Flexible modeling of regulatory networks improves transcription factor activity estimation.
NPJ Syst Biol Appl
; 10(1): 58, 2024 May 28.
Article
in En
| MEDLINE
| ID: mdl-38806476
ABSTRACT
Transcriptional regulation plays a crucial role in determining cell fate and disease, yet inferring the key regulators from gene expression data remains a significant challenge. Existing methods for estimating transcription factor (TF) activity often rely on static TF-gene interaction databases and cannot adapt to changes in regulatory mechanisms across different cell types and disease conditions. Here, we present a new algorithm - Transcriptional Inference using Gene Expression and Regulatory data (TIGER) - that overcomes these limitations by flexibly modeling activation and inhibition events, up-weighting essential edges, shrinking irrelevant edges towards zero through a sparse Bayesian prior, and simultaneously estimating both TF activity levels and changes in the underlying regulatory network. When applied to yeast and cancer TF knock-out datasets, TIGER outperforms comparable methods in terms of prediction accuracy. Moreover, our application of TIGER to tissue- and cell-type-specific RNA-seq data demonstrates its ability to uncover differences in regulatory mechanisms. Collectively, our findings highlight the utility of modeling context-specific regulation when inferring transcription factor activities.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Transcription Factors
/
Algorithms
/
Computational Biology
/
Gene Regulatory Networks
Limits:
Humans
Language:
En
Journal:
NPJ Syst Biol Appl
Year:
2024
Document type:
Article
Affiliation country:
Country of publication: