scBatch: batch-effect correction of RNA-seq data through sample distance matrix adjustment
Teng Fei, Tianwei Yu
Abstract
MOTIVATION: Batch effect is a frequent challenge in deep sequencing data analysis that can lead to misleading conclusions. Existing methods do not correct batch effects satisfactorily, especially with single-cell RNA sequencing (RNA-seq) data. RESULTS: We present scBatch, a numerical algorithm for batch-effect correction on bulk and single-cell RNA-seq data with emphasis on improving both clustering and gene differential expression analysis. scBatch is not restricted by assumptions on the mechanism of batch-effect generation. As shown in simulations and real data analyses, scBatch outperforms benchmark batch-effect correction methods. AVAILABILITY AND IMPLEMENTATION: The R package is available at github.com/tengfei-emory/scBatch. The code to generate results and figures in this article is available at github.com/tengfei-emory/scBatch-paper-scripts. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.