Statistical significance of cluster membership for unsupervised evaluation of cell identities.

ABSTRACT: MOTIVATION:Single-cell RNA-sequencing (scRNA-seq) allows us to dissect transcriptional heterogeneity arising from cellular types, spatio-temporal contexts and environmental stimuli. Transcriptional heterogeneity may reflect phenotypes and molecular signatures that are often unmeasured or unknown a priori. Cell identities of samples derived from heterogeneous subpopulations are then determined by clustering of scRNA-seq data. These cell identities are used in downstream analyses. How can we examine if cell identities are accurately inferred? Unlike external measurements or labels for single cells, using clustering-based cell identities result in spurious signals and false discoveries. RESULTS:We introduce non-parametric methods to evaluate cell identities by testing cluster memberships in a

SUBMITTER: Chung NC 

PROVIDER: S-EPMC7214036 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature