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

ABSTRACT: 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 othe

SUBMITTER: Hu X 

PROVIDER: S-EPMC11954866 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature