## ----style, echo=FALSE, results="asis", message=FALSE------------------------- BiocStyle::markdown() knitr::opts_chunk$set(tidy = FALSE, warning = FALSE, message = FALSE) ## ----echo=FALSE, results='hide', message=FALSE-------------------------------- suppressPackageStartupMessages(library(miRSM)) ## ----eval=TRUE, include=TRUE-------------------------------------------------- data(BRCASampleData) ## ----eval=TRUE, include=TRUE-------------------------------------------------- modulegenes_WGCNA <- module_WGCNA(ceRExp[, seq_len(80)], mRExp[, seq_len(80)]) modulegenes_WGCNA ## ----eval=TRUE, include=TRUE-------------------------------------------------- modulegenes_GFA <- module_GFA(ceRExp[seq_len(20), seq_len(15)], mRExp[seq_len(20), seq_len(15)], iter.max = 3000) modulegenes_GFA ## ----eval=TRUE, include=TRUE-------------------------------------------------- modulegenes_igraph <- module_igraph(ceRExp[, seq_len(10)], mRExp[, seq_len(10)]) modulegenes_igraph ## ----eval=TRUE, include=TRUE-------------------------------------------------- modulegenes_ProNet <- module_ProNet(ceRExp[, seq_len(10)], mRExp[, seq_len(10)]) modulegenes_ProNet ## ----eval=TRUE, include=TRUE-------------------------------------------------- # Reimport NMF package to avoid conflicts with DelayedArray package library(NMF) modulegenes_NMF <- module_NMF(ceRExp[, seq_len(10)], mRExp[, seq_len(10)]) modulegenes_NMF ## ----eval=TRUE, include=TRUE-------------------------------------------------- modulegenes_clust <- module_clust(ceRExp[, seq_len(30)], mRExp[, seq_len(30)]) modulegenes_clust ## ----eval=TRUE, include=TRUE-------------------------------------------------- modulegenes_biclust <- module_biclust(ceRExp[, seq_len(30)], mRExp[, seq_len(30)]) modulegenes_biclust ## ----eval=TRUE, include=TRUE-------------------------------------------------- modulegenes_igraph <- module_igraph(ceRExp[, seq_len(10)], mRExp[, seq_len(10)]) # Identify miRNA sponge modules using sensitivity RV coefficient (SRVC) miRSM_igraph_SRVC <- miRSM(miRExp, ceRExp, mRExp, miRTarget, modulegenes_igraph, num_shared_miRNAs = 3, pvalue.cutoff = 0.05, method = "SRVC", MC.cutoff = 0.8, SMC.cutoff = 0.01, RV_method = "RV") miRSM_igraph_SRVC ## ----eval=TRUE, include=TRUE-------------------------------------------------- nsamples <- 3 modulegenes_igraph_all <- module_igraph(ceRExp[, 151:300], mRExp[, 151:300]) modulegenes_WGCNA_exceptk <- lapply(seq(nsamples), function(i) module_WGCNA(ceRExp[-i, seq(150)], mRExp[-i, seq(150)])) miRSM_igraph_SRVC_all <- miRSM(miRExp, ceRExp[, 151:300], mRExp[, 151:300], miRTarget, modulegenes_igraph_all, method = "SRVC", SMC.cutoff = 0.01, RV_method = "RV") miRSM_WGCNA_SRVC_exceptk <- lapply(seq(nsamples), function(i) miRSM(miRExp[-i, ], ceRExp[-i, seq(150)], mRExp[-i, seq(150)], miRTarget, modulegenes_WGCNA_exceptk[[i]], method = "SRVC", SMC.cutoff = 0.01, RV_method = "RV")) Modulegenes_all <- miRSM_igraph_SRVC_all[[2]] Modulegenes_exceptk <- lapply(seq(nsamples), function(i) miRSM_WGCNA_SRVC_exceptk[[i]][[2]]) Modules_SS <- miRSM_SS(Modulegenes_all, Modulegenes_exceptk) Modules_SS ## ----eval=FALSE, include=TRUE------------------------------------------------- # modulegenes_WGCNA <- module_WGCNA(ceRExp[, seq_len(150)], # mRExp[, seq_len(150)]) # # Identify miRNA sponge modules using sensitivity RV coefficient (SRVC) # miRSM_WGCNA_SRVC <- miRSM(miRExp, ceRExp, mRExp, miRTarget, # modulegenes_WGCNA, method = "SRVC", # SMC.cutoff = 0.01, RV_method = "RV") # miRSM_WGCNA_SRVC_genes <- miRSM_WGCNA_SRVC[[2]] # miRSM_WGCNA_SRVC_FEA <- module_FA(miRSM_WGCNA_SRVC_genes, Analysis.type = 'FEA') # miRSM_WGCNA_SRVC_DEA <- module_FA(miRSM_WGCNA_SRVC_genes, Analysis.type = 'DEA') ## ----eval=TRUE, include=TRUE-------------------------------------------------- modulegenes_WGCNA <- module_WGCNA(ceRExp[, seq_len(150)], mRExp[, seq_len(150)]) # Identify miRNA sponge modules using sensitivity RV coefficient (SRVC) miRSM_WGCNA_SRVC <- miRSM(miRExp, ceRExp, mRExp, miRTarget, modulegenes_WGCNA, method = "SRVC", SMC.cutoff = 0.01, RV_method = "RV") miRSM_WGCNA_SRVC_genes <- miRSM_WGCNA_SRVC[[2]] miRSM.CEA.pvalue <- module_CEA(ceRExp, mRExp, BRCA_genes, miRSM_WGCNA_SRVC_genes) miRSM.CEA.pvalue ## ----eval=FALSE, include=TRUE------------------------------------------------- # # Using the built-in groundtruth from the miRSM package # Groundtruthcsv <- system.file("extdata", "Groundtruth_high.csv", package="miRSM") # Groundtruth <- read.csv(Groundtruthcsv, header=TRUE, sep=",") # # Using the identified miRNA sponge modules based on WGCNA and sensitivity RV coefficient (SRVC) # miRSM.Validate <- module_Validate(miRSM_WGCNA_SRVC_genes, Groundtruth) ## ----eval=TRUE, include=TRUE-------------------------------------------------- # Using the identified miRNA sponge modules based on WGCNA and sensitivity RV coefficient (SRVC) miRSM_WGCNA_Coexpress <- module_Coexpress(ceRExp, mRExp, miRSM_WGCNA_SRVC_genes, resample = 10, method = "mean", test.method = "t.test") miRSM_WGCNA_Coexpress ## ----eval=TRUE, include=TRUE-------------------------------------------------- # Using the identified miRNA sponge modules based on WGCNA and sensitivity RV coefficient (SRVC) miRSM_WGCNA_share_miRs <- share_miRs(miRExp, miRTarget, miRSM_WGCNA_SRVC_genes) miRSM_WGCNA_miRdistribute <- module_miRdistribute(miRSM_WGCNA_share_miRs) head(miRSM_WGCNA_miRdistribute) ## ----eval=FALSE, include=TRUE------------------------------------------------- # # Using the identified miRNA sponge modules based on WGCNA and sensitivity RV coefficient (SRVC) # miRSM_WGCNA_miRtarget <- module_miRtarget(miRSM_WGCNA_share_miRs, miRSM_WGCNA_SRVC_genes) ## ----eval=FALSE, include=TRUE------------------------------------------------- # # Using the identified miRNA sponge modules based on WGCNA and sensitivity RV coefficient (SRVC) # miRSM_WGCNA_miRsponge <- module_miRsponge(miRSM_WGCNA_SRVC_genes) ## ----------------------------------------------------------------------------- sessionInfo()