# #设置,包括= FALSE ----------------------------------------------------- knitr: opts_chunk美元集(echo = TRUE) # #——bc-installation eval = FALSE -------------------------------------------- # # 如果(!requireNamespace(“BiocManager”,悄悄地= TRUE)) # # install.packages (BiocManager) # BiocManager::安装(“MBQN ") ## ---- dependencies1 eval = FALSE ---------------------------------------------- # # 如果(!requireNamespace("BiocManager"， quietly = TRUE)) ## install.packages("BiocManager") ## BiocManager::install(pkgs = c("preprocessCore"，"limma"， " summarize实验"))## ----example1, eval = TRUE---------------------------------------------------- ##基本示例库("MBQN") set.seed(1234) #数据生成mtx <- mbqnSimuData("omics.dep") #数据失真mtx <- mbqnSimuDistortion(mtx)$x。mod ## ----figure1, Fig. height = 5, Fig. width = 6, Fig. align = "left"， Fig. cap = "图1未归一化、畸变强度数据矩阵箱线图。第一个特性是RI特性(红线)。它对每个样品都有最大强度!”——plot.new () mbqnBoxplot (mtx, irow = 1,主要= "非规范 ") ## ---- eval = TRUE ------------------------------------------------------------- res < - mbqnGetNRIfeatures (mtx, low_thr = 0.5) # #——figure2 fig.height = 5, fig.width = 6, fig.align =‘左’,fig.cap = "图2分位数归一化强度与平衡和不平衡的规范化RI特性。经典分位数归一化抑制了RI特征的任何强度变化，而MBQN保留了其变化，同时减少了系统批效应!---- plot.new() mbqn。mtx <- mbqnNRI(x = mtx, FUN = median, verbose = FALSE) # MBQN qn。mtx <- mbqnNRI(x = mtx, FUN = NULL, verbose = FALSE) # QN mbqnBoxplot(mbqn。mtx, irow = res $ ip, vals = data.frame (QN = qn.mtx [res ip美元]),主要= "规范化 ") ## ---- example2 eval = TRUE ---------------------------------------------------- ## 基本示例库(MBQN) set.seed (1234) mtx <——mbqnSimuData (omics.dep) #或者:mtx < -矩阵(# c(5、2、3,NA, 2、4、1、4、2、3、1,4,6,NA, 1, 3, NA, 1, 4, 3, NA, 1, 2, 3), ncol = 4 ) ## ---- eval = TRUE ------------------------------------------------------------- qn。mtx <- mbqn(mtx,FUN=NULL, verbose = FALSE)mtx < - mbqn (mtx,有趣=“中位数”,verbose = FALSE) qn.nri.mtx < mbqnNRI(简称mtx,有趣=“中位数”,low_thr = 0.5,详细= FALSE ) ## ---- eval = TRUE ------------------------------------------------------------- res < - mbqnGetNRIfeatures (mtx, low_thr = 0.5) #最大频率的RI /新名词特性(s): 100 % ## ---- 青年们,eval = TRUE ---------------------------------------------------- # plot.new () mtx < mbqnSimuData(“omics.dep”节目。fig = FALSE) mod.mtx <- mbqnSimuDistortion(mtx, s.mean = 0.05, s.scale = 0.01) mtx2 <- mod.mtx mod.mtx <- mod.mtx$x。mod res <- mbqnGetNRIfeatures(mod。mtx, low_thr = 0.5) # undistorted feature feature1 <- mtx[1,] # distorted feature feature1mod = mod.mtx[1,] # feature after normalization qn.feature1 = mbqn(mod.mtx, verbose = FALSE)[1,] qn.mtx = mbqn(mod.mtx,verbose = FALSE) mbqn.mtx = mbqn(mod.mtx, FUN = "mean",verbose = FALSE) mbqn.feature1 = mbqn(mod.mtx, FUN = "mean",verbose = FALSE)[1,] ## ---- eval = TRUE------------------------------------------------------------- # undistorted feature ttest.res0 <- t.test(feature1[seq_len(9)], feature1[c(10:18)], var.equal =TRUE) # distorted feature ttest.res1 <- t.test(feature1mod[seq_len(9)], feature1mod[c(10:18)], var.equal =TRUE) # mbqn normalized distorted feature ttest.res <- t.test(mbqn.feature1[seq_len(9)], mbqn.feature1[c(10:18)], var.equal =TRUE) ## ---- eval = TRUE------------------------------------------------------------- ## ----figure3, fig.height = 5, fig.width = 6, fig.align = "left", fig.cap = "Fig. 3 "---- plot.new() matplot(t(rbind(feature1 = feature1, mod.feature1 = (feature1mod-mean(feature1mod))/25+mean(feature1), qn.feature1 = (qn.feature1-mean(qn.feature1))+mean(feature1), mbqn.feature1 = ( mbqn.feature1-mean(mbqn.feature1))+mean(feature1))), type = "b", lty = c(1,1,1), pch = "o", ylab = "intensity", xlab = "sample", main = "Differentially expressed RI feature", ylim = c(34.48,34.85)) legend(x=11,y= 34.86, legend = c("feature","distorted feature/25" , "QN feature", " MBQN feature"),pch = 1, col = c(1,2,3,4), lty= c(1,1,1,1), bty = "n", y.intersp = 1.5, x.intersp = 0.2) legend(x = .1, y = 34.6, legend = paste("p-value (t-test) =",round(ttest.res1$p.value,2), "\np-value (t-test, mbqn) =", round(ttest.res$p.value,4)), bty = "n", x.intersp = 0) if (ttest.res$p.value<0.05) message("H0 (=equal mean) is rejected!") # print(mtx2$x.mod) # print(mtx2$mx.offset) # print(mtx2$mx.scale) print(paste("ttest.undistorted =",ttest.res0)) print(paste("ttest.distorted =", ttest.res1)) print(paste("ttest.mbqndistorted =", ttest.res))