# #设置,回声= FALSE,结果= '隐藏 '----------------------------------- 库(knitr) # opts_chunk美元设置(图。align="center"， fig.width=7, fig.height=7) #options(width=90) ## ----lib, message=FALSE, results='hide'， warning=FALSE------------------- library(flowVS) #load library ## ----HD, echo=TRUE, warning=FALSE, fig.keep='high'， fig.show='hold'， out.width='。49\\linewidth'， fig.cap='在高清数据中转换两个荧光通道。巴特利特统计量(y轴)是由不同余因子转换数据后的密度峰值(x轴)计算得出的。在巴特利特统计量最小(由红圈表示)的地方获得最佳辅因子。'---- ##例1:来自flowVS包数据的健康数据(HD) ##识别CD3和CD4通道辅助因子的最佳辅助因子= estParamFlowVS(HD[1:5]，channels=c('CD3'， 'CD4')) ## ----densityHD, echo=TRUE, warning=FALSE, fig.cap='使用最佳辅助因子对数据进行asins变换后的密度图。“---- ##转换所有样本中的CD3和CD4通道。VS = transFlowVS(HD，通道=c('CD3'， 'CD4')，辅助因子)##密度图(来自flowViz包)VS, main=" HD数据中转换的CD3和CD4通道")## ----ITN, echo=TRUE, warning=FALSE, fig.keep='high'， fig.show='hold'， out.width='。49\\linewidth'， fig.cap='转换ITN数据中的两个荧光通道。巴特利特统计量(y轴)是由不同余因子转换数据后的密度峰值(x轴)计算得出的。在巴特利特统计量最小(由红圈表示)的地方获得最佳辅因子。'---- ##例2:来自flowStats包suppressMessages(library(flowStats)) data(ITN) #识别淋巴细胞ITN。lymphs = fsApply(ITN,lymphs, list("FS"=c(200, 600),"SS"=c(0, 400)), "FSC", "SSC",FALSE) ## identify optimum cofactor for CD3 and CD4 channels cofactors = estParamFlowVS(ITN.lymphs[1:5],channels=c('CD3', 'CD4')) ## ----densityITN, echo=TRUE, warning=FALSE, fig.cap='The density plots after the data is transformed by asins transformation with the optimum cofactors.'---- ## transform CD4 channel in all samples ITN.VS = transFlowVS(ITN.lymphs, channels=c('CD3', 'CD4'), cofactors) ## density plot (from flowViz package) densityplot(~CD3+CD4, ITN.VS, main="Transfromed CD3 and CD4 channels in ITN data") ## ----microVS, echo=TRUE, warning=FALSE, fig.width=5, fig.height=5-------- suppressMessages(library(vsn)) data(kidney) kidney.microVS = microVS(exprs(kidney)) #variance stabilization ## ----vsn, echo=TRUE, warning=FALSE, fig.width=4.5, fig.height=4, fig.show='hold', fig.align='center', fig.cap='Variance stabilization of the Kidney microarray data by flowVs and vsn packages.'---- suppressMessages(library(vsn)) data(kidney) kidney.vsn = vsn2(exprs(kidney)) #variance stabilization by vsn plotMeanSd(kidney.microVS, main="Kidney data: VS by flowVS") plotMeanSd(exprs(kidney.vsn), main="Kidney data: VS by vsn") ## ----sessionInfo, results = 'asis', eval = TRUE, echo = TRUE------------- toLatex(sessionInfo())