A benchmark of batch-effect correction methods for single-cell RNA sequencing data.

ABSTRACT:

Background

Large-scale single-cell transcriptomic datasets generated using different technologies contain batch-specific systematic variations that present a challenge to batch-effect removal and data integration. With continued growth expected in scRNA-seq data, achieving effective batch integration with available computational resources is crucial. Here, we perform an in-depth benchmark study on available batch correction methods to determine the most suitable method for batch-effect removal.

Results

We compare 14 methods in terms of computational runtime, the ability to handle large datasets, and batch-effect correction efficacy while preserving cell type purity. Five scenarios are designed for the study: identical cell types with different technologies, non-identical cel

SUBMITTER: Tran HTN 

PROVIDER: S-EPMC6964114 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature