A semi-supervised Bayesian approach for marker gene trajectory inference from single-cell RNA-seq data.

ABSTRACT:

Motivation

Trajectory inference methods are essential for extracting temporal ordering from static single-cell transcriptomic profiles, thus facilitating the accurate delineation of cellular developmental hierarchies and cell-fate transitions. However, numerous existing methods treat trajectory inference as an unsupervised learning task, rendering them susceptible to technical noise and data sparsity, which often lead to unstable reconstructions and ambiguous lineage assignments.

Results

Here, we introduce BayesTraj, a semi-supervised Bayesian framework that incorporates prior knowledge of lineage topology and marker-gene expression to robustly reconstruct differentiation trajectories from scRNA-seq data. BayesTraj models cellular differentiation as a probabilistic mixture o

SUBMITTER: Wang J 

PROVIDER: S-EPMC12410927 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature