Batch Effect Correction of RNA-seq Data through Sample Distance Matrix Adjustment

Abstract

Batch effect is a frequent challenge in deep sequencing data analysis that can lead to misleading conclusions. We present scBatch, a numerical algorithm that conducts batch effect correction on the count matrix of RNA sequencing (RNA-seq) data. Different from traditional methods, scBatch starts with establishing an ideal correction of the sample distance matrix that effectively reflect the underlying biological subgroups, without considering the actual correction of the raw count matrix itself. It then seeks an optimal linear transformation of the count matrix to approximate the established sample pattern. The benefit of such an approach is the final result is not restricted by assumptions on the mechanism of the batch effect. As a result, the method yields good clustering and gene differential expression (DE) results. We compared the new method, scBatch, with leading batch effect removal methods ComBat and mnnCorrect on simulated data, real bulk RNA-seq data, and real single-cell RNA-seq data. The comparisons demonstrated that scBatch achieved better sample clustering and DE gene detection results.