Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics.

ABSTRACT: Advances in single-cell technologies allow scrutinizing of heterogeneous cell states, however, detecting cell-state transitions from snap-shot single-cell transcriptome data remains challenging. To investigate cells with transient properties or mixed identities, we present MuTrans, a method based on multiscale reduction technique to identify the underlying stochastic dynamics that prescribes cell-fate transitions. By iteratively unifying transition dynamics across multiple scales, MuTrans constructs the cell-fate dynamical manifold that depicts progression of cell-state transitions, and distinguishes stable and transition cells. In addition, MuTrans quantifies the likelihood of all possible transition trajectories between cell states using coarse-grained transition path theory. Downstream

SUBMITTER: Zhou P 

PROVIDER: S-EPMC8460805 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature