A comprehensive comparison of supervised and unsupervised methods for cell type identification in single-cell RNA-seq.

ABSTRACT: The cell type identification is among the most important tasks in single-cell RNA-sequencing (scRNA-seq) analysis. Many in silico methods have been developed and can be roughly categorized as either supervised or unsupervised. In this study, we investigated the performances of 8 supervised and 10 unsupervised cell type identification methods using 14 public scRNA-seq datasets of different tissues, sequencing protocols and species. We investigated the impacts of a number of factors, including total amount of cells, number of cell types, sequencing depth, batch effects, reference bias, cell population imbalance, unknown/novel cell type, and computational efficiency and scalability. Instead of merely comparing individual methods, we focused on factors' impacts on the general category of super

SUBMITTER: Sun X 

PROVIDER: S-EPMC8921620 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature