A model-based factorization method for scRNA data unveils bifurcating transcriptional modules underlying cell fate determination.

ABSTRACT: Manifold-learning is particularly useful to resolve the complex cellular state space from single-cell RNA sequences. While current manifold-learning methods provide insights into cell fate by inferring graph-based trajectory at cell level, challenges remain to retrieve interpretable biology underlying the diverse cellular states. Here, we described MGPfact^XMBD, a model-based manifold-learning framework and capable to factorize complex development trajectories into independent bifurcation processes of gene sets, and thus enables trajectory inference based on relevant features. MGPfact^XMBD offers a more nuanced understanding of the biological processes underlying cellular trajectories with potential determinants. When bench-tested across 239 datasets, MGPfact^XMBD</

SUBMITTER: Ren J 

PROVIDER: S-EPMC11798574 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature