## ----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-------------------------------- 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=FALSE, include=TRUE------------------------------------------------ # modulegenes_GFA <- module_GFA(ceRExp[seq_len(20), seq_len(15)], # mRExp[seq_len(20), seq_len(15)], # iter.max = 2600) # 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=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 miRspongeR package # library(miRspongeR) # Groundtruthcsv <- system.file("extdata", "Groundtruth.csv", package="miRspongeR") # 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, ceRExp, mRExp, 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(ceRExp, mRExp, miRSM_WGCNA_SRVC_genes) ## ----------------------------------------------------------------------------- sessionInfo()