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

• Published in: Nature communications Vol. 13; no. 1; pp. 192 - 11
• Main Authors: Linderman, George C., Zhao, Jun, Roulis, Manolis, Bielecki, Piotr, Flavell, Richard A., Nadler, Boaz, Kluger, Yuval
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 11.01.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2415; 631/208/514/1949; 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; Humanities and Social Sciences; Humans; Killer Cells, Natural - cytology; Killer Cells, Natural - metabolism; Mathematical analysis; Mice; Monocytes - cytology; Monocytes - metabolism; multidisciplinary; Primary Cell Culture; Ribonucleic acid; RNA; RNA - genetics; RNA - metabolism; RNA-Seq; Science; Science (multidisciplinary); Sequence Analysis, RNA - statistics & numerical data; Single-Cell Analysis; T-Lymphocytes - cytology; T-Lymphocytes - metabolism

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...