## ----设置，包括=假--------------------------------------------- knitr :: opts_chunk $ set（dpi = 300）knitr :: opts_chunk $ set（cache = false）## ---- echo = false，隐藏= true，消息= false，警告= false -------------------- devtools :: load_all（“。）## ---- eval = false --------------------------------------------------------------------------＃if（！qualocmamespace（“biocmanager”，静静= true））#sipling.packages（“biocmanager”）＃biocmanager ::安装（“starbiotrek”）## ---- eval = true ---------------------------------------------------------------------------图书馆（石墨）sel <-papitydatabases（）## ---- eval = true，回声= false -------------------------------------------- knitr :: Kable（Sel，Digits = 2，Caption =“List的Patwhay数据库和物种“，row.names = false）## ---- eval = true ----------------------------------------------------------------- incees =“hsapiens”pathwaydb =“kegg”路径<-getdata（物种，pathwaydb）## ----评估=假-------------------------------------------------------------------＃帕特hway_allgene <-getpatpdata（path_all = path [1：3]）## ---- eval = false ------------------------------------------------------＃pathway_net <-getpathnet（path_all = path [1：3]）##---- eval = true -------------------------------------------------------衔接<-convertedidgenes（path_all = path [1:10]）## ---- eval = true --------------------------------------------------------------------------- organismID="Saccharomyces_cerevisiae" netw<-getNETdata(network="SHpd",organismID) ## ---- eval = TRUE------------------------------------------------------------- lista_net<-pathnet(genes.by.pathway=pathway[1:5],data=netw) ## ---- eval = TRUE------------------------------------------------------------- list_path<-listpathnet(lista_net=lista_net,pathway=pathway[1:5]) ## ---- eval = TRUE------------------------------------------------------------- list_path_gene<-GE_matrix(DataMatrix=tumo[,1:2],genes.by.pathway=pathway[1:10]) ## ---- eval = TRUE------------------------------------------------------------- list_path_plot<-GE_matrix_mean(DataMatrix=tumo[,1:2],genes.by.pathway=pathway[1:10]) ## ---- eval = FALSE------------------------------------------------------------ # score_mean<-average(pathwayexpsubset=list_path_gene) ## ---- eval = TRUE------------------------------------------------------------- score_st_dev<-stdv(gslist=list_path_gene) ## ---- eval = FALSE------------------------------------------------------------ # score_euc_distance<-eucdistcrtlk(dataFilt=tumo[,1:2],pathway_exp=pathway[1:10]) ## ---- eval = FALSE------------------------------------------------------------ # cross_talk_st_dv<-dsscorecrtlk(dataFilt=tumo[,1:2],pathway_exp=pathway[1:10]) ## ---- eval = FALSE------------------------------------------------------------ # nf <- 60 # res_class<-svm_classification(TCGA_matrix=score_euc_dista[1:30,],nfs=nf, # normal=colnames(norm[,1:10]),tumour=colnames(tumo[,1:10])) ## ---- eval = FALSE------------------------------------------------------------ # DRIVER_SP<-IPPI(pathax=pathway_matrix[,1:3],netwa=netw_IPPI[1:50000,]) ## ---- eval = TRUE------------------------------------------------------------- formatplot<-plotcrosstalk(pathway_plot=pathway[1:6],gs_expre=tumo) library(qgraph) qgraph(formatplot[[1]], minimum = 0.25, cut = 0.6, vsize = 5, groups = formatplot[[2]], legend = TRUE, borders = FALSE,layoutScale=c(0.8,0.8)) ## ---- eval = TRUE------------------------------------------------------------- qgraph(formatplot[[1]],groups=formatplot[[2]], layout="spring", diag = FALSE, cut = 0.6,legend.cex = 0.5,vsize = 6,layoutScale=c(0.8,0.8)) ## ---- eval = FALSE------------------------------------------------------------ # formatplot<-plotcrosstalk(pathway_plot=pathway[1:6],gs_expre=tumo) # score<-runif(length(formatplot[[2]]), min=-10, max=+10) # circleplot(preplot=formatplot,scoregene=score) ## ---- fig.width=6, fig.height=4, echo=FALSE, fig.align="center"--------------- library(png) library(grid) img <- readPNG("circleplot.png") grid.raster(img) ## ----sessionInfo-------------------------------------------------------------- sessionInfo()