SAMD_Chapter.R

## ----setup, include=FALSE------------------------------------------------
require(knitr)
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE, 
                      comment = NA, prompt = TRUE, tidy = FALSE, 
                      fig.width = 7, fig.height = 7, fig_caption = TRUE,
                      cache=FALSE)
Sys.setlocale("LC_TIME", "C")

## ----echo=FALSE----------------------------------------------------------
if(!(require(printr))) {
  install.packages(
    'printr',
    type = 'source',
    repos = c('http://yihui.name/xran', 'http://cran.rstudio.com')
  )
}

## ----dataset, fig.cap="A simplified view of a gene expression matrix", echo=FALSE----
knitr::include_graphics("figures/Figure1.jpg")

## ----MDAProcess, fig.cap="The microarray data analysis process", echo=FALSE----
knitr::include_graphics("figures/Figure2.png")

## ----readPackagesList, results='hide', echo=FALSE------------------------
packages4MDA <- read.csv("packages4MDA.txt")

## ----packagesList, echo=FALSE--------------------------------------------
knitr::kable(
  packages4MDA, booktabs = TRUE,
  caption = 'List of R/Bioconductor packages used in this chapter'
)

## ----CreateFolders, warning=FALSE, eval=FALSE----------------------------
## dir.create("data")
## dir.create("results")

## ----ReadTargets---------------------------------------------------------
targets <- read.csv2("./data/targets.csv", header = TRUE, sep = ";") 
knitr::kable(
  targets, booktabs = TRUE,
  caption = 'Content of the targets file used for the current analysis')

## ----installPackages, message=FALSE, warning=FALSE, eval=FALSE-----------
## install.packages("colorspace")
## install.packages("gplots")
## install.packages("ggplot2")
## install.packages("ggrepel")
## source("http://bioconductor.org/biocLite.R")
## biocLite("oligo")
## biocLite("pd.mogene.2.1.st")
## biocLite("arrayQualityMetrics")
## biocLite("limma")
## biocLite("genefilter")
## biocLite("pvca")
## biocLite("mogene21sttranscriptcluster.db")
## biocLite("annotate")
## biocLite("org.Mm.eg.db")
## biocLite("ReactomePA")

## ----ReadCELfiles, message=FALSE, results='hide', warning=FALSE----------
require(oligo)
celFiles <- list.celfiles("./data", full.names = TRUE)
require(Biobase)
my.targets <-read.AnnotatedDataFrame(file.path("./data","targets.csv"), 
                                     header = TRUE, row.names = 1, 
                                     sep=";") 
rawData <- read.celfiles(celFiles, phenoData = my.targets)

## ----ChangeName----------------------------------------------------------
colnames(rawData) <-rownames(pData(rawData)) <- my.targets@data$ShortName

## ----QCRaw, message=FALSE, warning=FALSE, eval=FALSE---------------------
## require(arrayQualityMetrics)
## arrayQualityMetrics(rawData, outdir = file.path("./results", "QCDir.Raw"), force=TRUE)

## ----QCRawDataRes, fig.cap="Aspect of the summary table, in the index.html file, produced by the arrayQualityMetrics package on the raw data", echo=FALSE----
knitr::include_graphics("figures/Figure3.png")

## ------------------------------------------------------------------------
require(ggplot2)
require(ggrepel)
plotPCA3 <- function (datos, labels, factor, title, scale,colores, size = 1.5, glineas = 0.25) {
  data <- prcomp(t(datos),scale=scale)
  # plot adjustments
  dataDf <- data.frame(data$x)
  Group <- factor
  loads <- round(data$sdev^2/sum(data$sdev^2)*100,1)
  # main plot
  p1 <- ggplot(dataDf,aes(x=PC1, y=PC2)) +
    theme_classic() +
    geom_hline(yintercept = 0, color = "gray70") +
    geom_vline(xintercept = 0, color = "gray70") +
    geom_point(aes(color = Group), alpha = 0.55, size = 3) +
    coord_cartesian(xlim = c(min(data$x[,1])-5,max(data$x[,1])+5)) +
    scale_fill_discrete(name = "Group")
  # avoiding labels superposition
  p1 + geom_text_repel(aes(y = PC2 + 0.25, label = labels),segment.size = 0.25, size = size) + 
    labs(x = c(paste("PC1",loads[1],"%")),y=c(paste("PC2",loads[2],"%"))) +  
    ggtitle(paste("Principal Component Analysis for: ",title,sep=" "))+ 
    theme(plot.title = element_text(hjust = 0.5)) +
    scale_color_manual(values=colores)
  }

## ----PCARaw, message=FALSE, fig.cap="Visualization of the two first Principal Components for raw data"----
plotPCA3(exprs(rawData), labels = targets$ShortName, factor = targets$Group, 
         title="Raw data", scale = FALSE, size = 3, 
         colores = c("red", "blue", "green", "yellow"))

## ----savePCAraw, echo=TRUE, results='hide'-------------------------------
tiff("figures/PCA_RawData.tiff", res = 200, width = 4.5, height = 4, units = 'in')
plotPCA3(exprs(rawData), labels = targets$ShortName, factor = targets$Group, 
         title="Raw data", scale = FALSE, size = 2, 
         colores = c("red", "blue", "green", "yellow"))
dev.off()

## ----BoxplotRaw, message=FALSE, fig.cap="Boxplot for arrays intensities (Raw Data)"----
boxplot(rawData, cex.axis=0.5, las=2,  which="all", 
         col = c(rep("red", 3), rep("blue", 3), rep("green", 3), rep("yellow", 3)),
         main="Distribution of raw intensity values")

## ----saveIntensRaw, echo=FALSE, results='hide'---------------------------
tiff("figures/Intensity_RawData.tiff", res = 200, width = 4, height = 4, units = 'in')
boxplot(rawData, cex.axis=0.5, las=2,  which="all", 
         col = c(rep("red", 3), rep("blue", 3), rep("green", 3), rep("yellow", 3)),
         main="Distribution of raw intensity values")
dev.off()

## ----Normalization-------------------------------------------------------
eset_rma <- rma(rawData)

## ----QCNorm, message=FALSE, warning=FALSE, eval=FALSE--------------------
## arrayQualityMetrics(eset_rma, outdir = file.path("./results", "QCDir.Norm"), force=TRUE)

## ----QCNormDataRes, fig.cap="Aspect of the summary table, in the index.html file, produced by the arrayQualityMetrics package on normalized data", echo=FALSE----
knitr::include_graphics("figures/Figure6.png")

## ----PCANorm, message=FALSE, fig.cap="Visualization of first two principal components for normalized data"----
plotPCA3(exprs(eset_rma), labels = targets$ShortName, factor = targets$Group, 
         title="Normalized data", scale = FALSE, size = 3, 
         colores = c("red", "blue", "green", "yellow"))

## ----savePCAnorm, echo=FALSE, results='hide'-----------------------------
tiff("figures/PCA_NormData.tiff", res = 150, width = 5, height = 5, units = 'in')
plotPCA3(exprs(eset_rma), labels = targets$ShortName, factor = targets$Group, 
         title="Normalized data", scale = FALSE, size = 2, 
         colores = c("red", "blue", "green", "yellow"))
dev.off()

## ----BoxplotNorm, message=FALSE, fig.cap="Distribution of  intensities for normalized data"----
boxplot(eset_rma, cex.axis=0.5, las=2,  which="all", 
         col = c(rep("red", 3), rep("blue", 3), rep("green", 3), rep("yellow", 3)),
         main="Boxplot for arrays intensity: Normalized Data")

## ----saveIntensNorm, echo=FALSE, results='hide'--------------------------
tiff("figures/Intensity_NormData.tiff", res = 150, width = 5, height = 5, units = 'in')
boxplot(eset_rma, cex.axis=0.5, las=2,  which="all", 
         col = c(rep("red", 3), rep("blue", 3), rep("green", 3), rep("yellow", 3)),
         main="Boxplot for arrays intensity: Normalized Data")
dev.off()

## ----BatchDetection, message=FALSE, warning=FALSE------------------------
#load the library
require(pvca)
pData(eset_rma) <- targets
#select the threshold
pct_threshold <- 0.6
#select the factors to analyze
batch.factors <- c("Genotype", "Temperature")
#run the analysis
pvcaObj <- pvcaBatchAssess (eset_rma, batch.factors, pct_threshold)

## ----plotPVCA, fig.cap="Relative importance of the different factors -genotype, temperature and interaction- affecting gene expression"----
#plot the results
bp <- barplot(pvcaObj$dat, xlab = "Effects",
  ylab = "Weighted average proportion variance",
  ylim= c(0,1.1),col = c("mediumorchid"), las=2,
  main="PVCA estimation")
axis(1, at = bp, labels = pvcaObj$label, cex.axis = 0.55, las=2)
values = pvcaObj$dat
new_values = round(values , 3)
text(bp,pvcaObj$dat,labels = new_values, pos=3, cex = 0.5)

## ----savePVCAplot, echo=FALSE, results='hide'----------------------------
tiff("figures/PVCAplot.tiff", res = 150, width = 5, height = 5, units = 'in')
bp <- barplot(pvcaObj$dat, xlab = "Effects",
  ylab = "Weighted average proportion variance",
  ylim= c(0,1.1),col = c("mediumorchid"), las=2,
  main="PVCA estimation")
axis(1, at = bp, labels = pvcaObj$label, cex.axis = 0.45, las=2)
values = pvcaObj$dat
new_values = round(values , 3)
text(bp,pvcaObj$dat,labels = new_values, pos=3, cex = 0.5)
dev.off()

## ----SDplot, fig.cap="Values of standard deviations allong all samples for all genes ordered from smallest to biggest"----
sds <- apply (exprs(eset_rma), 1, sd)
sdsO<- sort(sds)
plot(1:length(sdsO), sdsO, main="Distribution of variability for all genes",
     sub="Vertical lines represent 90% and 95% percentiles",
     xlab="Gene index (from least to most variable)", ylab="Standard deviation")
abline(v=length(sds)*c(0.9,0.95))

## ----saveSDplot, echo=FALSE, results='hide'------------------------------
tiff("figures/SDplot.tiff", res = 150, width = 5, height = 5, units = 'in')
plot(1:length(sdsO), sdsO, main="Distribution of variability for all genes",
     sub="Vertical lines represent 90% and 95% percentiles",
     xlab="Gene index (from least to most variable)", ylab="Standard deviation")
abline(v=length(sds)*c(0.9,0.95))
dev.off()

## ----Filtering1, results='hide', message=FALSE---------------------------
require(genefilter)
require(mogene21sttranscriptcluster.db)
annotation(eset_rma) <- "mogene21sttranscriptcluster.db"
filtered <- nsFilter(eset_rma, 
                     require.entrez = TRUE, remove.dupEntrez = TRUE,
                     var.filter=TRUE, var.func=IQR, var.cutoff=0.75, 
                     filterByQuantile=TRUE, feature.exclude = "^AFFX")

## ----FilterResults1, results='hide', echo=FALSE--------------------------
names(filtered)
class(filtered$eset)

## ----FilterResults2------------------------------------------------------
print(filtered$filter.log)
eset_filtered <-filtered$eset

## ----SaveData1, results='hide', message=FALSE----------------------------
write.csv(exprs(eset_rma), file="./results/normalized.Data.csv")
write.csv(exprs(eset_filtered), file="./results/normalized.Filtered.Data.csv")
save(eset_rma, eset_filtered, file="./results/normalized.Data.Rda")

## ----LoadSavedData-------------------------------------------------------
if (!exists("eset_filtered")) load (file="./results/normalized.Data.Rda")

## ----DesignMatrix, message=FALSE-----------------------------------------
require(limma)
designMat<- model.matrix(~0+Group, pData(eset_filtered))
colnames(designMat) <- c("KO.COLD", "KO.RT", "WT.COLD", "WT.RT")
print(designMat)

## ----. setContrasts------------------------------------------------------
cont.matrix <- makeContrasts (KOvsWT.COLD = KO.COLD-WT.COLD,
                              KOvsWT.RT = KO.RT-WT.RT,
                              INT = (KO.COLD-WT.COLD) - (KO.RT-WT.RT),
                              levels=designMat)
print(cont.matrix)

## ---- linearmodelfit-----------------------------------------------------
require(limma)
fit<-lmFit(eset_filtered, designMat)
fit.main<-contrasts.fit(fit, cont.matrix)
fit.main<-eBayes(fit.main)
class(fit.main)

## ---- topTabs1-----------------------------------------------------------
topTab_KOvsWT.COLD <- topTable (fit.main, number=nrow(fit.main), coef="KOvsWT.COLD", adjust="fdr") 
head(topTab_KOvsWT.COLD)

## ---- topTabs2-----------------------------------------------------------
topTab_KOvsWT.RT <- topTable (fit.main, number=nrow(fit.main), coef="KOvsWT.RT", adjust="fdr") 
head(topTab_KOvsWT.RT)

## ---- topTabs3-----------------------------------------------------------
topTab_INT  <- topTable (fit.main, number=nrow(fit.main), coef="INT", adjust="fdr") 
head(topTab_INT)

## ----GeneAnnotation, message=FALSE, warning=FALSE------------------------
annotatedTopTable <- function(topTab, anotPackage)
{
  topTab <- cbind(PROBEID=rownames(topTab), topTab)
  myProbes <- rownames(topTab)
  thePackage <- eval(parse(text = anotPackage))
  geneAnots <- select(thePackage, myProbes, c("SYMBOL", "ENTREZID", "GENENAME"))
  annotatedTopTab<- merge(x=geneAnots, y=topTab, by.x="PROBEID", by.y="PROBEID")
return(annotatedTopTab)
}

## ----annotateTopTables---------------------------------------------------
topAnnotated_KOvsWT.COLD <- annotatedTopTable(topTab_KOvsWT.COLD,
anotPackage="mogene21sttranscriptcluster.db")
topAnnotated_KOvsWT.RT <- annotatedTopTable(topTab_KOvsWT.RT,
anotPackage="mogene21sttranscriptcluster.db")
topAnnotated_INT <- annotatedTopTable(topTab_INT,
anotPackage="mogene21sttranscriptcluster.db")
write.csv(topAnnotated_KOvsWT.COLD, file="./results/topAnnotated_KOvsWT_COLD.csv")
write.csv(topAnnotated_KOvsWT.RT, file="./results/topAnnotated_KOvsWT_RT.csv")
write.csv(topAnnotated_INT, file="./results/topAnnotated_INT.csv")

## ----annotatedTop, echo=FALSE--------------------------------------------
short <- head(topAnnotated_KOvsWT.COLD[1:5,1:4])
knitr::kable(
  short, booktabs = TRUE,
  caption = 'Annotations added to results "topTable" for the comparison "KOvsWT.COLD"'
)

## ----volcanoPlot, fig.cap="Volcano plot for the comparison between KO and WT in COLD temperature. The names of the top 10 genes (i.e. the first ten genes in the topTable) are shown in the plot"----
require(mogene21sttranscriptcluster.db)
geneSymbols <- select(mogene21sttranscriptcluster.db, rownames(fit.main), c("SYMBOL"))
SYMBOLS<- geneSymbols$SYMBOL
volcanoplot(fit.main, coef=1, highlight=10, names=SYMBOLS, 
            main=paste("Differentially expressed genes", colnames(cont.matrix)[1], sep="\n"))
  abline(v=c(-1,1))


## ----saveVolcanos, echo=FALSE, results='hide'----------------------------
tiff("figures/VolcanoPlot.tiff", res = 150, width = 5, height = 5, units = 'in')
volcanoplot(fit.main, coef=1, highlight=10, names=SYMBOLS, 
            main=paste("Differentially expressed genes", colnames(cont.matrix)[1], sep="\n"))
  abline(v=c(-1,1))

dev.off()

pdf("figures/Volcanos.pdf")
for (i in colnames(cont.matrix)){
  volcanoplot(fit.main, coef=i, highlight=10, names=SYMBOLS,
              main=paste("Differentially expressed genes",i, sep="\n"))
  abline(v=c(-1,1))
}
dev.off()

## ----decideTests.1-------------------------------------------------------
require(limma)
res<-decideTests(fit.main, method="separate", adjust.method="fdr", p.value=0.1, lfc=1)

## ----resumeDecideTests---------------------------------------------------
sum.res.rows<-apply(abs(res),1,sum)
res.selected<-res[sum.res.rows!=0,] 
print(summary(res))

## ---- vennDiagram, fig.cap="Venn diagram showing the genes in common between the three comparisons performed"----
vennDiagram (res.selected[,1:3], cex=0.9)
title("Genes in common between the three comparisons\n Genes selected with FDR < 0.1 and logFC > 1")

## ----vennPlot, echo=FALSE, results='hide'--------------------------------
tiff("figures/VennPlot.tiff", res = 150, width = 5.5, height = 5.5, units = 'in')
vennDiagram (res.selected[,1:3], cex=0.9)
title("Genes in common between the three comparisons\n Genes selected with FDR < 0.1 and logFC > 1")
dev.off()

## ----data4Heatmap--------------------------------------------------------
probesInHeatmap <- rownames(res.selected)
HMdata <- exprs(eset_filtered)[rownames(exprs(eset_filtered)) %in% probesInHeatmap,]

geneSymbols <- select(mogene21sttranscriptcluster.db, rownames(HMdata), c("SYMBOL"))
SYMBOLS<- geneSymbols$SYMBOL
rownames(HMdata) <- SYMBOLS
write.csv(HMdata, file = file.path("./results/data4Heatmap.csv"))

## ----heatmapNoclustering, fig.cap="Heatmap for expression data without any grouping"----
my_palette <- colorRampPalette(c("blue", "red"))(n = 299)
require(gplots)

heatmap.2(HMdata,
          Rowv = FALSE,
          Colv = FALSE,
          main = "Differentially expressed genes \n FDR < 0,1, logFC >=1",
          scale = "row",
          col = my_palette,
          sepcolor = "white",
          sepwidth = c(0.05,0.05),
          cexRow = 0.5,
          cexCol = 0.9,
          key = TRUE,
          keysize = 1.5,
          density.info = "histogram",
          ColSideColors = c(rep("red",3),rep("blue",3), rep("green",3), rep("yellow",3)),
          tracecol = NULL,
          dendrogram = "none",
          srtCol = 30)

## ----heatmapClustering, fig.cap="Heatmap for expression data grouping genes (rows) and samples (columns) by their similarity"----
heatmap.2(HMdata,
          Rowv = TRUE,
          Colv = TRUE,
          dendrogram = "both",
          main = "Differentially expressed genes \n FDR < 0,1, logFC >=1",
          scale = "row",
          col = my_palette,
          sepcolor = "white",
          sepwidth = c(0.05,0.05),
          cexRow = 0.5,
          cexCol = 0.9,
          key = TRUE,
          keysize = 1.5,
          density.info = "histogram",
          ColSideColors = c(rep("red",3),rep("blue",3), rep("green",3), rep("yellow",3)),
          tracecol = NULL,
          srtCol = 30)


## ---- echo=FALSE,  results='hide'----------------------------------------
tiff("figures/Heatmap1.tiff", res = 150, width = 5.5, height = 5.5, units = 'in')
heatmap.2(HMdata,
          Rowv = TRUE,
          Colv = TRUE,
          main = "Differentially expressed genes \n FDR < 0,1, logFC >=1",
          scale = "row",
          col = my_palette,
          sepcolor = "white",
          sepwidth = c(0.05,0.05),
          cexRow = 0.5,
          cexCol = 0.9,
          key = TRUE,
          keysize = 1.5,
          density.info = "histogram",
          ColSideColors = c(rep("red",3),rep("blue",3), rep("green",3), rep("yellow",3)),
          tracecol = NULL,
          dendrogram = "both",
          srtCol = 30)
dev.off()

tiff("figures/Heatmap2.tiff", res = 150, width = 5.5, height = 5.5, units = 'in')
heatmap.2(HMdata,
          Rowv = FALSE,
          Colv = FALSE,
          dendrogram = "none",
          main = "Differentially expressed genes \n FDR < 0,1, logFC >=1",
          scale = "row",
          col = my_palette,
          sepcolor = "white",
          sepwidth = c(0.05,0.05),
          cexRow = 0.5,
          cexCol = 0.9,
          key = TRUE,
          keysize = 1.5,
          density.info = "histogram",
          ColSideColors = c(rep("red",3),rep("blue",3), rep("green",3), rep("yellow",3)),
          tracecol = NULL,
          srtCol = 30)
dev.off()

## ----selectGenes---------------------------------------------------------
listOfTables <- list(KOvsWT.COLD = topTab_KOvsWT.COLD, 
                     KOvsWT.RT  = topTab_KOvsWT.RT, 
                     INT = topTab_INT)
listOfSelected <- list()
for (i in 1:length(listOfTables)){
  # select the toptable
  topTab <- listOfTables[[i]]
  # select the genes to be included in the analysis
  whichGenes<-topTab["adj.P.Val"]<0.15
  selectedIDs <- rownames(topTab)[whichGenes]
  # convert the ID to Entrez
  EntrezIDs<- select(mogene21sttranscriptcluster.db, selectedIDs, c("ENTREZID"))
  EntrezIDs <- EntrezIDs$ENTREZID
  listOfSelected[[i]] <- EntrezIDs
  names(listOfSelected)[i] <- names(listOfTables)[i]
}
sapply(listOfSelected, length)

## ------------------------------------------------------------------------
EntrezUni <- topAnnotated_KOvsWT.COLD$ENTREZID

## ----BiologicalSig-------------------------------------------------------
require(ReactomePA)

listOfData <- listOfSelected[1:2]
comparisonsNames <- names(listOfData)

organisme <- "mouse"
universe <- as.character(EntrezUni)

for (i in 1:length(listOfData)){
  data <- listOfData[[i]]
  genesIn <- listOfSelected[[i]]
  comparison <- comparisonsNames[i]
  enrich.result <- enrichPathway(gene = genesIn,
                                 pvalueCutoff = 0.05,
                                 readable = T,
                                 organism =  organisme,
                                 universe = universe,
                                 minGSSize = 5,
                                 maxGSSize = 500,
                                 pAdjustMethod = "BH")
  
  if (length(rownames(enrich.result@result)) != 0) {
  write.csv(as.data.frame(enrich.result), 
             file =paste0("./results/","ReactomePA.Results.",comparison,".csv"), 
             row.names = FALSE)
  
  pdf(file=paste0("./results/","ReactomePABarplot.",comparison,".pdf"))
    print(barplot(enrich.result, showCategory = 15, font.size = 4, 
            title = paste0("Reactome Pathway Analysis for ", comparison,". Barplot")))
  dev.off()
  
  pdf(file = paste0("./results/","ReactomePAcnetplot.",comparison,".pdf"))
    print(cnetplot(enrich.result, categorySize = "geneNum", schowCategory = 15, 
         vertex.label.cex = 0.75))
  dev.off()
  }
}

## ----tableReacto, echo=FALSE---------------------------------------------
Tab.react <- read.csv2(file.path("./results/ReactomePA.Results.KOvsWT.RT.csv"), 
                       sep = ",", header = TRUE, row.names = 1)

Tab.react <- Tab.react[1:4, 1:5]
knitr::kable(Tab.react, booktabs = TRUE, caption = "First rows and columns for Reactome results on KOvsWT.RT.csv comparison")


## ----listOfFiles, echo=FALSE---------------------------------------------
listOfFiles <- read.table(file="results/listOfFiles.txt", sep="\t", head=T)
knitr::kable(
  listOfFiles, booktabs = TRUE,
  caption = 'List of files generated in the analysis'
)