VeloViz: RNA velocity-informed embeddings for visualizing cellular trajectories.

ABSTRACT:

Motivation

Single-cell transcriptomics profiling technologies enable genome-wide gene expression measurements in individual cells but can currently only provide a static snapshot of cellular transcriptional states. RNA velocity analysis can help infer cell state changes using such single-cell transcriptomics data. To interpret these cell state changes inferred from RNA velocity analysis as part of underlying cellular trajectories, current approaches rely on visualization with principal components, t-distributed stochastic neighbor embedding and other 2D embeddings derived from the observed single-cell transcriptional states. However, these 2D embeddings can yield different representations of the underlying cellular trajectories, hindering the interpretation of cell state changes.

SUBMITTER: Atta L 

PROVIDER: S-EPMC8723140 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature