We developed group technical effects (GTE) as a quantitative metric for evaluating gene-level batch effects in single-cell data. It identifies highly batch-sensitive genes — the primary contributors to batch effects — that vary across datasets, and whose removal effectively mitigates the batch effects.
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References
Lander, E. S. Array of hope. Nat. Genet. 21, 3–4 (1999). This review discusses several issues in arrays, including the observation of batch effects.
Leek, J. T. et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11, 733–739 (2010). This review illustrates that the impact of batch effects on individual features in high-throughput data are widespread.
Büttner, M. et al. A test metric for assessing single-cell RNA-seq batch correction. Nat. Methods 16, 43–49 (2019). This paper reports a test metric to assess batch effects for single-cell RNA-seq data.
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019). This paper reports a metric to assess local batch separation in single-cell RNA-seq data.
Luecken, M. D. & Theis, F. J. Current best practices in singleâ€cell RNAâ€seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019). This paper argues that uncorrected data should be used for differential gene expression analysis.
Luecken, M. D. et al. Benchmarking atlas-level data integration in single-cell genomics. Nat. Methods 19, 41–50 (2022). This paper reports that HVG selection improves data integration performance.
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This is a summary of: Zhou, Y. et al. Quantifying batch effects for individual genes in single-cell data. Nat. Comput. Sci. https://doi.org/10.1038/s43588-025-00824-7 (2025).
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Unbalanced gene-level batch effects in single-cell data. Nat Comput Sci 5, 610–611 (2025). https://doi.org/10.1038/s43588-025-00829-2
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DOI: https://doi.org/10.1038/s43588-025-00829-2
