#source("https://bioconductor.org/biocLite.R")
#biocLite("limma")
#biocLite("edgeR")
#biocLite("Glimma")
#biocLite("Mus.musculus")
files <- c("GSM1545535_10_6_5_11.txt",
  "GSM1545536_9_6_5_11.txt",
  "GSM1545538_purep53.txt",
  "GSM1545539_JMS8-2.txt",
  "GSM1545540_JMS8-3.txt",
  "GSM1545541_JMS8-4.txt",
  "GSM1545542_JMS8-5.txt",
  "GSM1545544_JMS9-P7c.txt",
  "GSM1545545_JMS9-P8c.txt")
read.delim(files[1], nrow=5)
library(limma)
library(edgeR)
x<- readDGE(files,columns=c(1,3))
class(x)
dim(x)
samplenames<- substring(colnames(x),12,nchar(colnames(x)))
samplenames
colnames(x)<-samplenames
group<- as.factor(c("LP","ML","Basal","Basal","ML","LP","Basal","ML","LP"))
x$samples$group<-group
lane<- as.factor(rep(c("L004","L006","L008"),c(3,4,2)))
x$samples$lane<-lane
x$samples
library(Mus.musculus)
geneid <- rownames(x)
genes <- select(Mus.musculus, keys=geneid, columns=c("SYMBOL", "TXCHROM"), keytype="ENTREZID")
dim(genes)
head(genes)
genes <- genes[!duplicated(genes$ENTREZID),]
x$genes <- genes
x
cpm <- cpm(x)
lcpm <- cpm(x, log=TRUE)
table(rowSums(x$counts==0)==9)
keep.exprs <- rowSums(cpm>1)>=3
x <- x[keep.exprs,,keep.lib.sizes=FALSE]
dim(x)
library(RColorBrewer)
nsamples <- ncol(x)
col <- brewer.pal(nsamples, "Paired")
par(mfrow=c(1,2))
plot(density(lcpm[,1]), col=col[1], lwd=2, ylim=c(0,0.21), las=2, main = "", xlab="")
title(main="A. Raw data", xlab = "log=cpm")
abline(v=0, lty=3)
for(i in 2:nsamples){
  den <- density(lcpm[,i])
  lines(den$x, den$y, col=col[i], lwd=2)
}
legend("topright", samplenames, text.col = col, bty="n")
lcpm <- cpm(x, log=TRUE)
plot(density(lcpm[,1]), col=col[1], lwd=2, ylim = c(0,0.21), las=2, main="", xlab="")
title(main="B. Filtered data", xlab = "Log-cpm")
abline(v=0, lty=3)
for(i in 2:nsamples){
  den <- density(lcpm[,1])
  lines(den$x, den$y, col=col[i], lwd=2)
}
legend("topright", samplenames, text.col = col, bty="n")
x <- calcNormFactors(x, method = "TMM")
x$samples$norm.factors
x2 <- x
x2$samples$norm.factors <- 1
x2$counts[,1] <- ceiling(x2$counts[,1]*0.05)
x2$counts[,2] <- x2$counts[,2]*5

par(mfrow=c(1,2))
lcpm <- cpm(x2, log=TRUE)
boxplot(lcpm, las=2, col=col, main="")
title(main="A. Example: Unnormalised data",ylab="Log-cpm")
x2 <- calcNormFactors(x2)
x2$samples$norm.factors
lcpm <- cpm(x2, log=TRUE)
boxplot(lcpm, las=2, col=col, main="")
title(main="B. Example: Normalised data",ylab="Log-cpm")

lcpm <- cpm(x, log=TRUE)
par(mfrow=c(1,2))
col.group <- group
levels(col.group) <- brewer.pal(nlevels(col.group), "Set1")
col.group <- as.character(col.group)
col.lane <- lane
levels(col.lane) <- brewer.pal(nlevels(col.lane), "Set2")
col.lane <- as.character(col.lane)
plotMDS(lcpm, labels=group, col=col.group)
title(main="A. Sample groups")

plotMDS(lcpm, labels=lane, col=col.lane, dim=c(3,4))
title(main="B. Sequencing lanes")

library(Glimma)
glMDSPlot(lcpm, labels=paste(group, lane, sep="_"), groups=x$samples[,c(2,5)],
          launch=FALSE)
design <- model.matrix(~0+group+lane)
colnames(design) <- gsub("group", "", colnames(design))
design

contr.matrix <- makeContrasts(
  BasalvsLP = Basal-LP,
  BasalvsML = Basal - ML,
  LPvsML = LP - ML,
  levels = colnames(design))
contr.matrix

v <- voom(x, design, plot=TRUE)
v

vfit <- lmFit(v, design)
vfit <- contrasts.fit(vfit, contrasts=contr.matrix)
efit <- eBayes(vfit)
plotSA(efit)

summary(decideTests(efit))

tfit <- treat(vfit, lfc=1)
dt <- decideTests(tfit)
summary(dt)

de.common <- which(dt[,1]!=0 & dt[,2]!=0)
length(de.common)

head(tfit$genes$SYMBOL[de.common], n=20)

vennDiagram(dt[,1:2], circle.col=c("turquoise", "salmon"))
write.fit(tfit, dt, file="results.txt")

basal.vs.lp <- topTreat(tfit, coef=1, n=Inf)
basal.vs.ml <- topTreat(tfit, coef=2, n=Inf)
head(basal.vs.lp)

head(basal.vs.ml)

plotMD(tfit, column=1, status=dt[,1], main=colnames(tfit)[1], xlim=c(-8,13))

glMDPlot(tfit, coef=1, status=dt, main=colnames(tfit)[1],
         id.column="ENTREZID", counts=x$counts, groups=group, launch=FALSE)

library(gplots)
basal.vs.lp.topgenes <- basal.vs.lp$ENTREZID[1:100]
i <- which(v$genes$ENTREZID %in% basal.vs.lp.topgenes)
mycol <- colorpanel(1000,"blue","white","red")
heatmap.2(v$E[i,], scale="row",
          labRow=v$genes$SYMBOL[i], labCol=group,
          col=mycol, trace="none", density.info="none",
          margin=c(8,6), lhei=c(2,10), dendrogram="column")