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Castillo-Fernandez_2017.Rmd
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---
title: "Castillo-Fernandez 2017 Reanalysis"
author: "The methylation in ART meta-analysis group"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
fig_width: 7
fig_height: 7
theme: cosmo
---
## Loading packages
```{r,packages}
suppressPackageStartupMessages({
library("R.utils")
library("missMethyl")
library("limma")
library("topconfects")
library("minfi")
library("IlluminaHumanMethylation450kmanifest")
library("RColorBrewer")
library("IlluminaHumanMethylation450kanno.ilmn12.hg19")
library("eulerr")
library("plyr")
library("gplots")
library("reshape2")
library("beeswarm")
library("BSgenome")
library("MEDIPS")
library("DESeq2")
library("mitch")
library("BSgenome.Hsapiens.UCSC.hg19")
library("ensembldb")
library("EnsDb.Hsapiens.v75")
library("GenomicRanges")
})
```
## Annotation
```{r,anno}
names(listTables(EnsDb.Hsapiens.v75))
ensgenes<-genes(EnsDb.Hsapiens.v75)
enstranscripts<-transcripts(EnsDb.Hsapiens.v75)
enspromoters<-promoters(EnsDb.Hsapiens.v75,columns=c("gene_id","gene_name"))
head(enspromoters)
head(ensgenes)
#Curate a bunch of regions that are located near promoters for enrichment analysis
seqnames <- sapply(strsplit(rownames(counts)," "),"[[",1)
seqnames <- gsub("chr","",seqnames)
start <- as.integer(sapply(strsplit(rownames(counts)," "),"[[",2))
end <- as.integer(sapply(strsplit(rownames(counts)," "),"[[",3))
myranges <- GRanges(seqnames=seqnames,ranges=IRanges(start = start , end = end))
ol <- findOverlaps(enspromoters,myranges)
pro <- myranges[subjectHits(ol)]
an <- data.frame(enspromoters[queryHits(ol)])
pro$gene_name <- an$gene_name
pro <- data.frame(pro)
pro$seqnames <- paste("chr",pro$seqnames,sep="")
pro$coord <-paste(pro$seqnames,pro$start,pro$end)
gt <- pro[,c("coord","gene_name")]
#gt$coord <- paste("chr",gt$coord,sep="")
# genesets
download.file("https://reactome.org/download/current/ReactomePathways.gmt.zip", destfile="ReactomePathways.gmt.zip")
unzip("ReactomePathways.gmt.zip")
genesets <- gmt_import("ReactomePathways.gmt")
```
```{r,functions}
#customised beeswarm chart for MEDIP Seq data
make_beeswarm2<- function(dm,name,mx,groups,n=15) {
par(mar=c(3,3,1,1))
NCOLS=5
NROWS=floor(n/NCOLS)
if (n %% NCOLS > 0) { NROWS <- NROWS + 1 }
par(mfrow=c(NROWS, NCOLS))
topgenes <- rownames(head(dm[order(dm$pvalue),],n))
mx<-mx/colSums(mx)*1e6
ss <- mx[which(rownames(mx) %in% topgenes),]
n <- 1:n
g1name=levels(groups)[1]
g2name=levels(groups)[2]
g1dat <- ss[n,which(groups == g1name)]
g2dat <- ss[n,which(groups == g2name)]
g1l <-lapply(split(g1dat, row.names(g1dat)), unlist)
g2l <-lapply(split(g2dat, row.names(g2dat)), unlist)
for (i in n) {
mydat <- list(g1l[[i]],g2l[[i]])
beeswarm(mydat,cex=0.4, pch=19,
las=2, cex.lab=0.6, main=names( g1l )[i] ,
ylab="",labels = c(g1name,g2name))
grid()
}
}
#customised beeswarm chart(confects) for MEDIP Seq data
make_beeswarms_confects2 <- function(confects,name,mx,groups,n=15) {
par(mar=c(3,3,1,1))
NCOLS=5
NROWS=floor(n/NCOLS)
if (n %% NCOLS > 0) { NROWS <- NROWS + 1 }
par(mfrow=c(NROWS, NCOLS))
topgenes <- head(confects$table,n)$name
mx<-mx/colSums(mx)*1e6
ss <- mx[which(rownames(mx) %in% topgenes),]
n <- 1:n
g1name=levels(groups)[1]
g2name=levels(groups)[2]
g1dat <- ss[n,which(groups == g1name)]
g2dat <- ss[n,which(groups == g2name)]
g1l <-lapply(split(g1dat, row.names(g1dat)), unlist)
g2l <-lapply(split(g2dat, row.names(g2dat)), unlist)
for (i in n) {
mydat <- list(g1l[[i]],g2l[[i]])
beeswarm(mydat,cex=0.4, pch=19,
las=2, cex.lab=0.6, main=names( g1l )[i] ,
ylab="",labels = c(g1name,g2name))
grid()
}
}
# Annotation
anno<-function(dge,ensgenes){
dge$chr<-sapply(strsplit(rownames(dge)," "),"[[",1)
dge$chr<-gsub("chr","",dge$chr)
dge$start<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",2))
dge$end<-as.integer(sapply(strsplit(rownames(dge)," "),"[[",3))
sig<-subset(dge,pvalue<.05)
sig_down<-subset(sig,log2FoldChange<0)
sig_up<-subset(sig,log2FoldChange>0)
gr_up<-GRanges(seqnames=sig_up$chr,
ranges=IRanges(start = sig_up$start , end = sig_up$end))
gr_up <- gr_up[which(seqnames(gr_up) %in% seqnames(ensgenes)),]
ol_up<-distanceToNearest(gr_up, ensgenes)
ol_up
gr_up$gene <- ensgenes[subjectHits(ol_up),"symbol"]$symbol
gr_up$distance <- elementMetadata(ol_up)
gr_up
gr_down<-GRanges(seqnames=sig_down$chr,
ranges=IRanges(start = sig_down$start , end = sig_down$end))
gr_down <- gr_down[which(seqnames(gr_down) %in% seqnames(ensgenes)),]
ol_down<-distanceToNearest(gr_down, ensgenes)
ol_down
gr_down$gene <- ensgenes[subjectHits(ol_down),"symbol"]$symbol
gr_down$distance <- elementMetadata(ol_down)
gr_down
list("dge"=dge,"gr_up"=gr_up,"gr_down"=gr_down)
}
# volcano and smear plot
volcanosmear<-function(dge){
sig <- subset(dge,padj<0.05)
SIG = nrow(sig)
DN = nrow(subset(sig,log2FoldChange<0))
UP = nrow(subset(sig,log2FoldChange>0))
HEADER = paste(NAME, SIG , "DMRs,", UP ,"hypermethylated,", DN, "hypomethylated")
# smear plot
plot(log2(dge$baseMean),dge$log2FoldChange,cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 base mean",
ylim=c(-3,3),ylab="log2 fold change"
,pch=19,col="#838383")
points(log2(sig$baseMean),sig$log2FoldChange,cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
#volcano plot
plot(dge$log2FoldChange,-log10(dge$pvalue),cex=0.6,cex.axis=1.2,cex.lab=1.3,
xlab="log2 fold change",
ylab="log10 pvalue"
,pch=19,col="#838383")
points(sig$log2FoldChange,-log10(sig$pvalue),cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.0)
}
#heatmap chart
heatmaps<-function(samplesheet,dge,confects){
mygroups<-as.numeric(samplesheet$groups)
colCols<-gsub("1","yellow",mygroups)
colCols<-gsub("2","orange",colCols)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
dgem<-as.matrix(dge[1:50,7:ncol(dge)])
heatmap.2(dgem,trace="none",scale="row",margin=c(5,5),col=my_palette,
cexRow = 0.5,cexCol = 0.4,ColSideColors =colCols)
rows<-head(confects$table$index,50)
dgem<-as.matrix(dge[rows,7:ncol(dge)])
heatmap.2(dgem,trace="none",scale="row",margin=c(5,5),col=my_palette,
cexRow = 0.5,cexCol = 0.4,ColSideColors =colCols)
}
# custom barplot for 1d mitch
mitch_barplot <- function(res){
sig <- head(res$enrichment_result,30)
sig <- sig[order(sig$s.dist),]
par(mar=c(3,25,1,1)); barplot(sig$s.dist,horiz=TRUE,las=2,cex.names = 0.6,cex.axis = 0.6,
names.arg=sig$set,main="Enrichment score") ;grid()
}
```
## counts
```{r,counts,fig.width=8,fig.height=8}
counts <- read.table("mr.edgeR.c.tsv.gz",sep="\t",header=TRUE,row.names=1)
mycol <- max(grep(".bam.counts",colnames(counts)))
counts <- counts[,1:mycol]
rownames(counts) <- paste(counts$chr,counts$start, counts$stop)
counts[,1:4] = NULL
colnames(counts) <- gsub(".bam.counts","",colnames(counts))
dim(counts)
# exclude X, Y and MT
counts <- counts[grep("chrX",rownames(counts),invert=TRUE),]
counts <- counts[grep("chrY",rownames(counts),invert=TRUE),]
counts <- counts[grep("chrM",rownames(counts),invert=TRUE),]
dim(counts)
# barplot of reads per sample
par(mar=c(5,10,5,3))
barplot(colSums(counts),horiz=TRUE,las=2,main="number of reads per sample",cex.names=0.5)
```
## metadata
```{r,metadata}
sf1<-read.table("metadata/EGAD00001003159_metadata/Sample_File.map",stringsAsFactors = FALSE)
rs1<-read.table("metadata/EGAD00001003159_metadata/Run_Sample_meta_info.map",sep=";",stringsAsFactors = FALSE)
rs1[,ncol(rs1)]=NULL
md1<-apply(rs1,2,function(x){
sapply(strsplit(x,"="),"[[",2)
})
headers<-t(rs1[1,])
colnames(md1)<-sapply(strsplit(headers[,1],"="), "[[",1)
rownames(md1)<-sf1[,1]
md1<-as.data.frame(md1)
sf2<-read.table("metadata/EGAD00001003158_metadata/Sample_File.map",stringsAsFactors = FALSE)
rs2<-read.table("metadata/EGAD00001003158_metadata/Run_Sample_meta_info.map",sep=";",stringsAsFactors = FALSE)
rs2[,ncol(rs2)]=NULL
md2<-apply(rs2,2,function(x){
sapply(strsplit(x,"="),"[[",2)
})
headers<-t(rs2[1,])
colnames(md2)<-sapply(strsplit(headers[,1],"="), "[[",1)
rownames(md2)<-sf2[,1]
md2<-as.data.frame(md2)
md <- as.data.frame(rbind(md1,md2))
mdc <- md[grep("C",rownames(md)),]
mdw <- md[grep("W",rownames(md)),]
## CBMC
## number of natural conception 43
nrow(subset(mdc,medical_help_to_conceive=="no"))
## number of ovarian stimulation is 36
nrow(subset(mdc,ovarian_stimulation=="yes"))
## number of gamete intrafallopian transfer is 2
nrow(subset(mdc,gamete_intrafallopian_transfer=="yes"))
## numberof intracytoplasmic sperm injection is 21
nrow(subset(mdc,intracytoplasmic_sperm_injection=="yes"))
## number of ICSI frozen embryo is 9
nrow(subset(mdc,intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="yes"))
## number of ICSI fresh embryo is 12
nrow(subset(mdc,intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="no"))
## WCB
## number of medical help to conceive is 54
nrow(subset(mdw,medical_help_to_conceive=="no"))
## number of ovarian stimulation is 36
nrow(subset(mdw,ovarian_stimulation=="yes"))
## number of gamete intrafallopian transfer is 2
nrow(subset(mdw,gamete_intrafallopian_transfer=="yes"))
## numberof intracytoplasmic sperm injection is 18
nrow(subset(mdw,intracytoplasmic_sperm_injection=="yes"))
## number of ICSI frozen embryo is 6
nrow(subset(mdw,intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="yes"))
## number of ICSI fresh embryo is 12
nrow(subset(mdw,intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="no"))
```
based on the numbers, following are the contrasts
CBMC and WMBC
* Natural Vs Ovarian stimulation
* Natural Vs GIFT
* Natural Vs ICSI fresh
* Natural Vs ICSI frozen
* ICSI fresh Vs frozen
* Ovarian stimulation Vs GIFT
* Ovarian stimulation Vs ICSI fresh
* Ovarian stimulation Vs ICSI frozen
* GIFT Vs ICSI fresh
* GIFT Vs ICSI frozen
## CBMC: Natural Vs Ovarian stimulation
```{r,dm1,fig.width=8,fig.height=8}
NAME = "CBMC_natural_vs_ovarian_stimulation"
samplesheet<-subset(mdc, medical_help_to_conceive=="no" | ovarian_stimulation=="yes")
samplesheet$groups <- factor(samplesheet$ovarian_stimulation,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dim(y)
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)
z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]
confects <- deseq2_confects(res)
head(confects$table)
volcanosmear(dge)
heatmaps(samplesheet,dge,confects)
#beeswarm charts
mygroups<-samplesheet$groups
mygroups<-gsub("no","nat",mygroups)
mygroups<-gsub("yes","os",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dm1",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dm1",mx=y,groups=mygroups,n=15)
myanno<-anno(dge,ensgenes)
head(myanno$dge)
myanno$gr_up
myanno$gr_down
write.table(dge,file="dm1.tsv",sep="\t",quote=FALSE)
write.table(confects$table,file="dm1confects.tsv",sep="\t",quote=FALSE)
saveRDS(myanno,file="dm1.rds")
# run mitch 1d
dgem <- dge[which(rownames(dge) %in% gt$coord),]
m <- mitch_import(dgem, DEtype="DESeq2",geneTable=gt)
mr <- mitch_calc(m, genesets, priority="significance")
head(mr$enrichment_result,20)
```
## Natural Vs GIFT
```{r,dm2,fig.width=8,fig.height=8}
NAME = "CBMC_natural_vs_gamete_intrafallopian_transfer"
samplesheet<-subset(mdc, medical_help_to_conceive=="no" | gamete_intrafallopian_transfer=="yes")
samplesheet$groups <- factor(samplesheet$gamete_intrafallopian_transfer,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)
z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]
confects <- deseq2_confects(res)
head(confects$table)
volcanosmear(dge)
heatmaps(samplesheet,dge,confects)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","nat",mygroups)
mygroups<-gsub("yes","gift",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dm2",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dm2",mx=y,groups=mygroups,n=15)
myanno<-anno(dge,ensgenes)
head(myanno$dge)
myanno$gr_up
myanno$gr_down
write.table(dge,file="dm2.tsv",sep="\t",quote=FALSE)
write.table(confects$table,file="dm2confects.tsv",sep="\t",quote=FALSE)
saveRDS(myanno,file="dm2.rds")
# run mitch 1d
dgem <- dge[which(rownames(dge) %in% gt$coord),]
m <- mitch_import(dgem, DEtype="DESeq2",geneTable=gt)
mr <- mitch_calc(m, genesets, priority="significance")
head(mr$enrichment_result,20)
```
## Natural Vs ICSI fresh
```{r,dm3,fig.width=8,fig.height=8}
NAME = "CBMC_natural_vs_intracytoplasmic_sperm_injection_fresh_embryo"
samplesheet<-subset(mdc, medical_help_to_conceive=="no" | (intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="no"))
samplesheet$groups <- factor(samplesheet$intracytoplasmic_sperm_injection,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
head(samplesheet)
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)
z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]
confects <- deseq2_confects(res)
head(confects$table)
volcanosmear(dge)
heatmaps(samplesheet,dge,confects)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","nat",mygroups)
mygroups<-gsub("yes","icsi",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dm3",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dm3",mx=y,groups=mygroups,n=15)
myanno<-anno(dge,ensgenes)
head(myanno$dge)
myanno$gr_up
myanno$gr_down
write.table(dge,file="dm3.tsv",sep="\t",quote=FALSE)
write.table(confects$table,file="dm3confects.tsv",sep="\t",quote=FALSE)
saveRDS(myanno,file="dm3.rds")
# run mitch 1d
dgem <- dge[which(rownames(dge) %in% gt$coord),]
m <- mitch_import(dgem, DEtype="DESeq2",geneTable=gt)
mr <- mitch_calc(m, genesets, priority="significance")
head(mr$enrichment_result,20)
```
## Natural Vs ICSI frozen
```{r,dm4,fig.width=8,fig.height=8}
NAME = "CBMC_natural_vs_intracytoplasmic_sperm_injection_frozen_embryo"
samplesheet<-subset(mdc, medical_help_to_conceive=="no" | (intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="yes"))
samplesheet$groups <- factor(samplesheet$intracytoplasmic_sperm_injection,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)
z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]
confects <- deseq2_confects(res)
head(confects$table)
volcanosmear(dge)
heatmaps(samplesheet,dge,confects)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","nat",mygroups)
mygroups<-gsub("yes","icsi.fz",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dm4",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dm4",mx=y,groups=mygroups,n=15)
myanno<-anno(dge,ensgenes)
head(myanno$dge)
myanno$gr_up
myanno$gr_down
write.table(dge,file="dm4.tsv",sep="\t",quote=FALSE)
write.table(confects$table,file="dm4confects.tsv",sep="\t",quote=FALSE)
saveRDS(myanno,file="dm4.rds")
# run mitch 1d
dgem <- dge[which(rownames(dge) %in% gt$coord),]
m <- mitch_import(dgem, DEtype="DESeq2",geneTable=gt)
mr <- mitch_calc(m, genesets, priority="significance")
head(mr$enrichment_result,20)
```
## ICSI fresh Vs ICSI frozen
```{r,dm5,fig.width=8,fig.height=8}
NAME = "CBMC_intracytoplasmic_sperm_injection_fresh_embryo_vs_intracytoplasmic_sperm_injection_frozen_embryo"
samplesheet<-subset(mdc, intracytoplasmic_sperm_injection=="yes")
samplesheet$groups <- factor(samplesheet$frozen_embryo ,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)
z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]
confects <- deseq2_confects(res)
head(confects$table)
volcanosmear(dge)
heatmaps(samplesheet,dge,confects)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","icsi.fh",mygroups)
mygroups<-gsub("yes","icsi.fz",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dm5",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dm5",mx=y,groups=mygroups,n=15)
myanno<-anno(dge,ensgenes)
head(myanno$dge)
myanno$gr_up
myanno$gr_down
write.table(dge,file="dm5.tsv",sep="\t",quote=FALSE)
write.table(confects$table,file="dm5confects.tsv",sep="\t",quote=FALSE)
saveRDS(myanno,file="dm5.rds")
# run mitch 1d
dgem <- dge[which(rownames(dge) %in% gt$coord),]
m <- mitch_import(dgem, DEtype="DESeq2",geneTable=gt)
mr <- mitch_calc(m, genesets, priority="significance")
head(mr$enrichment_result,20)
```
## ovarian stimulation vs GIFT
```{r,dm6,fig.width=8,fig.height=8}
NAME = "CBMC_ovarian_stimulation_vs_gamete_intrafallopian_transfer"
samplesheet<-subset(mdc, ovarian_stimulation=="yes" | gamete_intrafallopian_transfer=="yes")
samplesheet$groups <- factor(samplesheet$gamete_intrafallopian_transfer,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)
z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]
confects <- deseq2_confects(res)
head(confects$table)
volcanosmear(dge)
heatmaps(samplesheet,dge,confects)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","os",mygroups)
mygroups<-gsub("yes","gift",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dm6",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dm6",mx=y,groups=mygroups,n=15)
myanno<-anno(dge,ensgenes)
head(myanno$dge)
myanno$gr_up
myanno$gr_down
write.table(dge,file="dm6.tsv",sep="\t",quote=FALSE)
write.table(confects$table,file="dm6confects.tsv",sep="\t",quote=FALSE)
saveRDS(myanno,file="dm6.rds")
# run mitch 1d
dgem <- dge[which(rownames(dge) %in% gt$coord),]
m <- mitch_import(dgem, DEtype="DESeq2",geneTable=gt)
mr <- mitch_calc(m, genesets, priority="significance")
head(mr$enrichment_result,20)
```
## ovarian stimulation vs ICSI fresh
```{r,dm7,fig.width=8,fig.height=8}
NAME = "CBMC_ovarian_stimulation_vs_intracytoplasmic_sperm_injection_fresh_embryo"
samplesheet<-subset(mdc, ovarian_stimulation=="yes" | (intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="no"))
samplesheet$groups <- factor(samplesheet$intracytoplasmic_sperm_injection,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)
z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]
confects <- deseq2_confects(res)
head(confects$table)
volcanosmear(dge)
heatmaps(samplesheet,dge,confects)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","os",mygroups)
mygroups<-gsub("yes","icsi.fh",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dm7",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dm7",mx=y,groups=mygroups,n=15)
myanno<-anno(dge,ensgenes)
head(myanno$dge)
myanno$gr_up
myanno$gr_down
write.table(dge,file="dm7.tsv",sep="\t",quote=FALSE)
write.table(confects$table,file="dm7confects.tsv",sep="\t",quote=FALSE)
saveRDS(myanno,file="dm7.rds")
# run mitch 1d
dgem <- dge[which(rownames(dge) %in% gt$coord),]
m <- mitch_import(dgem, DEtype="DESeq2",geneTable=gt)
mr <- mitch_calc(m, genesets, priority="significance")
head(mr$enrichment_result,20)
```
## ovarian stimulation vs ICSI frozen
```{r,dm8,fig.width=8,fig.height=8}
NAME = "CBMC_ovarian_stimulation_vs_intracytoplasmic_sperm_injection_frozen_embryo"
samplesheet<-subset(mdc, ovarian_stimulation=="yes" | (intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="yes"))
samplesheet$groups <- factor(samplesheet$intracytoplasmic_sperm_injection,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)
z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]
confects <- deseq2_confects(res)
head(confects$table)
volcanosmear(dge)
heatmaps(samplesheet,dge,confects)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","os",mygroups)
mygroups<-gsub("yes","icsi.fz",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dm8",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dm8",mx=y,groups=mygroups,n=15)
myanno<-anno(dge,ensgenes)
head(myanno$dge)
myanno$gr_up
myanno$gr_down
write.table(dge,file="dm8.tsv",sep="\t",quote=FALSE)
write.table(confects$table,file="dm8confects.tsv",sep="\t",quote=FALSE)
saveRDS(myanno,file="dm8.rds")
# run mitch 1d
dgem <- dge[which(rownames(dge) %in% gt$coord),]
m <- mitch_import(dgem, DEtype="DESeq2",geneTable=gt)
mr <- mitch_calc(m, genesets, priority="significance")
head(mr$enrichment_result,20)
```
## GIFT vs ICSI fresh
```{r,dm9,fig.width=8,fig.height=8}
NAME = "CBMC_gamete_intrafallopian_transfer_vs_intracytoplasmic_sperm_injection_fresh_embryo"
samplesheet<-subset(mdc, gamete_intrafallopian_transfer=="yes" | (intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="no"))
samplesheet$groups <- factor(samplesheet$intracytoplasmic_sperm_injection,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)
z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]
confects <- deseq2_confects(res)
head(confects$table)
volcanosmear(dge)
heatmaps(samplesheet,dge,confects)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","gift",mygroups)
mygroups<-gsub("yes","icsi.fh",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dm9",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dm9",mx=y,groups=mygroups,n=15)
myanno<-anno(dge,ensgenes)
head(myanno$dge)
myanno$gr_up
myanno$gr_down
write.table(dge,file="dm9.tsv",sep="\t",quote=FALSE)
write.table(confects$table,file="dm9confects.tsv",sep="\t",quote=FALSE)
saveRDS(myanno,file="dm9.rds")
# run mitch 1d
dgem <- dge[which(rownames(dge) %in% gt$coord),]
m <- mitch_import(dgem, DEtype="DESeq2",geneTable=gt)
mr <- mitch_calc(m, genesets, priority="significance")
head(mr$enrichment_result,20)
```
## GIFT vs ICSI frozen
```{r,dm10,fig.width=8,fig.height=8}
NAME = "CBMC_gamete_intrafallopian_transfer_vs_intracytoplasmic_sperm_injection_frozen_embryo"
samplesheet<-subset(mdc, gamete_intrafallopian_transfer=="yes" | (intracytoplasmic_sperm_injection=="yes"&frozen_embryo=="yes"))
samplesheet$groups <- factor(samplesheet$intracytoplasmic_sperm_injection,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)
z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]
confects <- deseq2_confects(res)
head(confects$table)
volcanosmear(dge)
heatmaps(samplesheet,dge,confects)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","gift",mygroups)
mygroups<-gsub("yes","icsi.fz",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dm10",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dm10",mx=y,groups=mygroups,n=15)
myanno<-anno(dge,ensgenes)
head(myanno$dge)
myanno$gr_up
myanno$gr_down
write.table(dge,file="dm10.tsv",sep="\t",quote=FALSE)
write.table(confects$table,file="dm10confects.tsv",sep="\t",quote=FALSE)
saveRDS(myanno,file="dm10.rds")
# run mitch 1d
dgem <- dge[which(rownames(dge) %in% gt$coord),]
m <- mitch_import(dgem, DEtype="DESeq2",geneTable=gt)
mr <- mitch_calc(m, genesets, priority="significance")
head(mr$enrichment_result,20)
```
## WMBC
```{r,counts w,fig.width=8,fig.height=8}
counts<-read.table("mr.edgeR.w.tsv.gz",sep="\t",header=TRUE,row.names=1)
mycol<-max(grep(".bam.counts",colnames(counts)))
counts<-counts[,1:mycol]
rownames(counts)<-paste(counts$chr,counts$start, counts$stop)
counts[,1:4]=NULL
colnames(counts)<-gsub(".bam.counts","",colnames(counts))
dim(counts)
# exclude X, Y and MT
counts <- counts[grep("chrX",rownames(counts),invert=TRUE),]
counts <- counts[grep("chrY",rownames(counts),invert=TRUE),]
counts <- counts[grep("chrM",rownames(counts),invert=TRUE),]
dim(counts)
# barplot of reads per sample
par(mar=c(5,10,5,3))
barplot(colSums(counts),horiz=TRUE,las=2,main="number of reads per sample",cex.names=0.5)
```
## Natural Vs Ovarian stimulation
```{r,dmw1,fig.width=8,fig.height=8}
NAME = "WCB_natural_vs_ovarian_stimulation"
samplesheet<-subset(mdw, medical_help_to_conceive=="no" | ovarian_stimulation=="yes")
samplesheet$groups <- factor(samplesheet$ovarian_stimulation,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dim(y)
dim(samplesheet)
colnames(y)
rownames(samplesheet)[which(!rownames(samplesheet)%in%colnames(y))]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)
z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]
confects <- deseq2_confects(res)
head(confects$table)
volcanosmear(dge)
heatmaps(samplesheet,dge,confects)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","nat",mygroups)
mygroups<-gsub("yes","os",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dmw1",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dmw1",mx=y,groups=mygroups,n=15)
myanno<-anno(dge,ensgenes)
head(myanno$dge)
myanno$gr_up
myanno$gr_down
write.table(dge,file="dmw1.tsv",sep="\t",quote=FALSE)
write.table(confects$table,file="dmw1confects.tsv",sep="\t",quote=FALSE)
saveRDS(myanno,file="dmw1.rds")
# run mitch 1d
dgem <- dge[which(rownames(dge) %in% gt$coord),]
m <- mitch_import(dgem, DEtype="DESeq2",geneTable=gt)
mr <- mitch_calc(m, genesets, priority="significance")
head(mr$enrichment_result,20)
```
## Natural Vs GIFT
```{r,dmw2,fig.width=8,fig.height=8}
NAME = "WBC_natural_vs_gamete_intrafallopian_transfer"
samplesheet<-subset(mdw, medical_help_to_conceive=="no" | gamete_intrafallopian_transfer=="yes")
samplesheet$groups <- factor(samplesheet$gamete_intrafallopian_transfer,levels=c("no","yes"))
samplesheet$sex <- factor(samplesheet$gender,levels=c("male","female"))
counts_f<-counts[,colnames(counts) %in% rownames(samplesheet)]
# filter genes with fewer than 10 reads per sample
y <- counts_f[which(rowSums(counts_f)/ncol(counts_f)>=(10)),]
y <- y[,order(colnames(y))]
samplesheet <- samplesheet[order(rownames(samplesheet)),]
dds <- DESeqDataSetFromMatrix(countData = y , colData = samplesheet, design = ~ sex + groups )
res <- DESeq(dds)
z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <-cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:10,1:6]
confects <- deseq2_confects(res)
head(confects$table)
volcanosmear(dge)
heatmaps(samplesheet,dge,confects)
#beeswarm charts
confects <- deseq2_confects(res)
mygroups<-samplesheet$groups
mygroups<-gsub("no","nat",mygroups)
mygroups<-gsub("yes","gift",mygroups)
mygroups<-factor(mygroups)
make_beeswarm2(dm=dge,name="dmw2",mx=y,groups=mygroups,n=15)
make_beeswarms_confects2(confects=confects,name="dmw2",mx=y,groups=mygroups,n=15)
myanno<-anno(dge,ensgenes)
head(myanno$dge)
myanno$gr_up
myanno$gr_down
write.table(dge,file="dmw2.tsv",sep="\t",quote=FALSE)
write.table(confects$table,file="dmw2confects.tsv",sep="\t",quote=FALSE)
saveRDS(myanno,file="dmw2.rds")
# run mitch 1d
dgem <- dge[which(rownames(dge) %in% gt$coord),]
m <- mitch_import(dgem, DEtype="DESeq2",geneTable=gt)
mr <- mitch_calc(m, genesets, priority="significance")
head(mr$enrichment_result,20)
```
## Natural Vs ICSI fresh