A Bayesian noisy logic model for inference of transcription factor activity from single cell and bulk transcriptomic data.

ABSTRACT: The advent of high-throughput sequencing has made it possible to measure the expression of genes at relatively low cost. However, direct measurement of regulatory mechanisms, such as transcription factor (TF) activity is still not readily feasible in a high-throughput manner. Consequently, there is a need for computational approaches that can reliably estimate regulator activity from observable gene expression data. In this work, we present a noisy Boolean logic Bayesian model for TF activity inference from differential gene expression data and causal graphs. Our approach provides a flexible framework to incorporate biologically motivated TF-gene regulation logic models. Using simulations and controlled over-expression experiments in cell cultures, we demonstrate that our method can accura

SUBMITTER: Arriojas A 

PROVIDER: S-EPMC10716740 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature