Loading…

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

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 avai...

Full description

Saved in:
Bibliographic Details
Published in:Genome Biology 2020-01, Vol.21 (1), p.12-12, Article 12
Main Authors: Tran, Hoa Thi Nhu, Ang, Kok Siong, Chevrier, Marion, Zhang, Xiaomeng, Lee, Nicole Yee Shin, Goh, Michelle, Chen, Jinmiao
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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. 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 cell types, multiple batches, big data, and simulated data. Performance is evaluated using four benchmarking metrics including kBET, LISI, ASW, and ARI. We also investigate the use of batch-corrected data to study differential gene expression. Based on our results, Harmony, LIGER, and Seurat 3 are the recommended methods for batch integration. Due to its significantly shorter runtime, Harmony is recommended as the first method to try, with the other methods as viable alternatives.
ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-019-1850-9