Abstract
Batch effects substantially impede the comparison of multiple single-cell experiment batches. Existing methods for batch effect removal and quantification primarily emphasize cell alignment across batches, often overlooking gene-level batch effects. Here we introduce group technical effects (GTE)—a quantitative metric to assess batch effects on individual genes. Using GTE, we show that batch effects unevenly impact genes within the dataset. A portion of highly batch-sensitive genes (HBGs) differ between datasets and dominate the batch effects, whereas non-HBGs exhibit low batch effects. We demonstrate that as few as three HBGs are sufficient to introduce substantial batch effects. Our method also enables the assessment of cell-level batch effects, outperforming existing batch effect quantification methods. We also observe that biologically similar cell types undergo similar batch effects, informing the development of data integration strategies. The GTE method is versatile and applicable to various single-cell omics data types.
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Data availability
All datasets used in this paper are publicly available. Specifically, the mouse MOp dataset can be accessed via the CELLxGENE portal at https://cellxgene.cziscience.com/collections/ae1420fe-6630-46ed-8b3d-cc6056a66467. The mouse retina and human cell line datasets are available at https://github.com/JinmiaoChenLab/Batch-effect-removal-benchmarking/tree/master/Data. The human cortical development and human PBMCs (CITE-seq) datasets can be accessed from the Gene Expression Omnibus (GEO) under accession codes GSE168408 and GSE156473, respectively. The human cortex dataset is available from https://cellxgene.cziscience.com/collections/35928d1c-36fc-4f93-9a8d-0b921ab41745. The human MTG dataset can be accessed via the Allen Brain Map portal at https://portal.brain-map.org/atlases-and-data/rnaseq. The human heart dataset is available from the Heart Cell Atlas project at https://www.heartcellatlas.org. The human PBMCs (scRNA-seq) dataset is accessible at https://github.com/satijalab/seurat-data (ref. 33). The TCGA READ bulk RNA-seq dataset can be accessed through Zenodo at https://doi.org/10.5281/zenodo.6392171 (ref. 34). The mouse brain scATAC-seq datasets (peak and gene activity versions) are available at https://doi.org/10.6084/m9.figshare.12420968.v8 (ref. 35). The mouse cell line proteomics dataset can be accessed via https://scproteomicsdb.com. Refer to Supplementary Table 2 for further details of the datasets. Processed datasets used for the analyses have been deposited to Zenodo at https://doi.org/10.5281/zenodo.13358933 (ref. 36). Source data are provided with this paper.
Code availability
The codes and Source data used to generate the results in this paper are available at GitHub (https://github.com/yzhou1999/GTEs; ref. 37) and at Zenodo (https://doi.org/10.5281/zenodo.15412860; ref. 38).
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (grant no. 62271173 to S.J., no. 62172122 to L.X. and no. 124B2027 to Y.Z.), the Key Research and Development Program of Heilongjiang (grant no. 2022ZX01A19 to S.J.), the Natural Science Foundation of Heilongjiang Province, China (grant no. JQ2023A003 to S.J.), and the Fundamental Research Funds for the Central Universities (grant no. HIT.DZJJ.2023133 to Q.S. and no. HIT.DZJJ.2024043 to Y.Z.).
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S.J. supervised the study. Y.Z. conceived and developed the method, and designed the analysis. Y.Z. and Q.S. performed the analysis. G.W. and L.X. checked the analysis results. Y.Z., Q.S., G.W., L.X. and S.J. wrote the paper. All authors read and approved the final paper.
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Nature Computational Science thanks Lachlan Coin, Debashis Ghosh and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Ananya Rastogi, in collaboration with the Nature Computational Science team.
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Supplementary Notes 1–3, Algorithm 1 and Figs. 1–22.
Supplementary Table 1
Identified common HBGs and non-HBGs.
Supplementary Table 2
Details of datasets used in the manuscript.
Source data
Source Data Fig. 1
The numerical Source data for Fig. 1.
Source Data Fig. 2
The numerical Source data for Fig. 2.
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Zhou, Y., Sheng, Q., Wang, G. et al. Quantifying batch effects for individual genes in single-cell data. Nat Comput Sci (2025). https://doi.org/10.1038/s43588-025-00824-7
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DOI: https://doi.org/10.1038/s43588-025-00824-7
