Zero-preserving imputation of single-cell RNA-seq data

• Published in: Nature communications Vol. 13; no. 1; p. 192
• Main Authors: Linderman, George C, Zhao, Jun, Roulis, Manolis, Bielecki, Piotr, Flavell, Richard A, Nadler, Boaz, Kluger, Yuval
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 11.01.2022; Nature Publishing Group UK; Nature Portfolio
• Subjects: Algorithms; Animals; Approximation; B-Lymphocytes - cytology; B-Lymphocytes - metabolism; Bronchi - cytology; Bronchi - metabolism; Datasets; Datasets as Topic; Epithelial Cells - cytology; Epithelial Cells - metabolism; Gene sequencing; Genes; Humans; Killer Cells, Natural - cytology; Killer Cells, Natural - metabolism; Mathematical analysis; Mice; Monocytes - cytology; Monocytes - metabolism; Primary Cell Culture; Ribonucleic acid; RNA; RNA - genetics; RNA - metabolism; RNA-Seq; Sequence Analysis, RNA - statistics & numerical data; Single-Cell Analysis; T-Lymphocytes - cytology; T-Lymphocytes - metabolism
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-27729-z

A key challenge in analyzing single cell RNA-sequencing data is the large number of false zeros, where genes actually expressed in a given cell are incorrectly measured as unexpressed. We present a method based on low-rank matrix approximation which imputes these values while preserving biologically non-expressed genes (true biological zeros) at zero expression levels. We provide theoretical justification for this denoising approach and demonstrate its advantages relative to other methods on simulated and biological datasets.