Benchmarking atlas-level data integration in single-cell genomics.

ABSTRACT: Single-cell atlases often include samples that span locations, laboratories and conditions, leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets requires reliable data integration. To guide integration method choice, we benchmarked 68 method and preprocessing combinations on 85 batches of gene expression, chromatin accessibility and simulation data from 23 publications, altogether representing >1.2 million cells distributed in 13 atlas-level integration tasks. We evaluated methods according to scalability, usability and their ability to remove batch effects while retaining biological variation using 14 evaluation metrics. We show that highly variable gene selection improves the performance of data integration methods, whereas scaling pushes methods to pr

SUBMITTER: Luecken MD 

PROVIDER: S-EPMC8748196 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature