black", size = 0),
legend.position="right",
panel.background = element_rect(fill = "white",colour = "black", size = 1, linetype = "solid")) +
labs(x = "log2 fold change", y = "-log10 p-value")
print(VP0)
pdf(paste('VulcanoPlot_Up_',nerve,'.pdf',sep=''),width=8, height=6.5)
plot(VP0)
dev.off()
# Vulcano plot
VP1 = ggplot(results) +
geom_point(aes(x=log2FoldChange, y=-log10(pvalue), color = DE, alpha = DE2), size =3) +
geom_point(data = subset(results, DE2 == 'mod'),
aes(x=log2FoldChange, y=-log10(pvalue), color = DE, alpha = DE2), size =3) +
ggtitle(paste("Vulcano Plot,", nerve)) +
scale_color_manual(values = mycolors) +
scale_size_manual(values = mysize) +
scale_alpha_manual(values = myalpha) +
geom_hline(yintercept=0, linetype="dashed", color = "darkgrey") +
geom_vline(xintercept=0, linetype="dashed", color = "darkgrey") +
geom_label_repel(data= results["nerve",], aes(x = log2FoldChange, y = -log10(pvalue), color = DE),
label = row.names(results["nerve",]), size = 3,
box.padding = unit(0.35, "lines"), point.padding = unit(0.5, "lines"),segment.color = 'grey50') +
theme(plot.title = element_text(color="black", size=12, face="bold.italic"),
axis.text.x = element_text(angle = 90, face = "bold", color = "black", size=16, hjust =1),
axis.title.x = element_text(face = "bold", color = "black", size = 16),
axis.text.y = element_text(angle = 0, face = "bold", color = "black", size=16),
axis.title.y = element_text(face = "bold", color = "black", size = 16),
legend.text = element_text(face = "bold", color = "black", size = 16),
legend.title = element_text(face = "bold", color = "black", size = 0),
legend.position="right",
panel.background = element_rect(fill = "white",colour = "black", size = 1, linetype = "solid")) +
labs(x = "log2 fold change", y = "-log10 p-value")
print(VP1)
pdf(paste('VulcanoPlot_Plnd1_',nerve,'.pdf',sep=''),width=8, height=6.5)
plot(VP1)
dev.off()
options(repr.plot.width=14, repr.plot.height=6.5)
p1 = MAplot ; p2 = VP;
print((p1 + theme(plot.margin = unit(c(0,30,0,0), "pt"))) +
(p2 + theme(plot.margin = unit(c(0,0,0,30), "pt"))) +
plot_layout(guides = "collect"))
pdf(paste(f,'/','MA_VP_',nerve,'.pdf',sep=''),width=16, height=6.5)
print((p1 + theme(plot.margin = unit(c(0,30,0,0), "pt"))) +
(p2 + theme(plot.margin = unit(c(0,0,0,30), "pt"))) +
plot_layout(guides = "collect"))
dev.off()
}
####### FDR ########
fdrUP = list()
alpha = 0.05
fdrUP = lapply(names(dgeResults),
function(x) row.names(dgeResults[[x]])[dgeResults[[x]]$padj <= alpha &
!is.na(dgeResults[[x]]$padj)&
dgeResults[[x]]$log2FoldChange > 0])
names(fdrUP)= names(dgeResults)
fdrDW = list()
fdrDW = lapply(names(dgeResults),
function(x) row.names(dgeResults[[x]])[dgeResults[[x]]$padj <= alpha &
!is.na(dgeResults[[x]]$padj)&
dgeResults[[x]]$log2FoldChange < 0])
names(fdrDW)= names(dgeResults)
####### SEQC ########
seqcUP = list()
pvalue = 0.01
seqcUP = lapply(names(dgeResults),
function(x) row.names(dgeResults[[x]])[dgeResults[[x]]$pvalue <= pvalue &
!is.na(dgeResults[[x]]$padj)&
dgeResults[[x]]$log2FoldChange > 1])
names(seqcUP)= names(dgeResults)
seqcDW = list()
seqcDW = lapply(names(dgeResults),
function(x) row.names(dgeResults[[x]])[dgeResults[[x]]$pvalue <= pvalue &
!is.na(dgeResults[[x]]$padj)&
dgeResults[[x]]$log2FoldChange < -1])
names(seqcDW)= names(dgeResults)
print('FDRup');lengths(fdrUP); print('FDRdw');lengths(fdrDW)
print('SEQCup');lengths(seqcUP); print('SEQCdw');lengths(seqcDW)
print('FDR_tot');lengths(fdrUP) + lengths(fdrDW)
print('SEQC_tot'); lengths(seqcUP) + lengths(seqcDW)