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

George C. Linderman, Jun Zhao, Manolis Roulis, Piotr Bielecki, Richard A. Flavell, Boaz Nadler and Yuval Kluger ()
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George C. Linderman: Yale University
Jun Zhao: Yale University
Manolis Roulis: Yale University
Piotr Bielecki: Yale University
Richard A. Flavell: Yale University
Boaz Nadler: Weizmann Institute of Science
Yuval Kluger: Yale University

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: Abstract 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.

Date: 2022
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DOI: 10.1038/s41467-021-27729-z

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