SCnorm: robust normalization of single-cell RNA-seq data

• Published in: Nature methods Vol. 14; no. 6; pp. 584 - 586
• Main Authors: Bacher, Rhonda, Chu, Li-Fang, Leng, Ning, Gasch, Audrey P, Thomson, James A, Stewart, Ron M, Newton, Michael, Kendziorski, Christina
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.06.2017; Nature Publishing Group
• Subjects: 13/100; 38/91; 631/114/2415; 631/114/794; 631/208/199; 631/208/514/1949; 96/35; Algorithms; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical Engineering/Biotechnology; brief-communication; Cells; Data Interpretation, Statistical; Genetic research; High-Throughput Nucleotide Sequencing - standards; Life Sciences; Methods; Proteomics; Reference Values; Ribonucleic acid; RNA; RNA - genetics; RNA sequencing; Robust control; Sequence Analysis, RNA - standards; Single-Cell Analysis - standards; Software; Transcriptome - genetics
• ISSN: 1548-7091; 1548-7105
• DOI: 10.1038/nmeth.4263

SCnorm normalizes single-cell RNA-seq data for improved downstream analyses such as differential expression and cell-state discrimination. The normalization of RNA-seq data is essential for accurate downstream inference, but the assumptions upon which most normalization methods are based are not applicable in the single-cell setting. Consequently, applying existing normalization methods to single-cell RNA-seq data introduces artifacts that bias downstream analyses. To address this, we introduce SCnorm for accurate and efficient normalization of single-cell RNA-seq data.