Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data

Abstract

Background: The 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. Results: Using a new dataset, we systematically evaluate and optimize the approach to TF activity inference in which a gene expression matrix is factored into a conditionindependent matrix of control strengths and a condition-dependent matrix of TF activity levels. These approaches require a TF network map, which specifies the target genes of each TF, as input. We evaluate different approaches to building the network map and deriving constraints on the matrices. We find that such constraints are essential for good performance. Constraints can be obtained from expression data in which the activities of individual TFs have been perturbed, and we find that such data are both necessary and sufficient for obtaining good performance. Remaining uncertainty about whether a TF activates or represses a target is a major source of error. To a considerable extent, control strengths inferred using expression data from one growth condition carry over to other conditions. As a result, the control strength matrices derived here can be used for other applications. Finally, we apply these methods to gain insight into the upstream factors that regulate the activities of four yeast TFs: Gcr2, Gln3, Gcn4, and Msn2. Evaluation code and data available at https://github.com/BrentLab/TFA-evaluation Conclusions: When a high-quality network map, constraints, and perturbation-response data are available, inferring TF activity levels by factoring gene expression matrices is effective. Furthermore, it provides insight into regulators of TF activity.

High performance single-cell gene regulatory network inference at scale: The Inferelator 3.0

“…Here we describe the Inferelator 3.0 pipeline for single-cell GRN inference, based on regularized regression (Bonneau et al, 2006). This pipeline calculates TF activity (Ma and Brent, 2021) using a prior knowledge network and regresses scRNAseq expression data against that activity estimate to learn new regulatory edges. We compare it directly to two other network inference methods that also utilize prior network information and scRNAseq data, benchmarking using real-world Saccharomyces cerevisiae scRNAseq data and comparing to a high-quality gold standard network.…”