Quantifying batch effects for individual genes in single-cell data

Abstract Batch effects significantly impede the comparison of multiple single-cell experiment batches. Existing batch effect removal methods primarily focus on aligning cells 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 technical genes (HTGs) differ between datasets and dominate the batch effects. Removing these genes effectively integrates the dataset. We demonstrate that as few as three HTGs are sufficient to introduce substantial batch effects. Furthermore, we 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.