Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data.

ABSTRACT: Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated steps from normalization to cell clustering. However, assigning cell type labels to cell clusters is often conducted manually, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. This is partially due to the scarcity of reference cell type signatures and because some methods support limited cell type signatures. Methods: In this study, we benchmarked five methods representing first-generation enrichment analysis (ORA), second-generation approaches (GSEA and GSVA), machine learning tools (CIBERSORT) and network-based neighbor voting (METANEIGHBOR), for the task of assigning cell type labels to cell clusters

SUBMITTER: Diaz-Mejia JJ 

PROVIDER: S-EPMC6720041 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature