# #设置,包括= FALSE --------------------------------------------------- knitr: opts_chunk美元集(崩溃= TRUE,评论= " # > ",作物相关零# # = https://stat.ethz.ch/pipermail/bioc-devel/2020-April/016656.html ) ## ---- 层次结构、消息= FALSE。宽度= " 600 px "-------------------------- 库(scp)的数据(“scp1”)情节(scp1) # #——assay_data --------------------------------------------------------------- 试验(scp1,“190321 s_lca10_x_fp97ag”)[1:5,) # #——名字 -------------------------------------------------------------------- 名(scp1)构成了rowData。# #— ------------------------------------------------------------------ rowData构成了rowData (scp1)[[”(scp1)的蛋白质 "]] ## ---- rowDataNames ------------------------------------------------------------- rowDataNames (scp1) # #——rbindRowData ------------------------------------------------------------- rbindRowData (scp1我= 1:5)# #——colData ------------------------------------------------------------------ colData (scp1) # #——colData_dollar ----------------------------------------------------------- scp1 SampleType # #——subset_assay美元 ------------------------------------------------------------- 190321 s_lca10_x_fp97ag scp1(,。 "] ## ---- subsetByAssay ----------------------------------------------------------- subsetByAssay (scp1“190321 s_lca10_x_fp97ag ") ## ---- subset_samples ---------------------------------------------------------- scp1 [scp1 SampleType美元= = "巨噬细胞 ", ] ## ---- subsetByColData --------------------------------------------------------- subsetByColData (scp1 scp1 $ SampleType = = "巨噬细胞 ") ## ---- subset_features ---------------------------------------------------------- scp1 [" Q02878 ", , ] ## ---- subsetByFeature --------------------------------------------------------- subsetByFeature (scp1“Q02878 ") ## ---- filterFeatures ---------------------------------------------------------- filterFeatures (scp1 ~相反 != "+") ## ---- filterNA ---------------------------------------------------------------- filterNA (scp1 i =“蛋白质”,机构= 0.7 ) ## ---- zeroIsNA ---------------------------------------------------------------- 表(化验(scp1肽)= = 0)scp1 < -zeroIsNA (scp1“肽”)表(化验(scp1,“肽”)= = 0)# #——aggregateFeatures -------------------------------------------------------- aggregateFeatures (scp1,我=“190321”s_lca10_x_fp97ag fcol =“蛋白质”,名字=“190321 s_lca10_x_fp97ag_aggr”,有趣= MsCoreUtils:: robustSummary) # #——正常化 ---------------------------------------------------------------- 正常化(scp1,“蛋白质”,方法= "中心。的意思是”,name = " proteins_mcenter ") ## ---- 扫描 -------------------------------------------------------------------- 科幻小说< - colSums(化验(scp1,“蛋白质”),na。rm = TRUE) / 1E4 sweep(scp1, i = "proteins"， MARGIN = 2， ## 1 = by feature;由样本数据=科幻,2 = = " / ",name = " proteins_sf ") ## ---- logTransform ------------------------------------------------------------- logTransform (scp1 i =“蛋白质”,基础= 2,pc = 1, name = " proteins_log ") ## ---- 嫁祸于 ------------------------------------------------------------------- anyNA(化验(scp1,“蛋白质”))scp1 < -转嫁(scp1,我=“蛋白质”,方法=“资讯”,k = 3) anyNA(化验(蛋白质scp1。 ")) ## ---- vis1、消息= FALSEfig.width = 6.5 ----------------------------------- rd < - rbindRowData (scp1,我= 1:3)图书馆(ggplot2) ggplot (data.frame (rd)) + aes (y =论坛,x =化验)+ geom_boxplot () ## ---- longFormat --------------------------------------------------------------- 如果< - longFormat (scp1 [,, 1], colvars = c(“SampleType”、“通道”))ggplot (data.frame(低频))+ aes (x =频道,y =价值,颜色= SampleType) + geom_boxplot () ## ---- setup2,包括= FALSE -------------------------------------------------- knitr: opts_chunk美元集(崩溃= TRUE,评论= ",作物=零 ) ## ---- sessioninfo,回声= FALSE -------------------------------------------------- sessionInfo ()