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

George C Linderman; Jun Zhao; Manolis Roulis; Piotr Bielecki; Richard A Flavell; Boaz Nadler; Yuval Kluger

doi:10.1038/s41467-021-27729-z

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

George C Linderman, Jun Zhao, Manolis Roulis, Piotr Bielecki, Richard A Flavell, Boaz Nadler, Yuval Kluger

Department of Computer Science and Applied Mathematics

Weizmann Institute of Science

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	192
Journal	Nature Communications
Volume	13
Issue number	1
DOIs	https://doi.org/10.1038/s41467-021-27729-z
State	Published - Dec 2022

All Science Journal Classification (ASJC) codes

General Chemistry
General Biochemistry,Genetics and Molecular Biology
General Physics and Astronomy

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10.1038/s41467-021-27729-zLicense: CC BY

Cite this

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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.",

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AU - Flavell, Richard A

AU - Nadler, Boaz

AU - Kluger, Yuval

PY - 2022/12

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AB - 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.

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DO - 10.1038/s41467-021-27729-z

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VL - 13

JO - Nature Communications

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IS - 1

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

Abstract

All Science Journal Classification (ASJC) codes

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