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

ABSTRACT: 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.

SUBMITTER: Skok Gibbs C

PROVIDER: S-EPMC11003171 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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

Skok Gibbs Claudia C Mahmood Omar O Bonneau Richard R Cho Kyunghyun K

Genome biology 20240408 1

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 comp ...[more]

PMID: 38589899

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