RTNduals: an R/Bioconductor package for analysis of co-regulation and inference of dual regulons

Abstract

Motivation Transcription factors (TFs) are key regulators of gene expression, and can activate or repress multiple target genes, forming regulatory units, or regulons. Understanding downstream effects of these regulators includes evaluating how TFs cooperate or compete within regulatory networks. Here we present RTNduals, an R/Bioconductor package that implements a general method for analyzing pairs of regulons. Results RTNduals identifies a dual regulon when the number of targets shared between a pair of regulators is statistically significant. The package extends the RTN (Reconstruction of Transcriptional Networks) package, and uses RTN transcriptional networks to identify significant co-regulatory associations between regulons. The Supplementary Information reports two case studies for TFs using the METABRIC and TCGA breast cancer cohorts. Availability and implementation RTNduals is written in the R language, and is available from the Bioconductor project at http://bioconductor.org/packages/RTNduals/. Supplementary information Supplementary data are available at Bioinformatics online.

Stage-stratified molecular profiling of non-muscle-invasive bladder cancer enhances biological, clinical, and therapeutic insight

“…Regulon analysis 25 , 26 , 27 identified 373 with significant activity. These were strongly associated with subtypes, confirming their biologically distinct features ( Figure S4 F).…”

“…To identify regulators of molecular subtypes identified, we analyzed regulatory networks (regulons) for a comprehensive set of 1547 transcription factors 94 using RTN. 25 , 26 , 27 Gene level normalization (using SST-RMA) and signal summarization was conducted using Affymetrix® Expression Console Software. We inferred the regulons using the R package RTN (version 2.13.2), which is described elsewhere.…”

Identification and Verification of m7G Modification Patterns and Characterization of Tumor Microenvironment Infiltration via Multi-Omics Analysis in Clear Cell Renal Cell Carcinoma

“…To further investigate the transcriptome differences, we analyzed regulons for m 7 G subtype-specific transcription factors from the obtained lists of renal cancer-associated transcription factors ( 26 , 54 , 55 ) using R package RTNduals ( 56 ), which rendered strong support to the biological pertinency of the three-classification because the regulon activity was closely related to m 7 G subtypes ( Figure 3C ). We also noted that ZEB2 exhibited the lowest activity in the MGCS2 group, suggesting the inhibition of the EMT process in this subtype.…”

spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape

“…lncRNAs with an experimentally verified role as ceRNAs could then serve as important biomarkers and potential therapeutic drug targets. We note that while we have applied spongEffects here to ceRNA networks inferred with SPONGE, our method is generally applicable to arbitrary ceRNA networks and potentially also to gene-regulatory networks, where similar concepts have already been explored (Castro et al ., 2016; Chagas et al ., 2019). We foresee two interesting directions for further research.…”