Reference-informed evaluation of batch correction for single-cell omics data with overcorrection awareness

作者：Xiaoyue Hu, He Li, Ming Chen, Junbin Qian, Hangjin Jiang · 发表于：Communications Biology · 年份：2025 · DOI：10.1038/s42003-025-07947-7 · 被引用次数：13 · 研究领域：Single-cell and spatial transcriptomics、Gene expression and cancer classification、Gene Regulatory Network Analysis

Batch effect correction (BEC) is fundamental to integrate multiple single-cell RNA sequencing datasets, and its success is critical to empower in-depth interrogation for biological insights. However, no simple metric is available to evaluate BEC performance with sensitivity to data overcorrection, which erases true biological variations and leads to false biological discoveries. Here, we propose RBET, a reference-informed statistical framework for evaluating the success of BEC. Using extensive simulations and six real data examples including scRNA-seq and scATAC-seq datasets with different numbers of batches, batch effect sizes and numbers of cell types, we demonstrate that RBET evaluates the performance of BEC methods more fairly with biologically meaningful insights from data, while other methods may lead to false results. Moreover, RBET is computationally efficient, sensitive to overcorrection and robust to large batch effect sizes. Thus, RBET provides a robust guideline on selecting case-specific BEC method, and the concept of RBET is extendable to other modalities. Reference-informed statistical method provides robust guidance on case-specific selection of batch effect correction methods for single-cell omics data with awareness to over-correction, and facilitates biologically meaningful insights from data