Dropout imputation and batch effect correction for single-cell RNA sequencing data

Single-cell RNA sequencing(scRNA-seq)allows researchers to examine the transcriptome at the single-cell level and has been increasingly employed as technologies continue to advance.Due to technical and biological reasons unique to scRNA-seq data,denoising and batch effect correction are almost indispensable to ensure valid and powerful data analysis.However,various aspects of scRNA-seq data pose grand challenges for such essential tasks pertaining to data pre-processing,normalization or harmonization.In this review,we first discuss properties of scRNA-seq data that contribute to the challenges for denoising and batch effect correction from a computational perspective.We then focus on reviewing several state-of-the-art methods for dropout imputation and batch effect correction,comparing their strengths and weaknesses.Finally,we benchmarked three widely used correction tools using two hematopoietic scRNA-seq datasets to show their performance in a real data application.