# #——knitrSetup,包括= FALSE ------------------------------------------- 库(knitr) opts_chunk美元集(out.extra = '风格= "显示:块;保证金:汽车“fig.align = "中心",整洁= FALSE) # #——安装、eval = FALSE ------------------------------------------------- ## 源(“//www.anjoumacpherson.com/biocLite.R”)# # biocLite (rcellminer) # # biocLite(“rcellminerData ") ## ---- loadLibrary、消息= FALSE警告= FALSE --------------------------- 库(rcellminer)库(rcellminerData) # #——searchHelp eval = FALSE,整洁= FALSE ---------------------------------- ## help.search(“rcellminer ") ## ---- plotCellminer ------------------------------------------------------- # Cellminer数据drugAct < - exprs (getAct (rcellminerData:: drugData)) molData < - getMolDataMatrices() #两种药物nsc < - c(" 3284 ", " 739 ")情节< - c(“药物”、“药物”)plotCellMiner (nsc drugAct molData,阴谋,NULL) #只是药物nsc < -“94600”情节< - c(“药物”)plotCellMiner (nsc drugAct molData,情节,NULL) #只是表达基因<- "TP53"图<- c("exp") plotCellMiner(drugAct, molData，图，NULL，基因)#两个基因#注:下标越位错误可能意味着基因不存在于该数据类型基因<- c(“TP53”，“MDM2”)情节<- c(“exp”，“mut”，“exp”)plotCellMiner(drugAct, molData, plots, NULL，基因)#基因和药物情节nsc <-“94600”基因<-“TP53”情节<- c(“mut”，“drug”，“cop”)plotCellMiner(drugAct, molData, plots, nsc，基因)## ----plotDrugSets-------------------------------------------------------- #获取CellMiner数据drugAct <- exprs(getAct(rcellminerData::drugData)) #使用NSC IDs选择药物药物<- "26273 39367 39368 105546 120958 255523 284751 289900 736740 743891 752330" drugs <- strsplit(drugs， " ")[[1]] drugAct <- drugAct[drugs，] mainLabel <- paste("Drug Set: "1、Drugs:"， length(Drugs)， sep=" ") plotDrugSets(drugAct, Drugs, mainLabel) ## ----plotStructures------------------------------------------------------ plotStructuresFromNscs("Topotecan"， getSmiles("609699")) ## ---- compareprints, results='hide'， message=FALSE, warning=FALSE---- #加载sqldf库(sqldf) #设置必要的数据##复合注释df <- as(featureData(getAct(rcellminerData::drugData))，"data.frame") ##药物活性drugAct <- exprs(getAct(rcellminerData::drugData)) ##分子分析数据molData <- getMolDataMatrices() #化合物特定属性的示例筛选tmpDf <- sqldf("SELECT NSC, SMILES FROM df WHERE SMILES != " ") #与100个NSCs进行比较以获得演示id <- head(tmpDf$NSC, 100) SMILES <- head(tmpDf$SMILES，100) #所有公共#id <- tmpDf$nsc #smiles <- tmpDf$smiles drugOfInterest <- "MK2206" smilesOfInterest <- "C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N" #制作所有化合物的向量进行成对比较id <- C(drugOfInterest, ids) smiles <- C(smilesOfInterest, smiles) ## ----runComparison, results='hide'， message=FALSE------------------------ #运行指纹比较结果<- compareprints (ids， smiles) ## ----plotSimilarStructures----------------------------------------------- # Plot top 2 results resultsIdx <- sapply(names(results)[2:3], function(x) { which(tmpDf$NSC == x) }) resultsIds <- names(results)[2:3] resultsSmiles <- tmpDf$SMILES[resultsIdx] resultsIds <- c(drugOfInterest, resultsIds) resultsSmiles <- c(smilesOfInterest, resultsSmiles) plotStructuresFromNscs(resultsIds, resultsSmiles, titleCex=0.5, mainLabel="Fingerprint Results") ## ----plotCellMiner------------------------------------------------------- nscs <- names(results)[2:3] plotCellMiner(drugAct=drugAct, molData=molData, plots=rep("drug", length(nscs)), nscs, NULL) ## ----makeDrugInfoTable, results='hide', message=FALSE-------------------- drugAnnot <- as(featureData(getAct(rcellminerData::drugData)), "data.frame") knownMoaDrugs <- unique(c(getMoaToCompounds(), recursive = TRUE)) knownMoaDrugInfo <- data.frame(NSC=knownMoaDrugs, stringsAsFactors = FALSE) knownMoaDrugInfo$Name <- drugAnnot[knownMoaDrugInfo$NSC, "NAME"] knownMoaDrugInfo$MOA <- vapply(knownMoaDrugInfo$NSC, getMoaStr, character(1)) # Order drugs by mechanism of action. knownMoaDrugInfo <- knownMoaDrugInfo[order(knownMoaDrugInfo$MOA), ] ## ----computeGI50Data, results='hide', message=FALSE---------------------- negLogGI50Data <- getDrugActivityData(nscSet = knownMoaDrugInfo$NSC) gi50Data <- 10^(-negLogGI50Data) ## ----makeIntegratedTable, results='hide', message=FALSE------------------ knownMoaDrugAct <- as.data.frame(cbind(knownMoaDrugInfo, gi50Data), stringsAsFactors = FALSE) # This table can be written out to a file #write.table(knownMoaDrugAct, file="knownMoaDrugAct.txt", quote=FALSE, sep="\t", row.names=FALSE, col.names=TRUE, na="NA") ## ----sessionInfo--------------------------------------------------------- sessionInfo()