# #——风格,呼应= FALSE,结果= ' asis '----------------------------------- BiocStyle:减价 () ## ---- eval = FALSE ---------------------------------------------------------- # y = beta0 + beta1 *身高+ beta2 *重量+ beta3 * shoe_size # #——消息= FALSE ------------------------------------------------------- 库(气管)数据(“气道”)se < -气道colData (se)库(DESeq2) dds < - DESeqDataSet细胞(se设计= ~ +敏捷)# #——消息= FALSE ------------------------------------------------------- 图书馆(MLSeq) filepath =执行(“extdata / cervical.txt”、包=“MLSeq”)颈=阅读。表(filepath,头= TRUE ) ## ------------------------------------------------------------------------ 行< - rlog (dds)头(化验(行 )) ## ------------------------------------------------------------------------ sampleDists < -区域(t(化验(行)))sampleDists # #——消息= FALSE ------------------------------------------------------- 库(“gplots”)sampleDistMatrix <- as. lib ("RColorBrewer")<- paste(rld$dex, rld$cell, sep="-") color <- colorRampPalette(rev(brewer. txt);pal(9，“蓝调”))(255)hc <- hclust(样本)热图。2 (sampleDistMatrix Rowv = as.dendrogram (hc)、symm = TRUE,跟踪=“没有”,上校=颜色,利润率= c (2, 10), labCol = FALSE ) ## ------------------------------------------------------------------------ plotPCA(行,intgroup = c(“敏捷”,“细胞 ")) ## ------------------------------------------------------------------------ 库(ggplot2) mds < - data.frame (cmdscale (sampleDistMatrix)) mds < cbind (mds,colData(行))qplot (X1, X2,颜色=敏捷,形状=细胞,data = as.data.frame (mds )) ## ---- plotMDS ------------------------------------------------------------- suppressPackageStartupMessages({库(limma)图书馆(DESeq2)图书馆(气管)})plotMDS(化验(行),坳= as.integer (dds敏捷美元),pch = as.integer (dds)美元细胞 )) ## ------------------------------------------------------------------------ set.seed(9)类= data.frame(条件=因素(代表(c (0, 1), c(29岁,29))))nt =上限(ncol(颈)* 0.2)印第安纳=样本(ncol(颈),nt, FALSE)颈。Train = cervical[， -ind]颈椎。火车= as.matrix(颈。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()