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

By Hoa T M Tran, Kok Siong Ang, Marion Chevrier, Xiaomeng Zhang, Nicole Yee Shin Lee, Michelle Goh, Jinmiao Chen. Genome Biology, 2020

985 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d5baf1912d43d0af0e8f683eb5532f5f5445430e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

In Molecular Biology

Citations in the corpus

Unsupervised spatially embedded deep representation of spatial transcriptomics (2021, methodology): “We first applied Harmony to remove the batch effects due to its superior 192 performance in scRNA-seq data integration.(34) Harmony was able to mix batches while 193 keeping different layers apart.”
Best practices for single-cell analysis across modalities (2023, methodology): “…such as canonical correlation analysis 36 and Harmony 37 were shown to perform well for batch correction on simpler integration tasks with distinct batch structures 38,39 . scANVI 40 can incorporate the cell-type labels, which is favourable as it can help to conserve biological variation 35 .”
Multi-omics single-cell data integration and regulatory inference with graph-linked embedding (2022, background): “Unpaired multi-omics integration shares some conceptual similarities with batch effect correction 50 , but the former is substantially more challenging because of the distinct, omics-specific feature spaces.”