diff --git a/DESeq2.R b/DESeq2.R new file mode 100644 index 0000000..7af2fc9 --- /dev/null +++ b/DESeq2.R @@ -0,0 +1,574 @@ +## ----setup, echo=FALSE, results="hide"---------------------------------------- +knitr::opts_chunk$set(tidy = FALSE, + cache = FALSE, + dev = "png", + message = FALSE, error = FALSE, warning = TRUE) + +## ----quickStart, eval=FALSE--------------------------------------------------- +# dds <- DESeqDataSetFromMatrix(countData = cts, +# colData = coldata, +# design= ~ batch + condition) +# dds <- DESeq(dds) +# resultsNames(dds) # lists the coefficients +# res <- results(dds, name="condition_trt_vs_untrt") +# # or to shrink log fold changes association with condition: +# res <- lfcShrink(dds, coef="condition_trt_vs_untrt", type="apeglm") + +## ----txiSetup----------------------------------------------------------------- +library("tximport") +library("readr") +library("tximportData") +dir <- system.file("extdata", package="tximportData") +samples <- read.table(file.path(dir,"samples.txt"), header=TRUE) +samples$condition <- factor(rep(c("A","B"),each=3)) +rownames(samples) <- samples$run +samples[,c("pop","center","run","condition")] + +## ----txiFiles----------------------------------------------------------------- +files <- file.path(dir,"salmon", samples$run, "quant.sf.gz") +names(files) <- samples$run +tx2gene <- read_csv(file.path(dir, "tx2gene.gencode.v27.csv")) + +## ----tximport, results="hide"------------------------------------------------- +txi <- tximport(files, type="salmon", tx2gene=tx2gene) + +## ----txi2dds, results="hide"-------------------------------------------------- +library("DESeq2") +ddsTxi <- DESeqDataSetFromTximport(txi, + colData = samples, + design = ~ condition) + +## ----------------------------------------------------------------------------- +coldata <- samples +coldata$files <- files +coldata$names <- coldata$run + +## ----echo=FALSE--------------------------------------------------------------- +library("tximeta") +se <- tximeta(coldata, skipMeta=TRUE) +ddsTxi2 <- DESeqDataSet(se, design = ~condition) + +## ----eval=FALSE--------------------------------------------------------------- +# library("tximeta") +# se <- tximeta(coldata) +# ddsTxi <- DESeqDataSet(se, design = ~ condition) + +## ----loadPasilla-------------------------------------------------------------- +library("pasilla") +pasCts <- system.file("extdata", + "pasilla_gene_counts.tsv", + package="pasilla", mustWork=TRUE) +pasAnno <- system.file("extdata", + "pasilla_sample_annotation.csv", + package="pasilla", mustWork=TRUE) +cts <- as.matrix(read.csv(pasCts,sep="\t",row.names="gene_id")) +coldata <- read.csv(pasAnno, row.names=1) +coldata <- coldata[,c("condition","type")] +coldata$condition <- factor(coldata$condition) +coldata$type <- factor(coldata$type) + +## ----showPasilla-------------------------------------------------------------- +head(cts,2) +coldata + +## ----reorderPasila------------------------------------------------------------ +rownames(coldata) <- sub("fb", "", rownames(coldata)) +all(rownames(coldata) %in% colnames(cts)) +all(rownames(coldata) == colnames(cts)) +cts <- cts[, rownames(coldata)] +all(rownames(coldata) == colnames(cts)) + +## ----matrixInput-------------------------------------------------------------- +library("DESeq2") +dds <- DESeqDataSetFromMatrix(countData = cts, + colData = coldata, + design = ~ condition) +dds + +## ----addFeatureData----------------------------------------------------------- +featureData <- data.frame(gene=rownames(cts)) +mcols(dds) <- DataFrame(mcols(dds), featureData) +mcols(dds) + +## ----htseqDirI, eval=FALSE---------------------------------------------------- +# directory <- "/path/to/your/files/" + +## ----htseqDirII--------------------------------------------------------------- +directory <- system.file("extdata", package="pasilla", + mustWork=TRUE) + +## ----htseqInput--------------------------------------------------------------- +sampleFiles <- grep("treated",list.files(directory),value=TRUE) +sampleCondition <- sub("(.*treated).*","\\1",sampleFiles) +sampleTable <- data.frame(sampleName = sampleFiles, + fileName = sampleFiles, + condition = sampleCondition) +sampleTable$condition <- factor(sampleTable$condition) + +## ----hsteqDds----------------------------------------------------------------- +library("DESeq2") +ddsHTSeq <- DESeqDataSetFromHTSeqCount(sampleTable = sampleTable, + directory = directory, + design= ~ condition) +ddsHTSeq + +## ----loadSumExp--------------------------------------------------------------- +library("airway") +data("airway") +se <- airway + +## ----sumExpInput-------------------------------------------------------------- +library("DESeq2") +ddsSE <- DESeqDataSet(se, design = ~ cell + dex) +ddsSE + +## ----prefilter---------------------------------------------------------------- +keep <- rowSums(counts(dds)) >= 10 +dds <- dds[keep,] + +## ----factorlvl---------------------------------------------------------------- +dds$condition <- factor(dds$condition, levels = c("untreated","treated")) + +## ----relevel------------------------------------------------------------------ +dds$condition <- relevel(dds$condition, ref = "untreated") + +## ----droplevels--------------------------------------------------------------- +dds$condition <- droplevels(dds$condition) + +## ----deseq-------------------------------------------------------------------- +dds <- DESeq(dds) +res <- results(dds) +res + +## ----eval=FALSE--------------------------------------------------------------- +# res <- results(dds, name="condition_treated_vs_untreated") +# res <- results(dds, contrast=c("condition","treated","untreated")) + +## ----lfcShrink---------------------------------------------------------------- +resultsNames(dds) +resLFC <- lfcShrink(dds, coef="condition_treated_vs_untreated", type="apeglm") +resLFC + +## ----parallel, eval=FALSE----------------------------------------------------- +# library("BiocParallel") +# register(MulticoreParam(4)) + +## ----resOrder----------------------------------------------------------------- +resOrdered <- res[order(res$pvalue),] + +## ----sumRes------------------------------------------------------------------- +summary(res) + +## ----sumRes01----------------------------------------------------------------- +sum(res$padj < 0.1, na.rm=TRUE) + +## ----resAlpha05--------------------------------------------------------------- +res05 <- results(dds, alpha=0.05) +summary(res05) +sum(res05$padj < 0.05, na.rm=TRUE) + +## ----IHW, eval=FALSE---------------------------------------------------------- +# # (unevaluated code chunk) +# library("IHW") +# resIHW <- results(dds, filterFun=ihw) +# summary(resIHW) +# sum(resIHW$padj < 0.1, na.rm=TRUE) +# metadata(resIHW)$ihwResult + +## ----MA----------------------------------------------------------------------- +plotMA(res, ylim=c(-2,2)) + +## ----shrunkMA----------------------------------------------------------------- +plotMA(resLFC, ylim=c(-2,2)) + +## ----MAidentify, eval=FALSE--------------------------------------------------- +# idx <- identify(res$baseMean, res$log2FoldChange) +# rownames(res)[idx] + +## ----warning=FALSE------------------------------------------------------------ +resultsNames(dds) +# because we are interested in treated vs untreated, we set 'coef=2' +resNorm <- lfcShrink(dds, coef=2, type="normal") +resAsh <- lfcShrink(dds, coef=2, type="ashr") + +## ----fig.width=8, fig.height=3------------------------------------------------ +par(mfrow=c(1,3), mar=c(4,4,2,1)) +xlim <- c(1,1e5); ylim <- c(-3,3) +plotMA(resLFC, xlim=xlim, ylim=ylim, main="apeglm") +plotMA(resNorm, xlim=xlim, ylim=ylim, main="normal") +plotMA(resAsh, xlim=xlim, ylim=ylim, main="ashr") + +## ----plotCounts--------------------------------------------------------------- +plotCounts(dds, gene=which.min(res$padj), intgroup="condition") + +## ----plotCountsAdv------------------------------------------------------------ +d <- plotCounts(dds, gene=which.min(res$padj), intgroup="condition", + returnData=TRUE) +library("ggplot2") +ggplot(d, aes(x=condition, y=count)) + + geom_point(position=position_jitter(w=0.1,h=0)) + + scale_y_log10(breaks=c(25,100,400)) + +## ----metadata----------------------------------------------------------------- +mcols(res)$description + +## ----export, eval=FALSE------------------------------------------------------- +# write.csv(as.data.frame(resOrdered), +# file="condition_treated_results.csv") + +## ----subset------------------------------------------------------------------- +resSig <- subset(resOrdered, padj < 0.1) +resSig + +## ----multifactor-------------------------------------------------------------- +colData(dds) + +## ----copyMultifactor---------------------------------------------------------- +ddsMF <- dds + +## ----fixLevels---------------------------------------------------------------- +levels(ddsMF$type) +levels(ddsMF$type) <- sub("-.*", "", levels(ddsMF$type)) +levels(ddsMF$type) + +## ----replaceDesign------------------------------------------------------------ +design(ddsMF) <- formula(~ type + condition) +ddsMF <- DESeq(ddsMF) + +## ----multiResults------------------------------------------------------------- +resMF <- results(ddsMF) +head(resMF) + +## ----multiTypeResults--------------------------------------------------------- +resMFType <- results(ddsMF, + contrast=c("type", "single", "paired")) +head(resMFType) + +## ----rlogAndVST--------------------------------------------------------------- +vsd <- vst(dds, blind=FALSE) +rld <- rlog(dds, blind=FALSE) +head(assay(vsd), 3) + +## ----meansd------------------------------------------------------------------- +# this gives log2(n + 1) +ntd <- normTransform(dds) +library("vsn") +meanSdPlot(assay(ntd)) +meanSdPlot(assay(vsd)) +meanSdPlot(assay(rld)) + +## ----heatmap------------------------------------------------------------------ +library("pheatmap") +select <- order(rowMeans(counts(dds,normalized=TRUE)), + decreasing=TRUE)[1:20] +df <- as.data.frame(colData(dds)[,c("condition","type")]) +pheatmap(assay(ntd)[select,], cluster_rows=FALSE, show_rownames=FALSE, + cluster_cols=FALSE, annotation_col=df) +pheatmap(assay(vsd)[select,], cluster_rows=FALSE, show_rownames=FALSE, + cluster_cols=FALSE, annotation_col=df) +pheatmap(assay(rld)[select,], cluster_rows=FALSE, show_rownames=FALSE, + cluster_cols=FALSE, annotation_col=df) + +## ----sampleClust-------------------------------------------------------------- +sampleDists <- dist(t(assay(vsd))) + +## ----figHeatmapSamples, fig.height=4, fig.width=6----------------------------- +library("RColorBrewer") +sampleDistMatrix <- as.matrix(sampleDists) +rownames(sampleDistMatrix) <- paste(vsd$condition, vsd$type, sep="-") +colnames(sampleDistMatrix) <- NULL +colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255) +pheatmap(sampleDistMatrix, + clustering_distance_rows=sampleDists, + clustering_distance_cols=sampleDists, + col=colors) + +## ----figPCA------------------------------------------------------------------- +plotPCA(vsd, intgroup=c("condition", "type")) + +## ----figPCA2------------------------------------------------------------------ +pcaData <- plotPCA(vsd, intgroup=c("condition", "type"), returnData=TRUE) +percentVar <- round(100 * attr(pcaData, "percentVar")) +ggplot(pcaData, aes(PC1, PC2, color=condition, shape=type)) + + geom_point(size=3) + + xlab(paste0("PC1: ",percentVar[1],"% variance")) + + ylab(paste0("PC2: ",percentVar[2],"% variance")) + + coord_fixed() + +## ----WaldTest, eval=FALSE----------------------------------------------------- +# dds <- estimateSizeFactors(dds) +# dds <- estimateDispersions(dds) +# dds <- nbinomWaldTest(dds) + +## ----simpleContrast, eval=FALSE----------------------------------------------- +# results(dds, contrast=c("condition","C","B")) + +## ----combineFactors, eval=FALSE----------------------------------------------- +# dds$group <- factor(paste0(dds$genotype, dds$condition)) +# design(dds) <- ~ group +# dds <- DESeq(dds) +# resultsNames(dds) +# results(dds, contrast=c("group", "IB", "IA")) + +## ----interFig, echo=FALSE, results="hide", fig.height=3----------------------- +npg <- 20 +mu <- 2^c(8,10,9,11,10,12) +cond <- rep(rep(c("A","B"),each=npg),3) +geno <- rep(c("I","II","III"),each=2*npg) +table(cond, geno) +counts <- rnbinom(6*npg, mu=rep(mu,each=npg), size=1/.01) +d <- data.frame(log2c=log2(counts+1), cond, geno) +library("ggplot2") +plotit <- function(d, title) { + ggplot(d, aes(x=cond, y=log2c, group=geno)) + + geom_jitter(size=1.5, position = position_jitter(width=.15)) + + facet_wrap(~ geno) + + stat_summary(fun.y=mean, geom="line", colour="red", size=0.8) + + xlab("condition") + ylab("log2(counts+1)") + ggtitle(title) +} +plotit(d, "Gene 1") + ylim(7,13) +lm(log2c ~ cond + geno + geno:cond, data=d) + +## ----interFig2, echo=FALSE, results="hide", fig.height=3---------------------- +mu[4] <- 2^12 +mu[6] <- 2^8 +counts <- rnbinom(6*npg, mu=rep(mu,each=npg), size=1/.01) +d2 <- data.frame(log2c=log2(counts + 1), cond, geno) +plotit(d2, "Gene 2") + ylim(7,13) +lm(log2c ~ cond + geno + geno:cond, data=d2) + +## ----simpleLRT, eval=FALSE---------------------------------------------------- +# dds <- DESeq(dds, test="LRT", reduced=~1) +# res <- results(dds) + +## ----simpleLRT2, eval=FALSE--------------------------------------------------- +# dds <- DESeq(dds, test="LRT", reduced=~batch) +# res <- results(dds) + +## ----apeThresh---------------------------------------------------------------- +resApeT <- lfcShrink(dds, coef=2, type="apeglm", lfcThreshold=1) +plotMA(resApeT, ylim=c(-3,3), cex=.8) +abline(h=c(-1,1), col="dodgerblue", lwd=2) + +## ----------------------------------------------------------------------------- +condition <- factor(rep(c("A","B","C"),each=2)) +model.matrix(~ condition) +# to compare C vs B, make B the reference level, +# and select the last coefficient +condition <- relevel(condition, "B") +model.matrix(~ condition) + +## ----------------------------------------------------------------------------- +grp <- factor(rep(1:3,each=4)) +cnd <- factor(rep(rep(c("A","B"),each=2),3)) +model.matrix(~ grp + cnd + grp:cnd) +# to compare condition effect in group 3 vs 2, +# make group 2 the reference level, +# and select the last coefficient +grp <- relevel(grp, "2") +model.matrix(~ grp + cnd + grp:cnd) + +## ----------------------------------------------------------------------------- +grp <- factor(rep(1:2,each=4)) +ind <- factor(rep(rep(1:2,each=2),2)) +cnd <- factor(rep(c("A","B"),4)) +model.matrix(~grp + grp:ind + grp:cnd) +# to compare condition effect across group, +# add a main effect for 'cnd', +# and select the last coefficient +model.matrix(~grp + cnd + grp:ind + grp:cnd) + +## ----boxplotCooks------------------------------------------------------------- +par(mar=c(8,5,2,2)) +boxplot(log10(assays(dds)[["cooks"]]), range=0, las=2) + +## ----dispFit------------------------------------------------------------------ +plotDispEsts(dds) + +## ----dispFitCustom------------------------------------------------------------ +ddsCustom <- dds +useForMedian <- mcols(ddsCustom)$dispGeneEst > 1e-7 +medianDisp <- median(mcols(ddsCustom)$dispGeneEst[useForMedian], + na.rm=TRUE) +dispersionFunction(ddsCustom) <- function(mu) medianDisp +ddsCustom <- estimateDispersionsMAP(ddsCustom) + +## ----filtByMean--------------------------------------------------------------- +metadata(res)$alpha +metadata(res)$filterThreshold +plot(metadata(res)$filterNumRej, + type="b", ylab="number of rejections", + xlab="quantiles of filter") +lines(metadata(res)$lo.fit, col="red") +abline(v=metadata(res)$filterTheta) + +## ----noFilt------------------------------------------------------------------- +resNoFilt <- results(dds, independentFiltering=FALSE) +addmargins(table(filtering=(res$padj < .1), + noFiltering=(resNoFilt$padj < .1))) + +## ----lfcThresh---------------------------------------------------------------- +par(mfrow=c(2,2),mar=c(2,2,1,1)) +ylim <- c(-2.5,2.5) +resGA <- results(dds, lfcThreshold=.5, altHypothesis="greaterAbs") +resLA <- results(dds, lfcThreshold=.5, altHypothesis="lessAbs") +resG <- results(dds, lfcThreshold=.5, altHypothesis="greater") +resL <- results(dds, lfcThreshold=.5, altHypothesis="less") +drawLines <- function() abline(h=c(-.5,.5),col="dodgerblue",lwd=2) +plotMA(resGA, ylim=ylim); drawLines() +plotMA(resLA, ylim=ylim); drawLines() +plotMA(resG, ylim=ylim); drawLines() +plotMA(resL, ylim=ylim); drawLines() + +## ----mcols-------------------------------------------------------------------- +mcols(dds,use.names=TRUE)[1:4,1:4] +substr(names(mcols(dds)),1,10) +mcols(mcols(dds), use.names=TRUE)[1:4,] + +## ----muAndCooks--------------------------------------------------------------- +head(assays(dds)[["mu"]]) +head(assays(dds)[["cooks"]]) + +## ----dispersions-------------------------------------------------------------- +head(dispersions(dds)) +head(mcols(dds)$dispersion) + +## ----sizefactors-------------------------------------------------------------- +sizeFactors(dds) + +## ----coef--------------------------------------------------------------------- +head(coef(dds)) + +## ----betaPriorVar------------------------------------------------------------- +attr(dds, "betaPriorVar") + +## ----priorInfo---------------------------------------------------------------- +priorInfo(resLFC) +priorInfo(resNorm) +priorInfo(resAsh) + +## ----dispPriorVar------------------------------------------------------------- +dispersionFunction(dds) +attr(dispersionFunction(dds), "dispPriorVar") + +## ----versionNum--------------------------------------------------------------- +metadata(dds)[["version"]] + +## ----normFactors, eval=FALSE-------------------------------------------------- +# normFactors <- normFactors / exp(rowMeans(log(normFactors))) +# normalizationFactors(dds) <- normFactors + +## ----offsetTransform, eval=FALSE---------------------------------------------- +# cqnOffset <- cqnObject$glm.offset +# cqnNormFactors <- exp(cqnOffset) +# EDASeqNormFactors <- exp(-1 * EDASeqOffset) + +## ----lineardep, echo=FALSE---------------------------------------------------- +DataFrame(batch=factor(c(1,1,2,2)), condition=factor(c("A","A","B","B"))) + +## ----lineardep2, echo=FALSE--------------------------------------------------- +DataFrame(batch=factor(c(1,1,1,1,2,2)), condition=factor(c("A","A","B","B","C","C"))) + +## ----lineardep3, echo=FALSE--------------------------------------------------- +DataFrame(batch=factor(c(1,1,1,2,2,2)), condition=factor(c("A","B","C","A","B","C"))) + +## ----groupeffect-------------------------------------------------------------- +coldata <- DataFrame(grp=factor(rep(c("X","Y"),each=6)), + ind=factor(rep(1:6,each=2)), + cnd=factor(rep(c("A","B"),6))) +coldata + +## ----------------------------------------------------------------------------- +as.data.frame(coldata) + +## ----groupeffect2------------------------------------------------------------- +coldata$ind.n <- factor(rep(rep(1:3,each=2),2)) +as.data.frame(coldata) + +## ----groupeffect3------------------------------------------------------------- +model.matrix(~ grp + grp:ind.n + grp:cnd, coldata) + +## ----groupeffect4, eval=FALSE------------------------------------------------- +# results(dds, contrast=list("grpY.cndB","grpX.cndB")) + +## ----missingcombo------------------------------------------------------------- +group <- factor(rep(1:3,each=6)) +condition <- factor(rep(rep(c("A","B","C"),each=2),3)) +d <- DataFrame(group, condition)[-c(17,18),] +as.data.frame(d) + +## ----missingcombo2------------------------------------------------------------ +m1 <- model.matrix(~ condition*group, d) +colnames(m1) +unname(m1) +all.zero <- apply(m1, 2, function(x) all(x==0)) +all.zero + +## ----missingcombo3------------------------------------------------------------ +idx <- which(all.zero) +m1 <- m1[,-idx] +unname(m1) + +## ----cooksPlot---------------------------------------------------------------- +W <- res$stat +maxCooks <- apply(assays(dds)[["cooks"]],1,max) +idx <- !is.na(W) +plot(rank(W[idx]), maxCooks[idx], xlab="rank of Wald statistic", + ylab="maximum Cook's distance per gene", + ylim=c(0,5), cex=.4, col=rgb(0,0,0,.3)) +m <- ncol(dds) +p <- 3 +abline(h=qf(.99, p, m - p)) + +## ----indFilt------------------------------------------------------------------ +plot(res$baseMean+1, -log10(res$pvalue), + log="x", xlab="mean of normalized counts", + ylab=expression(-log[10](pvalue)), + ylim=c(0,30), + cex=.4, col=rgb(0,0,0,.3)) + +## ----histindepfilt------------------------------------------------------------ +use <- res$baseMean > metadata(res)$filterThreshold +h1 <- hist(res$pvalue[!use], breaks=0:50/50, plot=FALSE) +h2 <- hist(res$pvalue[use], breaks=0:50/50, plot=FALSE) +colori <- c(`do not pass`="khaki", `pass`="powderblue") + +## ----fighistindepfilt--------------------------------------------------------- +barplot(height = rbind(h1$counts, h2$counts), beside = FALSE, + col = colori, space = 0, main = "", ylab="frequency") +text(x = c(0, length(h1$counts)), y = 0, label = paste(c(0,1)), + adj = c(0.5,1.7), xpd=NA) +legend("topright", fill=rev(colori), legend=rev(names(colori))) + +## ----vanillaDESeq, eval=FALSE------------------------------------------------- +# dds <- DESeq(dds, minReplicatesForReplace=Inf) +# res <- results(dds, cooksCutoff=FALSE, independentFiltering=FALSE) + +## ---- eval=FALSE-------------------------------------------------------------- +# mat <- assay(vsd) +# mm <- model.matrix(~condition, colData(vsd)) +# mat <- limma::removeBatchEffect(mat, batch=vsd$batch, design=mm) +# assay(vsd) <- mat +# plotPCA(vsd) + +## ----varGroup, echo=FALSE----------------------------------------------------- +set.seed(3) +dds1 <- makeExampleDESeqDataSet(n=1000,m=12,betaSD=.3,dispMeanRel=function(x) 0.01) +dds2 <- makeExampleDESeqDataSet(n=1000,m=12, + betaSD=.3, + interceptMean=mcols(dds1)$trueIntercept, + interceptSD=0, + dispMeanRel=function(x) 0.2) +dds2 <- dds2[,7:12] +dds2$condition <- rep("C",6) +mcols(dds2) <- NULL +dds12 <- cbind(dds1, dds2) +rld <- rlog(dds12, blind=FALSE, fitType="mean") +plotPCA(rld) + +## ----convertNA, eval=FALSE---------------------------------------------------- +# res$padj <- ifelse(is.na(res$padj), 1, res$padj) + +## ----sessionInfo-------------------------------------------------------------- +sessionInfo() + diff --git a/R_workshop.2.Rmd b/R_workshop.2.Rmd index 9f66509..18ea3e5 100644 --- a/R_workshop.2.Rmd +++ b/R_workshop.2.Rmd @@ -2,8 +2,8 @@ title: "R Workshop -- Day 2" author: "Franziska Metge" output: - html_document: default pdf_document: default + html_document: default --- @@ -239,8 +239,10 @@ library(pheatmap) ### Bioconductor ```{r eval = F} -source("https://bioconductor.org/biocLite.R") -biocLite("edgeR") +if (!requireNamespace("BiocManager", quietly = TRUE)) + install.packages("BiocManager", repos='http://cran.us.r-project.org' , dependencies = TRUE) + +BiocManager::install("edgeR") library(edgeR) ``` @@ -325,8 +327,8 @@ Resources to help you choose colors for your plot: - `plot(..., log = 'xy', ...)` ```{r, eval = TRUE, echo = FALSE, collapse = TRUE, warning = FALSE} -airway.D <- read.delim2('data/airway_gene_expression.tsv', as.is = T, header = T) -airway.I <- read.delim2('data/airway_sample_info.tsv', as.is = T, header = T) +airway.D = read.delim2('data/airway_gene_expression.tsv', as.is = T, header = T) +airway.I = read.delim2('data/airway_sample_info.tsv', as.is = T, header = T) print('head(airway.D)') head(airway.D) print('head(airway.I)') @@ -368,14 +370,14 @@ Not exactly how most volcano plots look like, but this nicely represents how the ### Hands On Work on the airway dataset ```{r, collapse = T, echo = TRUE} -airway.ge <- read.delim("data/airway_gene_expression.tsv", as.is = T, header = T, sep = '\t') +airway.ge = read.delim("data/airway_gene_expression.tsv", as.is = T, header = T, sep = '\t') # make Gene ID to row names head(airway.ge, n = 5) ``` -* Remove low abundant `rowMeans < 1` genes from the airway data +* Remove low abundant `rowMeans < 5` genes from the airway data - calculate the `RM = rowMeans()` using columns `2:9` of `airway.ge` - - subset `airway.ge` using `subset(airway.ge, RM > 1)` + - subset `airway.ge` using `subset(airway.ge, RM > 5)` * Calculate a p-value for your test data frame - use a `for(i in 1:nrow(expressed.airway)){ calculate p-value for row i}` - calculate p.value using t.test `as.numieric(t.test()$p.value)` @@ -391,7 +393,7 @@ head(airway.ge, n = 5) - add legend using `legend()` ```{r, eval = T, echo = FALSE, collapse=TRUE} -airway.D = subset(airway.D, rowMeans(airway.D[, 2:9]) > 1) +airway.D = subset(airway.D, rowMeans(airway.D[, 2:9]) > 5) mean.before = rowMeans(airway.D[airway.I[airway.I$Intervention == "before",]$SampleName]) mean.after = rowMeans(airway.D[airway.I[airway.I$Intervention == "after",]$SampleName]) @@ -425,12 +427,12 @@ legend('top', c('n.s.', 'p < 0.05'), col = c('black', 'red'), pch = 19, bty = 'n ```{r, eval = T, collapse = F} # load example data -wine <- read.csv('data/wine.csv') +wine = read.csv('data/wine.csv') head(wine) # define the first column as a factor -wineClasses <- factor(wine$Cvs) +wineClasses = factor(wine$Cvs) # compute principal component -winePCA <- prcomp(scale(wine[, -1])) +winePCA = prcomp(scale(wine[, -1])) # look at the results summary(winePCA) # look at the names of the results in order to find out what you can use to plot @@ -494,7 +496,7 @@ hist(x) # sometimes it is useful to separate the data into more bins hist(x, breaks = 20) # you can also store the information generated by the histogram into a new variable -x.hist <- hist(x, breaks = 20) +x.hist = hist(x, breaks = 20) head(x.hist) ``` You can add the title, x-axis label and color like in the plot function, lets play around a bit `hist(x, main = , col = , xlab = , xlim = )` @@ -514,12 +516,12 @@ hist(y,col = '#0000FF50', add = T) ### Hands On Work on airway data: ```{r, collapse=TRUE} -airway.D <- read.delim2('data/airway_gene_expression.tsv', as.is = T, header = T) -airway.I <- read.delim2('data/airway_sample_info.tsv', as.is = T, header = T) +airway.D = read.delim2('data/airway_gene_expression.tsv', as.is = T, header = T) +airway.I = read.delim2('data/airway_sample_info.tsv', as.is = T, header = T) # make Gene ID to row names head(airway.ge, n = 5) -# subset to rows with mean > 1 -airway.D = subset(airway.D, rowMeans(airway.D[, 2:9]) > 1) +# subset to rows with mean > 5 +airway.D = subset(airway.D, rowMeans(airway.D[, 2:9]) > 5) # claculate averages for before and after intervention mean.before = rowMeans(airway.D[airway.I[airway.I$Intervention == "before",]$SampleName]) @@ -541,14 +543,21 @@ hist(log2(mean.after), breaks = H1$breaks, col = '#20502050', add = TRUE) Heatmaps are another regular plot to describe expression data among many other applications. We will use the data and code from this webpage: https://davetang.org/muse/2018/05/15/making-a-heatmap-in-r-with-the-pheatmap-package/ -```{r eval = T, message=FALSE} +```{r, eval = F, echo = TRUE} +if (!requireNamespace("BiocManager", quietly = TRUE)) + install.packages("BiocManager", repos='http://cran.us.r-project.org' , + dependencies = TRUE, version = "1.30.10") +BiocManager::install("DESeq") +``` + +```{r eval = T, message=FALSE, eval = FALSE} library(pheatmap) library(DESeq) # load data and subset -example_file <- system.file ("extra/TagSeqExample.tab", package="DESeq") -data <- read.delim(example_file, header=T, row.names="gene") -data_subset <- as.matrix(data[rowSums(data)>50000,]) +example_file = system.file ("extra/TagSeqExample.tab", package="DESeq") +data = read.delim(example_file, header=T, row.names="gene") +data_subset = as.matrix(data[rowSums(data)>50000,]) # create heatmap using pheatmap pheatmap(data_subset) @@ -567,8 +576,8 @@ pheatmap(as.matrix(airway.D[1:300, -1]), scale = 'row') * Draw a heatmap of only the significantly (p <= 0.01) changed rows ```{r, collapse=TRUE} -airway.D <- read.delim2('data/airway_gene_expression.tsv', as.is = T, header = T) -airway.D = subset(airway.D, rowMeans(airway.D[, 2:9]) > 1) +airway.D = read.delim2('data/airway_gene_expression.tsv', as.is = T, header = T) +airway.D = subset(airway.D, rowMeans(airway.D[, 2:9]) > 5) for(i in 1:nrow(airway.D)){ # calculate p.value @@ -581,25 +590,25 @@ head(airway.D) - use `subset(data, condition)` to subset to only significant genes ```{r, eval = TRUE, echo = FALSE} -sig.airway <- subset(airway.D, p.value <= 0.01) +sig.airway = subset(airway.D, p.value <= 0.01) pheatmap(as.matrix(sig.airway[, 2:9]), scale = 'row') ``` -## Venndiagram -Imagine you have two gene sets. To visualize the overlap of these two sets you can draw a venn diagram. To draw a venn diagram you need to load the `VennDiagram` + + -```{r, message= FALSE, collapse=TRUE} -library(VennDiagram) -genes1 = paste('gene', seq(1, 30)) -genes2 = paste('gene', seq(1, 50, 2)) -venn.diagram(list(group1 = genes1, group2 = genes2), - fill = c('dodgerblue', 'firebrick'), - filename = 'venn1.tiff') + + + + + + + -genes.overlap = calculate.overlap(list(group1 = genes1, group2 = genes2)) -genes.overlap -``` + + + ## Barplot ```{r eval = T} @@ -644,40 +653,40 @@ boxplot(x.and.y ~ groups.x.and.y) boxplot(as.matrix(log2(airway.D[,c(2,4, 6, 8, 3, 5, 7, 9)])), las = 2, cex.axis = 0.5, ylab = 'log2(expression)' ) ``` -## Plot properties -You can modify almost everything about the plot properties. To modify plot properties use `par()` + + -### Margins -Sometimes the axis label does not fit onto the plot or you want to add subtitle and need more space on the bottom. You can use `par(mar = c(bottom, left, top, right))` to modify the plot margins. + + -```{r} -par(mar = c(7, 5, 2, 1)) -boxplot(x.and.y ~ groups.x.and.y, las = 2) -``` + + + + -Sometimes you want multiple plots in one graphic. Use `par(mfrow = (nrow, ncol))` -```{r} -par(mfrow = c(1, 2)) -boxplot(x.and.y ~ groups.x.and.y, las = 2) -boxplot(list(x, y)) -``` + + + + + + -## Add text to a plot + -### Within the plot -```{r eval = T} -M = matrix(c(12, 13, 11, 9, 5, 7 ), byrow = T, ncol = 3) -bp = barplot(M, beside = T, main = 'Barplot, beside = T', - names.arg = c('A', 'B', 'C'), ylim = c(0, 15)) -text(x = colMeans(bp), y = c(12, 13, 11), labels = '**', cex = 1.5, pos = 3) -``` + + + + + + + -### Within the margins -```{r eval = T} -M = matrix(c(12, 13, 11, 9, 5, 7 ), byrow = T, ncol = 3) -bp = barplot(M, beside = T, main = 'Barplot, beside = T', names.arg = c('A', 'B', 'C')) -mtext('Samples', 1, 3) -``` + + + + + + ### Advanced links (plotting) - data to viz: https://www.data-to-viz.com/ @@ -685,12 +694,15 @@ mtext('Samples', 1, 3) # End of the day challenge -Pick a plot from data-to-viz web-page and reproduce the plot. I also encourage you to explore the data. In some cases you will need to install extra libraries, but don't be afraid, R-studio usually helps you with that. If we have time I would like you to share your plot and quickly explain what you visualized. +Pick a plot from the R graph gallery and reproduce the plot. I also encourage you to explore the data. In some cases you will need to install extra libraries, but don't be afraid, R-studio usually helps you with that. If we have time I would like you to share your plot and quickly explain what you visualized. + +# Recommended further reading +- Introduction to the tidyverse: https://r4ds.had.co.nz/introduction.html # And for tomorrow: Tomorrow we will perform a real differential gene expression analysis, following the DESeq2 Tutorial - + To ensure that everything is running smoothly, please make sure that you have the following libraries installed. If not please install them using either `install.packages()` or Bioconductor. @@ -718,7 +730,7 @@ library("ggbeeswarm") # bioconductor libraries if (!requireNamespace("BiocManager", quietly = TRUE)) - install.packages("BiocManager", repos='http://cran.us.r-project.org' , dependencies = TRUE, version = "1.30.10") + install.packages("BiocManager", repos='http://cran.us.r-project.org' , dependencies = TRUE) BiocManager::install("airway") BiocManager::install("GenomicFeatures") diff --git a/R_workshop.2.html b/R_workshop.2.html index 14a8633..16fc492 100644 --- a/R_workshop.2.html +++ b/R_workshop.2.html @@ -610,8 +610,10 @@

Cran

Bioconductor

-
source("https://bioconductor.org/biocLite.R")
-biocLite("edgeR")
+
if (!requireNamespace("BiocManager", quietly = TRUE))
+    install.packages("BiocManager", repos='http://cran.us.r-project.org' , dependencies = TRUE)
+
+BiocManager::install("edgeR")
 
 library(edgeR)

 
We will install more packages later. In general it is quite straight forward.

@@ -768,7 +770,7 @@ 
Volcano plot

 

 
Hands On

 
Work on the airway dataset

-
airway.ge <- read.delim("data/airway_gene_expression.tsv", as.is = T, header = T, sep = '\t')
+
airway.ge = read.delim("data/airway_gene_expression.tsv", as.is = T, header = T, sep = '\t')
 # make Gene ID to row names
 head(airway.ge, n = 5)
 ##            GeneID S_01.control.Rep1 S_02.__treat.Rep1 S_03.control.Rep2
@@ -790,10 +792,10 @@ 
Hands On

 ## 4               229
 ## 5                60

 
    
-
  • Remove low abundant rowMeans < 1 genes from the airway data
+
  • Remove low abundant rowMeans < 5 genes from the airway data
 
      
 
    • calculate the RM = rowMeans() using columns 2:9 of airway.ge
      • 
-
    • subset airway.ge using subset(airway.ge, RM > 1)
      • 
+
    • subset airway.ge using subset(airway.ge, RM > 5)
      • 
 
    • 
 
  • Calculate a p-value for your test data frame
 
      
@@ -825,7 +827,7 @@ 
      Hands On
      
 ## [1] "pvalue"
 ##          1          3          4          5          7          8 
 ## 0.22082663 0.82755428 0.67474685 0.38412456 0.49498597 0.04771523

-

+
@@ -843,7 +845,7 @@

PCA plot

# load example data
-wine <- read.csv('data/wine.csv')
+wine = read.csv('data/wine.csv')
 head(wine)
##   Cvs Alcohol Malic.acid  Ash Alcalinity.of.ash Magnesium Total.phenols
 ## 1   1   14.23       1.71 2.43              15.6       127          2.80
@@ -867,9 +869,9 @@ 
PCA plot

 ## 5                         2.93     735
 ## 6                         2.85    1450
# define the first column as a factor
-wineClasses <- factor(wine$Cvs)
+wineClasses = factor(wine$Cvs)
 # compute principal component
-winePCA <- prcomp(scale(wine[, -1]))
+winePCA = prcomp(scale(wine[, -1]))
 # look at the results
 summary(winePCA)
## Importance of components:
@@ -912,15 +914,15 @@ 
Hands On

 
 
 
## Importance of components:
-##                             PC1     PC2     PC3      PC4      PC5      PC6
-## Standard deviation     101.9949 62.6071 48.3187 45.05735 41.18304 35.92998
-## Proportion of Variance   0.4578  0.1725  0.1027  0.08934  0.07464  0.05681
-## Cumulative Proportion    0.4578  0.6303  0.7330  0.82237  0.89700  0.95381
-##                             PC7       PC8
-## Standard deviation     32.39630 3.438e-14
-## Proportion of Variance  0.04619 0.000e+00
-## Cumulative Proportion   1.00000 1.000e+00

-

+##                            PC1     PC2      PC3      PC4      PC5      PC6
+## Standard deviation     96.8163 55.4581 40.56263 36.99393 33.00294 26.81905
+## Proportion of Variance  0.5267  0.1728  0.09246  0.07691  0.06121  0.04042
+## Cumulative Proportion   0.5267  0.6996  0.79204  0.86895  0.93015  0.97057
+##                             PC7      PC8
+## Standard deviation     22.88373 2.04e-14
+## Proportion of Variance  0.02943 0.00e+00
+## Cumulative Proportion   1.00000 1.00e+00
+

@@ -991,7 +993,7 @@

Histogram

# sometimes it is useful to separate the data into more bins
 hist(x, breaks = 20)
 # you can also store the information generated by the histogram into a new variable
-x.hist <- hist(x, breaks = 20)
+x.hist = hist(x, breaks = 20) 

head(x.hist)
## $breaks
@@ -1031,8 +1033,8 @@ 
Histogram

 

 
Hands On

 
Work on airway data:

-
airway.D <- read.delim2('data/airway_gene_expression.tsv', as.is = T, header = T)
-airway.I <- read.delim2('data/airway_sample_info.tsv', as.is = T, header = T)
+
airway.D = read.delim2('data/airway_gene_expression.tsv', as.is = T, header = T)
+airway.I = read.delim2('data/airway_sample_info.tsv', as.is = T, header = T)
 # make Gene ID to row names
 head(airway.ge, n = 5)
 ##            GeneID S_01.control.Rep1 S_02.__treat.Rep1 S_03.control.Rep2
@@ -1053,8 +1055,8 @@ 
Hands On

 ## 3               508
 ## 4               229
 ## 5                60
-# subset to rows with mean > 1
-airway.D = subset(airway.D, rowMeans(airway.D[, 2:9]) > 1)
+# subset to rows with mean > 5
+airway.D = subset(airway.D, rowMeans(airway.D[, 2:9]) > 5)
 
 # claculate averages for before and after intervention
 mean.before = rowMeans(airway.D[airway.I[airway.I$Intervention == "before",]$SampleName])
@@ -1069,20 +1071,24 @@ 
Hands On

 
  • using the same breaks H1 = hist(...) and H2 = hist(..., breaks = H1$breaks, add = TRUE )
    • 
 
 
-
    
+

    Heatmap

    Heatmaps are another regular plot to describe expression data among many other applications.
    We will use the data and code from this webpage: https://davetang.org/muse/2018/05/15/making-a-heatmap-in-r-with-the-pheatmap-package/

    +
    if (!requireNamespace("BiocManager", quietly = TRUE))
+     install.packages("BiocManager", repos='http://cran.us.r-project.org' , 
+                      dependencies = TRUE, version = "1.30.10")
+BiocManager::install("DESeq")
    library(pheatmap)
 library(DESeq)
 
 # load data and subset
-example_file <- system.file ("extra/TagSeqExample.tab", package="DESeq")
-data <- read.delim(example_file, header=T, row.names="gene")
-data_subset <- as.matrix(data[rowSums(data)>50000,])
+example_file = system.file ("extra/TagSeqExample.tab", package="DESeq")
+data = read.delim(example_file, header=T, row.names="gene")
+data_subset = as.matrix(data[rowSums(data)>50000,])
  
 # create heatmap using pheatmap
 pheatmap(data_subset)
    @@ -1095,12 +1101,12 @@

    Hands On

  • pheatmpa(as.matrix(airway.D[<correct row indeces>, <only numerical columns>]))
    • -

    +

    • Draw a heatmap of only the significantly (p <= 0.01) changed rows
    -
    airway.D <- read.delim2('data/airway_gene_expression.tsv', as.is = T, header = T)
-airway.D = subset(airway.D, rowMeans(airway.D[, 2:9]) > 1)
+
    airway.D = read.delim2('data/airway_gene_expression.tsv', as.is = T, header = T)
+airway.D = subset(airway.D, rowMeans(airway.D[, 2:9]) > 5)
 
   for(i in 1:nrow(airway.D)){
     # calculate p.value
@@ -1132,39 +1138,20 @@ 
    Hands On
    
 
      
 
    • use subset(data, condition) to subset to only significant genes
      • 
 
    
-
    
-
    +

    + + + + + + + + + + + + -
    -

    Venndiagram

    -

    Imagine you have two gene sets. To visualize the overlap of these two sets you can draw a venn diagram. To draw a venn diagram you need to load the VennDiagram

    -
    library(VennDiagram)
-genes1 = paste('gene', seq(1, 30))
-genes2 = paste('gene', seq(1, 50, 2))
-venn.diagram(list(group1 = genes1, group2 = genes2), 
-             fill = c('dodgerblue', 'firebrick'),
-             filename = 'venn1.tiff')
-## [1] 1
-
-genes.overlap = calculate.overlap(list(group1 = genes1, group2 = genes2))
-genes.overlap
-## $a1
-##  [1] "gene 1"  "gene 2"  "gene 3"  "gene 4"  "gene 5"  "gene 6"  "gene 7" 
-##  [8] "gene 8"  "gene 9"  "gene 10" "gene 11" "gene 12" "gene 13" "gene 14"
-## [15] "gene 15" "gene 16" "gene 17" "gene 18" "gene 19" "gene 20" "gene 21"
-## [22] "gene 22" "gene 23" "gene 24" "gene 25" "gene 26" "gene 27" "gene 28"
-## [29] "gene 29" "gene 30"
-## 
-## $a2
-##  [1] "gene 1"  "gene 3"  "gene 5"  "gene 7"  "gene 9"  "gene 11" "gene 13"
-##  [8] "gene 15" "gene 17" "gene 19" "gene 21" "gene 23" "gene 25" "gene 27"
-## [15] "gene 29" "gene 31" "gene 33" "gene 35" "gene 37" "gene 39" "gene 41"
-## [22] "gene 43" "gene 45" "gene 47" "gene 49"
-## 
-## $a3
-##  [1] "gene 1"  "gene 3"  "gene 5"  "gene 7"  "gene 9"  "gene 11" "gene 13"
-##  [8] "gene 15" "gene 17" "gene 19" "gene 21" "gene 23" "gene 25" "gene 27"
-## [15] "gene 29"

    Barplot

    @@ -1181,7 +1168,7 @@

    Hands On

  • use colMeans(airway.D[, 2:9])
    • -

    +

    @@ -1207,41 +1194,35 @@

    Hands On

  • boxplot()
    • -

    -
    - -
    -

    Plot properties

    -

    You can modify almost everything about the plot properties. To modify plot properties use par()

    -
    -

    Margins

    -

    Sometimes the axis label does not fit onto the plot or you want to add subtitle and need more space on the bottom. You can use par(mar = c(bottom, left, top, right)) to modify the plot margins.

    -
    par(mar = c(7, 5, 2, 1))
-boxplot(x.and.y ~ groups.x.and.y, las = 2)
    -

    -

    Sometimes you want multiple plots in one graphic. Use par(mfrow = (nrow, ncol))

    -
    par(mfrow = c(1, 2))
-boxplot(x.and.y ~ groups.x.and.y, las = 2)
-boxplot(list(x, y))
    -

    -
    -
    -
    -

    Add text to a plot

    -
    -

    Within the plot

    -
    M = matrix(c(12, 13, 11, 9, 5, 7 ), byrow = T, ncol = 3)
-bp = barplot(M, beside = T, main = 'Barplot, beside = T', 
-             names.arg = c('A', 'B', 'C'), ylim = c(0, 15))
-text(x = colMeans(bp), y = c(12, 13, 11), labels = '**', cex = 1.5, pos = 3)
    -

    -
    -
    -

    Within the margins

    -
    M = matrix(c(12, 13, 11, 9, 5, 7 ), byrow = T, ncol = 3)
-bp = barplot(M, beside = T, main = 'Barplot, beside = T', names.arg = c('A', 'B', 'C'))
-mtext('Samples', 1, 3)
    -

    +

    + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    End of the day challenge

    -

    Pick a plot from data-to-viz web-page and reproduce the plot. I also encourage you to explore the data. In some cases you will need to install extra libraries, but don’t be afraid, R-studio usually helps you with that. If we have time I would like you to share your plot and quickly explain what you visualized.

    +

    Pick a plot from the R graph gallery and reproduce the plot. I also encourage you to explore the data. In some cases you will need to install extra libraries, but don’t be afraid, R-studio usually helps you with that. If we have time I would like you to share your plot and quickly explain what you visualized.

    +
    +

    And for tomorrow:

    -

    Tomorrow we will perform a real differential gene expression analysis, following the DESeq2 Tutorial http://master.bioconductor.org/packages/release/workflows/vignettes/rnaseqGene/inst/doc/rnaseqGene.html

    +

    Tomorrow we will perform a real differential gene expression analysis, following the DESeq2 Tutorial https://www.bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html

    To ensure that everything is running smoothly, please make sure that you have the following libraries installed. If not please install them using either install.packages() or Bioconductor.

    # loading all libraries
 
@@ -1270,6 +1257,7 @@ 
    And for tomorrow:
    
 install.packages("RColorBrewer")
 install.packages("PoiClaClu")
 install.packages("ggbeeswarm")
+install.packages("dbplyr")
 
 library("magrittr")
 library("dplyr")
@@ -1282,7 +1270,7 @@ 
    And for tomorrow:
    
 
 # bioconductor libraries
 if (!requireNamespace("BiocManager", quietly = TRUE))
-    install.packages("BiocManager", repos='http://cran.us.r-project.org' , dependencies = TRUE, version = "1.30.10")
+    install.packages("BiocManager", repos='http://cran.us.r-project.org' , dependencies = TRUE)
 
 BiocManager::install("airway")
 BiocManager::install("GenomicFeatures")
@@ -1300,6 +1288,7 @@ 
    And for tomorrow:
    
 BiocManager::install("sva")
 BiocManager::install("RUVSeq")
 BiocManager::install("fission")
+BiocManager::install("tximeta")
 
 library("airway")
 library("GenomicFeatures")
@@ -1311,12 +1300,13 @@ 
    And for tomorrow:
    
 library("apeglm")
 library("genefilter")
 library("AnnotationDbi")
-library("org.Hs.eg.,m db")
+library("org.Hs.eg.db")
 library("ReportingTools")
 library("Gviz")
 library("sva")
 library("RUVSeq")
-library("fission")
    +library("fission") +library("tximeta")
    diff --git a/R_workshop.2.pdf b/R_workshop.2.pdf index 0abd340..c41700e 100644 Binary files a/R_workshop.2.pdf and b/R_workshop.2.pdf differ diff --git a/R_workshop.Rmd b/R_workshop.Rmd index fa027e1..5eeaeb8 100644 --- a/R_workshop.Rmd +++ b/R_workshop.Rmd @@ -1,8 +1,8 @@ --- title: "R Workshop -- Introduction to R" output: - html_document: default pdf_document: default + html_document: default fontsize: 8pt --- @@ -34,7 +34,7 @@ Now, please share your screen with us for a few moments to make sure we are all *** R can be used as a simple calculator -```{r} +```{r, collapse = TRUE} # adding 18 + 6 @@ -48,21 +48,47 @@ R can be used as a simple calculator 18/6 ``` +*** + +# Programming Syntax in R + +1. Difference between Console and Script +2. What is a variable + + `name = value` + +3. What is a function + + `name(argument = value)` + + `name(argument1 = value1, argument2 = value2)` + +4. You can have nested functions + + `name(arguments = name(arguments = values))` + +*** + + R can also be used as a more advanced calculator -```{r} +```{r, collapse = TRUE} # square a number 4 ^ 2 # square root of a number -sqrt(16) +sqrt(x = 16) + +# sqrt is the function name +# 16 is the argument # log-transform data log10(100) log2(8) + +# nested calculations +sqrt(x = log2(x = 8)) +# or short +sqrt(log2(8)) ``` R can be used to check if a value is bigger/smaller than another -```{r} +```{r, collapse = TRUE} # greater than, less than 6 == 6 # equal to 6 > 6 # greater than @@ -71,7 +97,7 @@ R can be used to check if a value is bigger/smaller than another ``` You can store a result or any value in a variable to use later -```{r} +```{r, collapse = TRUE} # assign the value 6 to variable x x = 6 # check out what x is @@ -81,6 +107,7 @@ print(x) # use x x + 5 ``` + ## Hands On **Try to execute the following calculations** @@ -96,6 +123,11 @@ x + 5 ### Numeric ```{r eval = FALSE} x = 10.5 # or x <- 10 + +# access variables +x +# or +print(x) ``` ### Logical @@ -155,6 +187,12 @@ c = c('apple', 'six') # mixed vector? d = c('six', a, 5) + +# access values in a vector +a[1] +a[1] + 4 +a[1] = a[1] + 4 +print(a) ``` ### Matrix @@ -176,6 +214,14 @@ t(A) # concatenate two matrices row- or column-wise D = cbind(A, B) E = rbind(A, B) + +# access matrix elements +# rows +D[1,] +# columns +D[,2] +# individual cells +D[1,2] ``` ### List @@ -194,6 +240,8 @@ print(xn) # access list elements x[2] # vs. x[[2]] +x[[2]] +x[['Strings']] xn$Strings # vs. xn['Strings'] or xn[['Strings']] ``` diff --git a/R_workshop.html b/R_workshop.html index 7d658be..e93e064 100644 --- a/R_workshop.html +++ b/R_workshop.html @@ -389,51 +389,87 @@

    Introduction to R Studio

    R can be used as a simple calculator

    # adding
-18 + 6
    -
    ## [1] 24
    -
    # substracting
-18 - 6
    -
    ## [1] 12
    -
    # multiplication
-18 * 6
    -
    ## [1] 108
    -
    # Division
-18/6
    -
    ## [1] 3
    +18 + 6 +## [1] 24 + +# substracting +18 - 6 +## [1] 12 + +# multiplication +18 * 6 +## [1] 108 + +# Division +18/6 +## [1] 3     +
    +
    +
    +

    Programming Syntax in R

    +
      +
    1. Difference between Console and Script
      2. +
    2. What is a variable +
        +
      • name = value
        • +
      3. +
    3. What is a function +
        +
      • name(argument = value)
        • +
      • name(argument1 = value1, argument2 = value2)
        • +
      4. +
    4. You can have nested functions +
        +
      • name(arguments = name(arguments = values))
        • +
      5. +
    +

    R can also be used as a more advanced calculator

    # square a number
-4 ^ 2
    -
    ## [1] 16
    -
    # square root of a number
-sqrt(16)
    -
    ## [1] 4
    -
    # log-transform data
-log10(100)
    -
    ## [1] 2
    -
    log2(8)
    -
    ## [1] 3
    +4 ^ 2 +## [1] 16 + +# square root of a number +sqrt(x = 16) +## [1] 4 + +# sqrt is the function name +# 16 is the argument + +# log-transform data +log10(100) +## [1] 2 +log2(8) +## [1] 3 + +# nested calculations +sqrt(x = log2(x = 8)) +## [1] 1.732051 +# or short +sqrt(log2(8)) +## [1] 1.732051

    R can be used to check if a value is bigger/smaller than another

    # greater than, less than
-6 == 6 # equal to
    -
    ## [1] TRUE
    -
    6 > 6 # greater than
    -
    ## [1] FALSE
    -
    6 >= 6 # greater or equal then
    -
    ## [1] TRUE
    -
    6 < 6 # less than
    -
    ## [1] FALSE
    +6 == 6 # equal to +## [1] TRUE +6 > 6 # greater than +## [1] FALSE +6 >= 6 # greater or equal then +## [1] TRUE +6 < 6 # less than +## [1] FALSE

    You can store a result or any value in a variable to use later

    # assign the value 6 to variable x
 x = 6
 # check out what x is
-x
    -
    ## [1] 6
    -
    # or
-print(x)
    -
    ## [1] 6
    -
    # use x
-x + 5
    -
    ## [1] 11
    +x +## [1] 6 +# or +print(x) +## [1] 6 +# use x +x + 5 +## [1] 11

    Hands On

    Try to execute the following calculations

    @@ -450,7 +486,12 @@

    Hands On

    Relevant basic data types

    Numeric

    -
    x = 10.5 # or x <- 10
    +
    x = 10.5 # or x <- 10
+
+# access variables
+x
+# or 
+print(x)

    Logical

    @@ -506,7 +547,13 @@

    Vector

    c = c('apple', 'six') # mixed vector? -d = c('six', a, 5) +d = c('six', a, 5) + +# access values in a vector +a[1] +a[1] + 4 +a[1] = a[1] + 4 +print(a)

    Matrix

    @@ -526,7 +573,15 @@

    Matrix

    # concatenate two matrices row- or column-wise D = cbind(A, B) -E = rbind(A, B) +E = rbind(A, B) + +# access matrix elements +# rows +D[1,] +# columns +D[,2] +# individual cells +D[1,2]

    List

    @@ -544,6 +599,8 @@

    List

    # access list elements x[2] # vs. x[[2]] +x[[2]] +x[['Strings']] xn$Strings # vs. xn['Strings'] or xn[['Strings']]
    @@ -692,11 +749,11 @@

    Repeat

    Random numbers

    # random numbers from a normal distribution
 rnorm(n = 5, mean = 5, sd = 1) 
-## [1] 3.073082 5.903837 5.941621 5.891309 4.580985
+## [1] 3.167047 4.887193 4.198460 6.396455 5.599182
 
 # random numbers from a uniform distribution
 runif(n = 5, min = 1, max = 5)
-## [1] 4.910746 3.200908 2.756291 4.979401 3.739086
    +## [1] 3.728042 2.285100 1.705257 1.207352 2.309108

    Other distributions in R include:

    • Normal: rnorm(n, mean, sd)
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2 + +# Hands on 3 + +n = c('Alice', 'Ben', 'Charly', 'Dawn') +a = c(25, 32, 27, 33) +h = c(1.65, 1.96, 1.86, 1.74) +s = c('Ski', 'Basketball', 'Soccer', 'Volleyball') + +df = data.frame(Name = n, Age = a, Height = h, Sport = s) +# access column +df$Numbers +df[,1] + +# acces row +df[2,] + +# access element +df[3, 1] + + + +# Basic functions +M = matrix(c(5, 6, 7, 8), nrow = 2) + +sum(M) + +rowSums(M) +colSums(M) + +mean(M) +rowMeans(M) +colMeans(M) + +var(M) +var(as.numeric(M)) +sd(M) + +mean(c(5, 6, 7, 89)) +median(c(5, 6, 7, 8)) + +x = c(5, 6, 7 , 89) +quantile(c(5, 6, 7, 89), probs = c(0.25, 0.5, 0.75)) + + +colMeans(df[, c('Age', "Height")]) + + +seq(from = 10, to = 20, by = 2) +seq(from = 1 , to = 0, by = -0.1) + +rep(x = NA, times = 3) + +rep(c(0,1), times = 5) + +rep(c(0,1), each = 5) + +set.seed(1) +RN = rnorm(n = 1000, mean = 5, sd = 2) + + + +set.seed(124) +df <- data.frame(A=rnorm(10), B=rnorm(10), C=rnorm(10)) + +C1<-c(3,2,4,4,5) +C2<-c(3,7,3,4,5) +C3<-c(5,4,3,6,3) +DF<-data.frame(ID=c("A","B","C","D","E"),C1=C1,C2=C2,C3=C3) + +data.frame(ID=DF[,1], Means=rowMeans(DF[,-1])) + +DF$Mean <- rowMeans(DF[,2:4]) + + +# read in tables +D = read.table('data/mouse.tab', as.is = TRUE, header = FALSE) +D = read.csv('data/mouse.csv') +D = D[,-1] + +library(readxl) +mouse <- read_excel("data/mouse.xlsx") + + + +D = read.table('data/mouse.tab', as.is = T, header = T, sep = '\t') + + + +M = matrix(round(rnorm(30, 10, 5),1), ncol = 5) + + +M[,1] + M[,2] +M[3,] + M[4,] + +M * 2 +M + 2 + +SUMS = colSums(M) + + +colSums(t(t(M)/SUMS) * 100) + + +summary(Experiment1) +table(Experiment1$cage) + +# correlation between two columns +cor(Experiment1$cage, Experiment1$weight, method = 'spearman') + +# t.test for one column split by a group column with paired data +t.test(Experiment1$lifespan ~ Experiment1$treatment, paired = TRUE) + + +## Hands on + +# 1 read in data +airway.ge <- read.delim("data/airway_gene_expression.tsv", as.is = T, header = T, sep = '\t') + +# 2. calculate correlation (cor()) between column 'S_01.control.Rep1' and column 'S_03.control.Rep2' + +# 3. calculate if there is a signficant difference between +# 'S_01.control.Rep1' and 'S_02.__treat.Rep1' using t.test() + +t.test(airway.ge$S_01.control.Rep1, airway.ge$S_02.__treat.Rep1, paired = TRUE) + + + +# plotting +plot(x = Experiment1$lifespan, y = Experiment1$weight, col = '#20508080', + ylab = 'weight [g]', xlab = 'lifespan [weeks]', pch = 19) + +L = lm(Experiment1$weight ~ Experiment1$lifespan) # y ~ x !!! + +abline(L, col = 'red') + +#subset +Exp_cage10 = subset(ExperimentP, cage == 10) + +Exp_cage10_11 = subset(ExperimentP, cage %in% c(10,11)) + + +# Conditional statements + +x = 5 +y = NA +z = 'six' + +if(x == 5) { + print('x is equal to 5') +} + + +if (x == 5) { + x = x*2 +} else { + print('x is not equal to 5') +} + + +if (x < 5) { + x * 2 +} else if (x == 5) { + x +} else { + x / 2 +} + + +if(is.na(y)){ + print('y is not available') +} else { + print(y) +} + + + +if (is.character(z)){ + print(length(z)) + print(nchar(z)) +} else if(is.numeric(z)){ + print(z * 3) +} else { + print("can't work with z. It's neither a number nor a character") +} + + + +number = 1 + +while (number < 50){ + print(number) + number = number * 2 +} + + +for( i in 1:8){ + print(paste("i is now:", i)) +} + +files = list.files('.') +for(i in files){ + print(i) +} + + +for(x in 2:4){ + if( x == 3){ + break + } else { + print(x) + } +} + + +gene = read.csv("data/gene_exp1.csv") +head(gene) + +for(row in 1:nrow(gene)){ + TT = t.test(gene[row,2:4], gene[row,5:7]) + gene[row, 'Diff'] = TT$p.value +} + +significant = subset(gene, Diff <= 0.05) + + +source('data/create_gene_expression_table.R') + +# rewrite this using Experiment_DEG and group1 = rep 1-4, group2 = 5-8 +for(row in 1:nrow(gene)){ + TT = t.test(gene[row,2:4], gene[row,5:7]) + gene[row, 'Diff'] = TT$p.value +} + +significant = subset(gene, Diff <= 0.05) + diff --git a/scripts/day2_scripts.R b/scripts/day2_scripts.R new file mode 100644 index 0000000..9307413 --- /dev/null +++ b/scripts/day2_scripts.R @@ -0,0 +1,351 @@ +for(i in 1:nrow(Experiment_DEG)){ + ttest = t.test(Experiment_DEG[i, 2:5], Experiment_DEG[i, 6:9]) + Experiment_DEG[i, 'pvals'] = as.numeric(ttest$p.value) +} + + +sig_genes = subset(Experiment_DEG, pvals < 0.05) + + +# write our own function +Square <- function(number_to_square = 3){ + + our_result = number_to_square ^ 2 + + return(our_result) + +} + +Square(number_to_square = 4) + + +exponent_2_and_3_list <- function(x, y){ + + sq = x ^ 2 + cb = x ^ 3 + + return(list(square = sq, cube = cb)) + +} + +exponent_2_and_3_list(4) + +rain = exponent_2_and_3_list(4) + + +# hands on + +return_bigger <- function(input_value, vector_to_subset){ + return_vector = numeric(0) + + for(value in vector_to_subset){ + if(value > input_value){ + return_vector = c(return_vector , value) + } + } + return(return_vector) +} + +return_bigger(5, c(10,3,2,16,4,7)) + + + + + +myVolcanoPlot <- function(D, group1, group2, alpha ){ + # Input: + # D > dataframe to be analyzed + # group1 > column indeces for group1 + # group2 > column indeces for group2 + # alpha > p.value cut off for significant genes + + # this function calculates the pvalue using a t.test + # the group means and the log2fc and makes a plot + + # calculate the pvalue for each row using apply + for(i in 1:nrow(D)){ + # calculate p.value + D[i,'p.value'] = as.numeric(t.test(D[i,group1], D[i,group2])$p.value) + + # calculate mean for group 1 + D[i,'mean.g1'] = mean(as.numeric(D[i,group1]), na.rm = TRUE) + + # calculate mean for group 2 + D[i,'mean.g2'] = mean(as.numeric(D[i,group2]), na.rm = TRUE) + + # calculate log2 fold change + D[i,'log2fc'] = log2(D[i,'mean.g2']/D[i,'mean.g1']) + } + + D[, 'q.value'] = p.adjust(D[,'p.value'], method = 'fdr') + plot(D[,'log2fc'], -log10(D[,'q.value']), pch = 19, + xlab = 'log2fc', ylab = '-log10(q.value)', cex.axis = 1.3, cex.lab = 1.3, + col = ifelse(D[,'q.value'] <= alpha, + ifelse(D[, 'log2fc'] < 0, '#2120ac50', '#b2182b50'), + '#bdbdbd30')) # nested ifelse call to make three colors + # add legend + legend('bottomleft', c('n.s', 'sig down', 'sig up'), col = c('#bdbdbd30', '#2120ac50', '#b2182b50'), pch = 19, cex = 0.8, bty = 'n') + + return(D) +} + + +AD = myVolcanoPlot(Experiment_DEG[1:1000, ], 2:5, 6:9, 0.01) +AD = myVolcanoPlot(Experiment_DEG[1:1000, ], 2:5, 6:9, 0.5) +AD = myVolcanoPlot(Experiment_DEG[1:1000, ], 2:5, 6:9, 0.05) + +text(x = c(-2, 1), y = c(3.5, 2.5), labels = c('A', 'B')) + + +# save a table +write.table(AD, 'data/output_results.csv', sep = ',', quote = FALSE) + +AD[, 'neglog10'] = -log10(AD[, "q.value"]) +write.xlsx(AD, 'data/output_results.xlsx', row.names = FALSE) + +install.packages(c('pheatmap', 'gplots', 'ggplot2')) + + +if (!requireNamespace("BiocManager", quietly = TRUE)) + install.packages("BiocManager", repos='http://cran.us.r-project.org' , + dependencies = TRUE, version = "1.30.10") +BiocManager::install(c('edgeR', 'DESeq2')) + +library(edgeR) +library(DESeq2) +library(pheatmap) +library(gplots) +library(ggplots2) + + +## Plotting + +x = runif(n = 100, min = 20, max = 80) +y = rnorm(n = 100, mean = x, sd = 10) + +plot(x = x, y = y, main = 'Simple Plot', + xlab = 'this is the x axis', + ylab = 'this is the y axis', + cex.lab = 1.5, cex.axis = 1.7, cex.main = 3, cex = 1.5, + pch = ifelse(abs(x - mean(x)) > sd(x), 6, 17), + col = ifelse(y > x, 'firebrick2', 'dodgerblue2') + ) + +legend('topleft', legend = c('outside sd', 'inside sd'), + pch = c(6, 17), cex = 0.8, bty = 'n') +legend('bottomright', legend = c('x bigger', 'y bigger'), fill = c('dodgerblue2', 'firebrick2')) + +abline(a = 0, b = 1) + + + +# Hints for hands on +airway.D = read.delim2('data/airway_gene_expression.tsv') +airway.info = read.delim2('data/airway_sample_info.tsv') + +# remove 0 +airway.D = subset(airway.D, rowMeans(airway.D[, -1]) > 5) + +mean_before = rowMeans(airway.D[, c(2,4, 6)], na.rm = TRUE) +mean_after = rowMeans(airway.D[, c(3, 5, 7)], na.rm = TRUE) + +# calculate log2 fold change log2(mean_after/mean_before) + +log2fc = log2(mean_after/mean_before) + +# pvalue +airway.pvalue = numeric(0) +for(i in 1:nrow(airway.D)){ + airway.pvalue[i] = as.numeric(t.test(airway.D[i, c(2,4, 6)], + airway.D[i, c(3, 5, 7)])$p.value) +} + +qvalues = p.adjust(airway.pvalue, method = 'fdr') +# plot a volcano plot x = log2fc, y = -log10(airway.pvalue) + +plot(x = log2fc, y = -log10(qvalues), + col = ifelse(qvalues < 0.05, 'red', 'gray')) + + + + + +# example ggplot2 +D = data.frame(value1 = x, value2 = y) +D[,'bigger'] = ifelse(y > x, 'bigger than x', 'smaller than x') +D[, 'sd'] = ifelse(abs(x - mean(x)) > sd(x), 'outside', 'inside') + + +library(ggplot2) + +p <- ggplot(data = D, + mapping = aes(x = value1, y = value2, color = bigger, shape = sd)) + + geom_point() + +p = p + ggtitle('this is a ggplot2 plot') + +p + + +# Volcano plots + +head(Experiment_DEG) +for(i in 1:nrow(Experiment_DEG)){ + # calculate p.value + Experiment_DEG[i,'p.value'] = as.numeric(t.test(Experiment_DEG[i,2:5], Experiment_DEG[i,6:9])$p.value) +} + +mean_groupA = rowMeans(Experiment_DEG[,2:5]) +mean_groupB = rowMeans(Experiment_DEG[, 6:9]) + +log2fc = log2(mean_groupB/mean_groupA) +qvalues = p.adjust(Experiment_DEG$p.value, method = 'fdr') + +plot(x = log2fc, y = -log10(qvalues), + col = ifelse(qvalues < 0.05, 'red', 'gray')) + + +## PCA +wine <- read.csv('data/wine.csv') +head(wine) + +wineClasses = factor(wine$Cvs) + +winePCA = prcomp(scale(wine[, -1])) + +names(winePCA) + +plot(winePCA$x[,1:2]) + +plot(x = winePCA$x[,1], y = winePCA$x[,2], col = wineClasses, pch = 19) + + +# Hints for hands on +airway.D = read.delim2('data/airway_gene_expression.tsv') +airway.info = read.delim2('data/airway_sample_info.tsv') + +# remove 0 +airway.D = subset(airway.D, rowMeans(airway.D[, -1]) > 5) + +PCA <- prcomp(t(airway.D[,2:9])) + +plot() + +# histogram +x = rnorm(1000, 20, 80) + +hist(x, main = 'test plot', col = 'gray', breaks = 50) + +x.hist <- hist(x, breaks = 20) + + +x = runif(100, 20, 80) +y = rnorm(100, x, 10) + +hist(x, xlab = 'WT',main = 'Simple Histogram', + cex.main = 2, cex.axis = 1.5, cex.lab = 1.5, + col = 'gray', xlim = c(0, 100)) + +hist(y, col = '#0000FF50', add = TRUE) + + + +# hands on + + +# Hints for hands on +airway.D = read.delim2('data/airway_gene_expression.tsv') +airway.info = read.delim2('data/airway_sample_info.tsv') + +# remove 0 +airway.D = subset(airway.D, rowMeans(airway.D[, -1]) > 5) + +mean_before = rowMeans(airway.D[, c(2,4, 6)], na.rm = TRUE) +mean_after = rowMeans(airway.D[, c(3, 5, 7)], na.rm = TRUE) + +# draw two histograms one of mean_before and one of mean_after + +hist(log2(mean_before), breaks = 100) + +hist() + +# advanced: try to draw both distributions in one plot + + +library(pheatmap) +pheatmap(data_subset, cutree_cols = 2, cutree_rows = 5) +pheatmap(airway.D[1:300,2:9], scale = 'row') + +significant_airway = subset(airway.D, airway.pvalue < 0.05) + +pheatmap(significant_airway[, 2:9], + scale = 'row', show_rownames = FALSE, + cutree_rows = 2) + + + + + + + + + + +# cran libraries +install.packages("magrittr") +install.packages("dplyr") +install.packages("ggplot2") +install.packages("pheatmap") +install.packages("RColorBrewer") +install.packages("PoiClaClu") +install.packages("ggbeeswarm") +install.packages("dbplyr") + +library("magrittr") +library("dplyr") +library("ggplot2") +library("pheatmap") +library("RColorBrewer") +library("PoiClaClu") +library("ggbeeswarm") + + +# bioconductor libraries +if (!requireNamespace("BiocManager", quietly = TRUE)) + install.packages("BiocManager", repos='http://cran.us.r-project.org' , dependencies = TRUE) + +BiocManager::install("airway") +BiocManager::install("GenomicFeatures") +BiocManager::install("GenomicAlignments") +BiocManager::install("Rsamtools") +BiocManager::install("BiocParallel") +BiocManager::install("DESeq2") +BiocManager::install("vsn") +BiocManager::install("apeglm") +BiocManager::install("genefilter") +BiocManager::install("AnnotationDbi") +BiocManager::install("org.Hs.eg.db") +BiocManager::install("ReportingTools") +BiocManager::install("Gviz") +BiocManager::install("sva") +BiocManager::install("RUVSeq") +BiocManager::install("fission") +BiocManager::install("tximeta") + +library("airway") +library("GenomicFeatures") +library("GenomicAlignments") +library("Rsamtools") +library("BiocParallel") +library("DESeq2") +library("vsn") +library("apeglm") +library("genefilter") +library("AnnotationDbi") +library("org.Hs.eg.db") +library("ReportingTools") +library("Gviz") +library("sva") +library("RUVSeq") +library("fission") +library("tximeta") diff --git a/venn1.tiff b/venn1.tiff new file mode 100644 index 0000000..db15fd4 Binary files /dev/null and b/venn1.tiff differ