Open AccessVeloViz: RNA velocity-informed embeddings for visualizing cellular trajectories
Author(s) -
Lyla Atta,
Arpan Sahoo,
Jean Fan
Publication year - 2021
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btab653
Subject(s) - bioconductor , computer science , transcriptome , source code , embedding , visualization , computational biology , rna , theoretical computer science , algorithm , data mining , biology , gene expression , artificial intelligence , gene , genetics , operating system
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.