## ----include=FALSE--------------------------------------------------------- library(RTNduals) data("tniData", package = "RTN") gexp <- tniData$expData annot <- tniData$rowAnnotation tfs <- c("IRF8","IRF1","PRDM1","E2F3","STAT4","LMO4","ZNF552") ## ----eval=FALSE------------------------------------------------------------ # ##--- load package and dataset for demonstration # library(RTNduals) # data("tniData", package = "RTN") # gexp <- tniData$expData # annot <- tniData$rowAnnotation # tfs <- c("IRF8","IRF1","PRDM1","E2F3","STAT4","LMO4","ZNF552") ## ----include=FALSE--------------------------------------------------------- ##--- generate a pre-processed TNI-class object rtni <- tni.constructor(gexp, regulatoryElements = tfs, rowAnnotation=annot) ## ----eval=FALSE------------------------------------------------------------ # ##--- generate a pre-processed TNI-class object # rtni <- tni.constructor(gexp, regulatoryElements = tfs, rowAnnotation=annot) ## ----include=FALSE--------------------------------------------------------- ##--- compute a regulatory network (set nPermutations>=1000) rtni <- tni.permutation(rtni, nPermutations=100, pValueCutoff=0.05, verbose=FALSE) ## ----eval=FALSE------------------------------------------------------------ # ##--- compute a regulatory network (set nPermutations>=1000) # rtni <- tni.permutation(rtni, nPermutations=100, pValueCutoff=0.05) ## ----include=FALSE--------------------------------------------------------- ##--- check stability of the regulatory network (set nBootstrap>=100) rtni <- tni.bootstrap(rtni, nBootstrap=10, verbose=FALSE) ## ----eval=FALSE------------------------------------------------------------ # ##--- check stability of the regulatory network (set nBootstrap>=100) # rtni <- tni.bootstrap(rtni, nBootstrap=10) ## ----include=FALSE--------------------------------------------------------- ##--- Compute the DPI-filtered regulatory network rtni <- tni.dpi.filter(rtni, eps = NA, verbose=FALSE) ## ----eval=FALSE------------------------------------------------------------ # ##--- Compute the DPI-filtered regulatory network # # Note: we recommend setting 'eps = NA' in order to # # estimate the threshold from the empirical null # # distribution computed in the permutation and # # bootstrap steps. # rtni <- tni.dpi.filter(rtni, eps = NA) ## ----include=FALSE--------------------------------------------------------- ##--- construct an mbr object and apply DPI algorithm rmbr <- tni2mbrPreprocess(rtni) ## ----eval=FALSE------------------------------------------------------------ # ##--- construct an mbr object and apply DPI algorithm # rmbr <- tni2mbrPreprocess(rtni) ## ----include=FALSE--------------------------------------------------------- ##--- test associations for dual regulons rmbr <- mbrAssociation(rmbr, verbose=FALSE) ## ----eval=FALSE------------------------------------------------------------ # ##--- test associations for dual regulons # rmbr <- mbrAssociation(rmbr) ## ----eval=TRUE------------------------------------------------------------- ##--- check summary mbrGet(rmbr, what="summary") ## ----eval=TRUE------------------------------------------------------------- ##--- get results overlap <- mbrGet(rmbr, what="dualsOverlap") correlation <- mbrGet(rmbr, what="dualsCorrelation") ## ----label='Session information', eval=TRUE, echo=FALSE-------------------- sessionInfo()