Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data.

ABSTRACT: In single-cell RNA sequencing (scRNA-seq), doublets form when two cells are encapsulated into one reaction volume. The existence of doublets, which appear to be-but are not-real cells, is a key confounder in scRNA-seq data analysis. Computational methods have been developed to detect doublets in scRNA-seq data; however, the scRNA-seq field lacks a comprehensive benchmarking of these methods, making it difficult for researchers to choose an appropriate method for specific analyses. We conducted a systematic benchmark study of nine cutting-edge computational doublet-detection methods. Our study included 16 real datasets, which contained experimentally annotated doublets, and 112 realistic synthetic datasets. We compared doublet-detection methods regarding detection accuracy under various exp

SUBMITTER: Xi NM 

PROVIDER: S-EPMC7897250 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature