PMF-GRN: a variational inference approach to single-cell gene regulatory network inference using probabilistic matrix factorization.

Autor: Skok Gibbs C; Center for Data Science, New York University, New York, NY, 10011, USA., Mahmood O; Center for Data Science, New York University, New York, NY, 10011, USA., Bonneau R; Center for Data Science, New York University, New York, NY, 10011, USA.; Prescient Design, Genentech, New York, NY, 10010, USA.; Center for Genomics and Systems Biology, New York University, New York, NY, 10003, USA., Cho K; Center for Data Science, New York University, New York, NY, 10011, USA. kyunghyun.cho@nyu.edu.; Prescient Design, Genentech, New York, NY, 10010, USA. kyunghyun.cho@nyu.edu.
Jazyk: angličtina
Zdroj: Genome biology [Genome Biol] 2024 Apr 08; Vol. 25 (1), pp. 88. Date of Electronic Publication: 2024 Apr 08.
DOI: 10.1186/s13059-024-03226-6
Abstrakt: Inferring gene regulatory networks (GRNs) from single-cell data is challenging due to heuristic limitations. Existing methods also lack estimates of uncertainty. Here we present Probabilistic Matrix Factorization for Gene Regulatory Network Inference (PMF-GRN). Using single-cell expression data, PMF-GRN infers latent factors capturing transcription factor activity and regulatory relationships. Using variational inference allows hyperparameter search for principled model selection and direct comparison to other generative models. We extensively test and benchmark our method using real single-cell datasets and synthetic data. We show that PMF-GRN infers GRNs more accurately than current state-of-the-art single-cell GRN inference methods, offering well-calibrated uncertainty estimates.
(© 2024. The Author(s).)
Databáze: MEDLINE
Externí odkaz: