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<p>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.</p>

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Quantifying batch effects for individual genes in single-cell data

  • 期刊论文

  • 发表日期：

    2025年6月27日

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  • Yang Zhou,
  • Qiongyu Sheng,
  • Guohua Wang,
  • Li Xu,
  • Shuilin Jin

摘要

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.