ScRegulate: single-cell regulatory-embedded variational inference of transcription factor activity from gene expression.

ABSTRACT:

Motivation

Accurately inferring transcription factor (TF) activity from single-cell RNA sequencing (scRNA-seq) data remains a fundamental challenge in computational biology. While existing methods rely on statistical models, motif enrichment, or prior-based inference, they often depend on deterministic assumptions about regulatory relationships and rely on static regulatory databases. Few approaches effectively integrate prior biological knowledge with data-driven inference to capture novel, dynamic, and context-specific regulatory interactions.

Results

To address these limitations, we develop scRegulate, a generative deep-learning framework leveraging variational inference to estimate TF activities guided by experimental TF-target gene relationships and progressively adapte

SUBMITTER: Zandigohar M 

PROVIDER: S-EPMC12701802 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature