SMNN: batch effect correction for single-cell RNA-seq data via supervised mutual nearest neighbor detection. Issue 3 (26th June 2020)

• Record Type: Journal Article
• Authors: Yang, Yuchen; Li, Gang; Qian, Huijun; Wilhelmsen, Kirk C; Shen, Yin; Li, Yun
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 3(2021)
• Publication Date: 2020-06-26
• Subjects: single-cell RNA sequencing -- batch effect -- supervised mutual nearest neighbor; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbaa097
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Batch effect correction has been recognized to be indispensable when integrating single-cell RNA sequencing (scRNA-seq) data from multiple batches. State-of-the-art methods ignore single-cell cluster label information, but such information can improve the effectiveness of batch effect correction, particularly under realistic scenarios where biological differences are not orthogonal to batch effects. To address this issue, we propose SMNN for batch effect correction of scRNA-seq data via supervised mutual nearest neighbor detection. Our extensive evaluations in simulated and real datasets show that SMNN provides improved merging within the corresponding cell types across batches, leading to reduced differentiation across batches over MNN, Seurat v3 and LIGER. Furthermore, SMNN retains more cell-type-specific features, partially manifested by differentially expressed genes identified between cell types after SMNN correction being biologically more relevant, with precision improving by up to 841.0%.