CellRank 2: unified fate mapping in multiview single-cell data.

ABSTRACT: Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or RNA velocity to reconstruct state-change trajectories; however, trajectory inference does not incorporate valuable time point information or utilize additional modalities, whereas methods that address these different data views cannot be combined or do not scale. Here we present CellRank 2, a versatile and scalable framework to study cellular fate using multiview single-cell data of up to millions of cells in a unified fashion. CellRank 2 consistently recovers terminal states and fate probabilities across data modalities in human hematopoiesis and endodermal development. Our framework also allows combining transitions within and across experimental time points, a feature

SUBMITTER: Weiler P 

PROVIDER: S-EPMC11239496 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature