# # # R代码从装饰图案来源的小插曲/延时/本月/ doc / UsingMLP。Rnw“# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #代码块1号:loadData # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #要求(MLP)需要(limma) pathExampleData < -系统。文件(“exampleFiles”、“expressionSetGcrma。rda”,包=“延时”)负载(pathExampleData) #如果需要(mouse4302mmentrezg.db))(! #注释(expressionSetGcrma) < -“mouse4302”注释(expressionSetGcrma) < - - - - - -“mouse4302”# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #代码块2号:preparePValues # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #计算统计值通过limma组< - as.numeric(因素(pData (expressionSetGcrma) subGroup1美元水平= c (“WT”、“KO”))) < - 1设计model.matrix(~组)符合< - lmFit (exprs (expressionSetGcrma),设计)fit2 < - ebay(合适的)结果< - limma::: topTable (fit2,系数=“集团”,调整。方法=“罗斯福”,[=正)pvalues < -结果数,“P。价值”)的名字(pvalues) < - rownames(结果)#因为我们转向使用“_at”,下一步也应该需要名字(pvalues) < - - - - - -子(“_at”、“名称(pvalues)) # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #代码块3号:createGeneSets # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # geneSet <——getGeneSets(物种=“鼠标”,geneSetSource =“GOCC entrezIdentifiers =名字(pvalues))的尾巴(geneSet, 3) # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #代码块数量4:showAttributes # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # str(属性(geneSet)) # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #代码块5号:runMLP # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # set.seed (111) mlpOut < - MLP (geneSet = geneSet geneStatistic = pvalues minGenes = 5, maxGenes = 100, rowPermutations = TRUE, nPermutations = 50, smoothPValues = TRUE, probabilityVector = c (0.5, 0.9, 0.95, 0.99, 0.999, 0.9999, 0.99999), df = 9)头(mlpOut) # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #代码块6号:showAttributes # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # str(属性(mlpOut)) # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #代码块7号:quantileCurves # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # pdf (“mlpQuantileCurves。pdf”,宽度= 10,身高= 10)情节(mlpOut、类型=“quantileCurves”) dev.off() # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #代码块8号:barplot # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # pdf (“mlpBarplot。pdf”,宽度= 10,身高= 10)op < - par (mar = c(30、10 6 2)情节(mlpOut、类型=“barplot”)票面(op) dev.off() # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #代码块9号:GOgraph # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # pdf (“mlpGOgraph。pdf”,宽度= 8,身高= 6)op < - par (mar = c(0, 0, 0, 0))情节(mlpOut类型=“GOgraph”, nRow = 10)票面(op) dev.off() # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #代码块10号:geneSignificance # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # geneSetID < - rownames (mlpOut) [1] pdf (“geneSignificance。pdf”,宽度= 10,身高= 10)op < - par (mar = c(2) 25日,10日,6日)plotGeneSetSignificance (geneSet = geneSet geneSetIdentifier = geneSetID geneStatistic = pvalues annotationPackage =注释(expressionSetGcrma))票面(op) dev.off ()