Highly Effective Batch Effect Correction Method for RNA-seq Count Data

Abstract

AbstractRNA sequencing (RNA-seq) has become a cornerstone in transcriptomics, offering detailed insights into gene expression across diverse biological conditions and sample types. However, RNA-seq data often suffer from batch effects, which are systematic non-biological differences that compromise data reliability and obscure true biological variation. To address these challenges, we introduce ComBat-ref, a refined method of batch effect correction that enhances the statistical power and reliability of differential expression analysis in RNA-seq data. Building on the foundations of ComBat-seq, ComBat-ref employs a negative binomial model to adjust count data but innovates by using a pooled dispersion parameter for entire batches and preserving count data for the reference batch. Our method demonstrated superior performance in both simulated environments and real datasets, such as the growth factor receptor network (GFRN) data and NASA GeneLab transcriptomic datasets, significantly improving sensitivity and specificity over existing methods. By effectively mitigating batch effects while maintaining high detection power, ComBat-ref proves to be a robust tool for enhancing the accuracy and interpretability of RNA-seq data analyses.