ArticleBioinformatics (Oxford, England)2021
Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- Disentangling factors affecting bacterial transcriptional regulatory network inference.iScience · 2026Article
- Large-scale, interpretable gene regulatory network inference through biologically informed matrix factorization.bioRxiv : the preprint server for biology · 2026Article
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- A network-based model of Aspergillus fumigatus elucidates regulators of development and defensive natural products of an opportunistic pathogen.Nucleic acids research · 2026Article
- Predicting input signals of transcription factors in Escherichia coli.Molecular systems biology · 2025Article
- Mapping the transcriptional regulatory network of a fungal pathogen by exploiting transcription factor perturbation.bioRxiv : the preprint server for biology · 2025Article
- Clinical value of circulating splicing factors in prostate cancer: SRRM1 as a novel predictive biomarker and therapeutic target.Molecular therapy. Oncology · 2024Article
- BIOTIC: a Bayesian framework to integrate single-cell multi-omics for transcription factor activity inference and improve identity characterization of cells.Briefings in bioinformatics · 2024Article
- Transcriptome data are insufficient to control false discoveries in regulatory network inference.Cell systems · 2024Article
- Prostate cancers with distinct transcriptional programs in Black and White men.Genome medicine · 2024Article
- Flexible modeling of regulatory networks improves transcription factor activity estimation.NPJ systems biology and applications · 2024Article
- Mapping Single-Cell Transcriptomes of Endometrium Reveals Potential Biomarkers in Endometrial Cancer.ImmunoTargets and therapy · 2024Article
- Navigating the archaeal frontier: insights and projections from bioinformatic pipelines.Frontiers in microbiology · 2024Article
- GOAT: Gene-level biomarker discovery from multi-Omics data using graph ATtention neural network for eosinophilic asthma subtype.Bioinformatics (Oxford, England) · 2023Article
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- Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data.G3 (Bethesda, Md.) · 2023Article
- High-performance single-cell gene regulatory network inference at scale: the Inferelator 3.0.Bioinformatics (Oxford, England) · 2022Article
- A genome-scale yeast library with inducible expression of individual genes.Molecular systems biology · 2021Article
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2 authors.
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Abstract
motivationThe activity of a transcription factor (TF) in a sample of cells is the extent to which it is exerting its regulatory potential. Many methods of inferring TF activity from gene expression data have been described, but due to the lack of appropriate large-scale datasets, systematic and objective validation has not been possible until now.
resultsWe systematically evaluate and optimize the approach to TF activity inference in which a gene expression matrix is factored into a condition-independent matrix of control strengths and a condition-dependent matrix of TF activity levels. We find that expression data in which the activities of individual TFs have been perturbed are both necessary and sufficient for obtaining good performance. To a considerable extent, control strengths inferred using expression data from one growth condition carry over to other conditions, so the control strength matrices derived here can be used by others. Finally, we apply these methods to gain insight into the upstream factors that regulate the activities of yeast TFs Gcr2, Gln3, Gcn4 and Msn2. AVAILABILITY AND IMPLEMENTATION: Evaluation code and data are available at https://doi.org/10.5281/zenodo.4050573. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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