## ------------------------------------------------------------------------ 库(GEOquery ) ## ---- 缓存= TRUE --------------------------------------------------------- x x = getGEOSuppFiles(“GSE20986”) ## ------------------------------------------------------------------------ 解压(“GSE20986 / GSE20986_RAW.tar”,exdir = "数据 ") ## ------------------------------------------------------------------------ 塞尔=列表。文件(“数据/模式= "[广州]”)酸式焦磷酸钠(粘贴(“数据”,塞尔,9 = " / "),gunzip ) ## ------------------------------------------------------------------------ phenodata =矩阵(代表(list.files(“数据”),2),ncol = 2)类(phenodata) phenodata < - as.data.frame (phenodata) colnames (phenodata) < - c(“名字”,“文件名”)phenodata美元目标< - c(“爱丽丝”,“视网膜”、“视网膜”、“彩虹”,“视网膜”、“彩虹”,“脉络”、“脉络”、“脉络”、“huvec”,“huvec”、“huvec”)。表(phenodata,“数据/ phenodata.txt”,引用= F, 9 =“t \”,row.names = F ) ## ------------------------------------------------------------------------ 库(simpleaffy) celfiles < -阅读。affy(covdesc = "phenodata.txt"， path = "data") boxplot(celfiles) library(RColorBrewer) cols = brewer. txt$Targets colnames(eset) colnames(eset) <- samples boxplot(celfiles, col = cols, las = 2) distance <- dist(t(eset)，方法= "最大")集群< - hclust(距离)情节(集群 ) ## ------------------------------------------------------------------------ 需要(simpleaffy)要求(affyPLM) celfiles。Gcrma = Gcrma (celfiles) par(mfrow=c(1,2)) boxplot(celfiles。gcrma, col = cols, las = 2, main = "后归一化");dev.off() distance <- dist(t(exprs(celfiles.gcrma))，方法= "最大")集群< - hclust(距离)情节(集群 ) ## ------------------------------------------------------------------------ 库(limma) < - as.factor phenodata样本(样本)设计< - model.matrix(0 ~ +样品)colnames(设计)colnames(设计)< - c(“脉络”、“huvec”,“彩虹”,“视网膜”)设计的对比。matrix = makeContrasts(huvec_choroid = huvec - choroid, huvec_retina = huvec - retina, huvec_iris <- huvec - iris, levels = design)Gcrma，设计)huvec_fit <-反差。huvec_fit <- eBayes(huvec_fit) library(hgu133plus2.db) library(annotation) probenames. huvec_fit <- eBayes(huvec_fit) library(hgu133plus2.db) library(annotation) probenames。list <- rownames(topTable(huvec_ebay, number = 100000));list, "hgu133plus2") results <- topTable(huvec_ebay, number = 100000, coef = "huvec_choroid") results <- cbind(results, getsymbols) ## ------------------------------------------------------------------------ summary(results) results$threshold <- "1" a <- subset(results, adj.P.Val < 0.05 & logFC > 5) results[rownames(a), "threshold"] <- "2" b <- subset(results, adj.P.Val < 0.05 & logFC < -5) results[rownames(b), "threshold"] <- "3" table(results$threshold) ## ------------------------------------------------------------------------ library(ggplot2) volcano <- ggplot(data = results, aes(x = logFC, y = -1*log10(adj.P.Val), colour = threshold, label = getsymbols)) volcano <- volcano + geom_point() + scale_color_manual(values = c("black", "red", "green"), labels = c("Not Significant", "Upregulated", "Downregulated"), name = "Key/Legend") volcano + geom_text(data = subset(results, logFC > 5 & -1*log10(adj.P.Val) > 5), aes(x = logFC, y = -1*log10(adj.P.Val), colour = threshold, label = getsymbols) )