CeTF: an R/Bioconductor package for transcription factor co-expression networks using regulatory impact factors (RIF) and partial correlation and information (PCIT) analysis

Abstract

Background Finding meaningful gene-gene interaction and the main Transcription Factors (TFs) in co-expression networks is one of the most important challenges in gene expression data mining. Results Here, we developed the R package “CeTF” that integrates the Partial Correlation with Information Theory (PCIT) and Regulatory Impact Factors (RIF) algorithms applied to gene expression data from microarray, RNA-seq, or single-cell RNA-seq platforms. This approach allows identifying the transcription factors most likely to regulate a given network in different biological systems — for example, regulation of gene pathways in tumor stromal cells and tumor cells of the same tumor. This pipeline can be easily integrated into the high-throughput analysis. To demonstrate the CeTF package application, we analyzed gastric cancer RNA-seq data obtained from TCGA (The Cancer Genome Atlas) and found the HOXB3 gene as the second most relevant TFs with a high regulatory impact (TFs-HRi) regulating gene pathways in the cell cycle. Conclusion This preliminary finding shows the potential of CeTF to list master regulators of gene networks. CeTF was designed as a user-friendly tool that provides many highly automated functions without requiring the user to perform many complicated processes. It is available on Bioconductor (http://bioconductor.org/packages/CeTF) and GitHub (http://github.com/cbiagii/CeTF).

Gene Functional Networks from Time Expression Profiles: A Constructive Approach Demonstrated in Chili Pepper (Capsicum annuum L.)

“…Besides, the Gene2TF algorithm allows the selection of TF candidates to be regulating any gene of interest (see Supplementary SI.3.5). Even when the correlation in time of expression of a target gene with a TF candidate in various genotypes could suggest the possibility of co-regulation [46,47], other bioinformatic or experimental means are needed to corroborate such a role [36][37][38].…”

Transcriptomic signatures of progressive and regressive liver fibrosis and portal hypertension

“… 60 1.70.0 WGCNA 61 Langfelder and Horvath 61 1.71 biotmle 62 Hejazi et al. 62 1.22 CeTF 63 Oliveira de Biagi et al. 63 1.9.0 GENIE3 64 Huynh-Thu et al.…”

Haploid androgenetic development of bovine embryos reveals imbalanced WNT signaling and impaired cell fate differentiation

“…Additionally, we considered genes as differentially expressed if they were exclusive, expressed in one group (at least five counts in all technical replicates), and not expressed in the other group (zero counts in all technical replicates) within comparison and using the function filterByExpr from edgeR package [36]. We estimate the hub genes using CeTF [37] based on RIF—Regulatory Impact Factor and PCIT—Partial Correlation and Information Theory [38, 39]. Gene ontology analysis was performed using clusterProfiler [40] and pathways explored using Pathview [41].…”