## ----style, echo=FALSE, results='asis'----------------------------------- BiocStyle::markdown() ## ----eval=FALSE---------------------------------------------------------- # y = beta0 + beta1 * height + beta2 * weight + beta3 * shoe_size ## ----message=FALSE------------------------------------------------------- library(airway) data("airway") se <- airway colData(se) library("DESeq2") dds <- DESeqDataSet(se, design = ~ cell + dex) ## ----message=FALSE------------------------------------------------------- library(MLSeq) filepath = system.file("extdata/cervical.txt", package = "MLSeq") cervical = read.table(filepath, header = TRUE) ## ------------------------------------------------------------------------ rld <- rlog(dds) head(assay(rld)) ## ------------------------------------------------------------------------ sampleDists <- dist( t( assay(rld) ) ) sampleDists ## ----message=FALSE------------------------------------------------------- library("gplots") library("RColorBrewer") sampleDistMatrix <- as.matrix( sampleDists ) rownames(sampleDistMatrix) <- paste( rld$dex, rld$cell, sep="-" ) colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255) hc <- hclust(sampleDists) heatmap.2( sampleDistMatrix, Rowv=as.dendrogram(hc), symm=TRUE, trace="none", col=colors, margins=c(2,10), labCol=FALSE ) ## ------------------------------------------------------------------------ plotPCA(rld, intgroup = c("dex", "cell")) ## ------------------------------------------------------------------------ library(ggplot2) mds <- data.frame(cmdscale(sampleDistMatrix)) mds <- cbind(mds, colData(rld)) qplot(X1,X2,color=dex,shape=cell,data=as.data.frame(mds)) ## ----plotMDS------------------------------------------------------------- suppressPackageStartupMessages({ library(limma) library(DESeq2) library(airway) }) plotMDS(assay(rld), col=as.integer(dds$dex), pch=as.integer(dds$cell)) ## ------------------------------------------------------------------------ set.seed(9) class = data.frame(condition = factor(rep(c(0, 1), c(29, 29)))) nTest = ceiling(ncol(cervical) * 0.2) ind = sample(ncol(cervical), nTest, FALSE) cervical.train = cervical[, -ind] cervical.train = as.matrix(cervical.train + 1) classtr = data.frame(condition = class[-ind, ]) cervical.test = cervical[, ind] cervical.test = as.matrix(cervical.test + 1) classts = data.frame(condition = class[ind, ]) ## ------------------------------------------------------------------------ cervical.trainS4 = DESeqDataSetFromMatrix(countData = cervical.train, colData = classtr, formula(~condition)) cervical.trainS4 = DESeq(cervical.trainS4, fitType = "local") cervical.testS4 = DESeqDataSetFromMatrix(countData = cervical.test, colData = classts, formula(~condition)) cervical.testS4 = DESeq(cervical.testS4, fitType = "local") ## ------------------------------------------------------------------------ svm = classify(data = cervical.trainS4, method = "svm", normalize = "deseq", deseqTransform = "vst", cv = 5, rpt = 3, ref = "1") svm ## ------------------------------------------------------------------------ getSlots("MLSeq") ## ------------------------------------------------------------------------ trained(svm) ## ------------------------------------------------------------------------ pred.svm = predictClassify(svm, cervical.testS4) table(pred.svm, relevel(cervical.testS4$condition, 2)) ## ------------------------------------------------------------------------ rf = classify(data = cervical.trainS4, method = "randomforest", normalize = "deseq", deseqTransform = "vst", cv = 5, rpt = 3, ref = "1") trained(rf) pred.rf = predictClassify(rf, cervical.testS4) table(pred.rf, relevel(cervical.testS4$condition, 2)) ## ------------------------------------------------------------------------ sessionInfo()