# #——回声= FALSE ------------------------------------------------------------ 美元knitr: opts_chunk集(= "发表评论,消息= FALSE,警告= FALSE) # # - eval = FALSE ------------------------------------------------------------- # 如果(!requireNamespace(“BiocManager”,悄悄地= TRUE)) # install.packages (BiocManager) # BiocManager::安装(“NBAMSeq ") ## ----------------------------------------------------------------------------- 库(NBAMSeq ) ## ----------------------------------------------------------------------------- ## 的一个例子countData n = 50 # #代表数量的基因m = 20 # # m代表样本容量countData =矩阵(rnbinom (n * m,μ= 100,大小= 1/3),ncol = m) + 1模式(countData) =“整数”colnames (countData) = paste0(“样本”,1:m) rownames (countData) = paste0(“基因”,1:n)头(countData ) ## ----------------------------------------------------------------------------- ## 的一个例子colData把= runif (m, 20, 80) var1 = rnorm (m) var2 = rnorm (m) var3 = rnorm (m) var4 = as.factor(样本(c (0, 1, 2), m,取代= TRUE)) colData = data.frame(把=把var1 = var1 var2 = var2 var3 = var3,var4 = var4) rownames (colData) = paste0(“样本”,1:m)头(colData ) ## ----------------------------------------------------------------------------- 设计= ~ s(把)+ var1 + var2 + var3 + var4 ## ----------------------------------------------------------------------------- 德牧= NBAMSeqDataSet (countData = countData colData = colData,设计=设计)德牧 ## ----------------------------------------------------------------------------- 德牧= NBAMSeq (gsd) # # - eval = TRUE ---------------------------------------------------------------- 图书馆(BiocParallel)德牧= NBAMSeq(德牧,并行= TRUE ) ## ----------------------------------------------------------------------------- res1 =结果(德牧,name = "把")头(res1 ) ## ----------------------------------------------------------------------------- 它=结果(德牧,name = " var1”)(它 ) ## ----------------------------------------------------------------------------- res3 =结果(德牧,对比= c(“var4”、“2”、“0”))头(res3 ) ## ----------------------------------------------------------------------------- ## 假设我们有兴趣gene10之间非线性关系的# #表达和“把”makeplot(德牧,phenoname =“把”,genename =“gene10”,主要= " gene10 ") ## ----------------------------------------------------------------------------- ## 这里我们探讨最重要的非线性协会res1 = res1[订单(res1 pvalue美元)]topgene = rownames (res1)[1]科幻= getsf(德牧)# # # #得到估计的大小因素原始数除以大小因素获得归一化计算countnorm = t (t (countData) / sf)头(res1 ) ## ----------------------------------------------------------------------------- 库(ggplot2) setTitle = topgene df =data.frame(pheno =pheno, logcount = log2(countnorm[topgene，]+1)) ggplot(df, aes(x=pheno, y=logcount))+geom_point(shape=19,size=1)+ geom_smooth(method='黄土')+xlab("pheno")+ylab("log(normcount +1) ")+ annotation ("text"， x= max(df$pheno)-5, y= max(df$logcount)-1, label = paste0("edf: "， signif(res1[topgene，"edf"]，digits = 4)))+ ggtitle(setTitle)+ theme(text = element_text(size=10)， plot。标题= element_text (hjust = 0.5 )) ## ---- sessionInfo -------------------------------------------------------------- sessionInfo ()