A comparison of methods accounting for batch effects in differential expression analysis of UMI count based single cell RNA sequencing
Open accessAbstract
Accounting for batch effects, especially latent batch effects, in differential expression (DE) analysis is critical for identifying true biological effects. Single-cell RNA sequencing (scRNA-seq) is a powerful tool for quantifying cell-to-cell variation in transcript abundance and characterizing cellular dynamics. Although many scRNA-seq DE analysis methods accommodate known batch variables, their performance has not been systematically evaluated. Moreover, the challenge of accounting for latent batch variables in scRNA-seq DE analysis is largely unmet. In contrast, many methods have been developed to account for batch variables (either known or latent) in other high-dimensional data, especially bulk RNA-seq. We extensively evaluate 11 methods for batch variables in different scRNA-seq DE analysis scenarios, with a primary focus on latent batch variables. We demonstrate that for known batch variables, incorporating them as covariates into a regression model outperformed approaches using a batch-corrected matrix. For latent batches, fixed effects models have inflated FDRs, whereas aggregation-based methods and mixed effects models have significant power loss. Surrogate variable based methods generally control the FDR well while achieving good power with small group effects. However, their performance (except that of SVA) deteriorated substantially in scenarios involving large group effects and/or group label impurity. In these settings, SVA achieves relatively good performance despite an occasionally inflated FDR (up to 0.2). Finally we make the following recommendations for scRNA-seq DE analysis: 1) incorporate known batch variables instead of using batch-corrected data; and 2) employ SVA for latent batch correction. However, better methods are still needed to fully unleash the power of scRNA-seq.
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Under an unconfounded experimental design with batches containing both controls and cases, batch effects can be removed by adding a batch covariate to the regression model, and this increases power compared to non-batch corrected analyses.
“However, under the assumptions of an unconfounded experimental design with batches containing both controls and cases, batch effects can be removed by adding a batch covariate to the regression model71. This increases the power compared to non-batch corrected analyses72–74.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [71]
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- Authors
- Chen, W., Zhang, S., Williams, J., Ju, B., Shaner, B., Easton, J., Wu, G., & Chen, X.
- Affiliations
- St. Jude Children's Research Hospital (US)
- Corresponding author
- Chen, Xiang
- ORCID
- Venue
- Computational and Structural Biotechnology Journal
- Published
- 2020-01
- Volume
- 18
- Pages
- 861-873
- ISSN
- 2001-0370 (ISSN-L)
- PMC
- PMC7163294
- OpenAlex
- W3014125121
- Cited by
- 44
- References
- 36
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 4 · 2023: 11 · 2022: 3 · 2021: 14 · 2020: 3
- FWCI
- 2.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computational and Structural Biotechnology Journal (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- American Lebanese Syrian Associated Charities
- National Institutes of Health (P30CA021765)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- batch effects, differential expression analysis, single-cell RNA sequencing, surrogate variable analysis, fixed effects model, mixed models, false discovery rate, group effect
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