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

• Main Authors: Gang Li, Yuchen Yang, Eric Van Buren, Yun Li
• Format: Article
• Language: English
• Published: Wolters Kluwer Health 2019-12-01
• Series: Journal of Bio-X Research
• Online Access: http://journals.lww.com/10.1097/JBR.0000000000000053

Abstract. 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.