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

Author(s)

Oliveira de Biagi Jr, Carlos Alberto ; Nociti, Ricardo Perecin ^{[1, 2]} ; Brotto, Danielle Barbosa ^{[3, 1]} ; Funicheli, Breno Osvaldo ^[1] ; Ruy, Patricia de Cassia ^{[4, 1]} ; Bianchi Ximenez, Joao Paulo ^[1] ; Alves Figueiredo, David Livingstone ^{[5, 6]} ; Silva Jr, Wilson Araujo

Affiliation

^[1] Oliveira de Biagi Jr, Jr., Carlos Alberto, Ctr Cell Based Therapy CEPID FAPESP, Reg Blood Ctr Ribeirao Preto, Natl Inst Sci & Technol Stem Cell & Cell Therapy, Ribeirao Preto - Brazil

^[2] Univ Sao Paulo, Fac Anim Sci & Food Engn, Dept Vet Med, Lab Mol Morphophysiol & Dev, Pirassununga - Brazil

^[3] Oliveira de Biagi Jr, Jr., Carlos Alberto, Univ Sao Paulo, Dept Genet, Ribeirao Preto Med Sch, Ribeirao Preto - Brazil

^[4] HCFMRP USP, Ctr Med Genom, Ribeirao Preto - Brazil

^[5] Oliveira de Biagi Jr, Jr., Carlos Alberto, IPEC, Inst Canc Res, Guarapuava - Brazil

^[6] Midwest State Univ Parana UNICTR, Dept Med, Guarapuava - Brazil

• Document type: Journal article
• Source: BMC Genomics; v. 22, n. 1 AUG 20 2021.
• Web of Science Citations: 0

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. (AU)

• FAPESP's process: 13/08135-2 - CTC - Center for Cell-Based Therapy
• Grantee: Dimas Tadeu Covas
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC