## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(dpi = 300) knitr::opts_chunk$set(cache=FALSE) ## ----echo = FALSE,hide=TRUE, message=FALSE,warning=FALSE---------------------- devtools::load_all(".") ## ----eval = FALSE------------------------------------------------------------- # if (!requireNamespace("BiocManager", quietly=TRUE)) # install.packages("BiocManager") # BiocManager::install("StarBioTrek") ## ----eval = TRUE-------------------------------------------------------------- library(graphite) sel<-pathwayDatabases() ## ----eval = TRUE, echo = FALSE------------------------------------------------ knitr::kable(sel, digits = 2, caption = "List of patwhay databases and species",row.names = FALSE) ## ----eval = TRUE-------------------------------------------------------------- species="hsapiens" pathwaydb="kegg" path<-GetData(species,pathwaydb) ## ----eval = FALSE------------------------------------------------------------- # pathway_ALLGENE<-GetPathData(path_ALL=path[1:3]) ## ----eval = FALSE------------------------------------------------------------- # pathway_net<-GetPathNet(path_ALL=path[1:3]) ## ----eval = TRUE-------------------------------------------------------------- pathway<-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()