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

Carlos Alberto Oliveira De Biagi,Breno Osvaldo Funicheli,Danielle Barbosa Brotto,David Livingstone Alves Figueiredo,Ricardo Perecin Nociti,Wilson Araújo Silva,João Paulo Bianchi Ximenez,Patrícia De Cássia Ruy

doi:10.1186/s12864-021-07918-2

Abstract

BackgroundFinding 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.ResultsHere, 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.ConclusionThis 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).

Highlights

ResultsWe developed the R package “Coexpression for Transcription Factors (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
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
The elevated expression of the SOX4 gene has been described to regulate the epithelial-mesenchymal transition (EMT) mechanism mediated by TGF-beta [23]

Summary

Results

To demonstrate the tool’s utility, we used stomach adenocarcinoma RNA-seq data from The Cancer Genome Atlas (TCGA) project [18] and applied all analyzes available in the CeTF package. Some studies show that high expression of the SETD3 gene is associated with poor survival in triple-negative breast cancer [19], while HOXB3 and FOXA1 were identified as indicators of better prognosis [20,21,22]. A total of 8,037 genes remained in the analysis and are represented, with 151 upregulated genes (red dots) and 118 down-regulated genes (blue dots). On this set of genes, 7 TFs are up-regulated (green dots), 9 TFs are down-regulated (pink dots) and 504 are not differentially expressed. The Chip-seq data from one of our studies (unpublished data) were used to

Conclusion

Background

Summary statistics from two variables

Conclusions