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

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...

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Detalles Bibliográficos
Autores principales: Linderman, George C., Zhao, Jun, Roulis, Manolis, Bielecki, Piotr, Flavell, Richard A., Nadler, Boaz, Kluger, Yuval
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8752663/
https://www.ncbi.nlm.nih.gov/pubmed/35017482
http://dx.doi.org/10.1038/s41467-021-27729-z
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author Linderman, George C.
Zhao, Jun
Roulis, Manolis
Bielecki, Piotr
Flavell, Richard A.
Nadler, Boaz
Kluger, Yuval
author_facet Linderman, George C.
Zhao, Jun
Roulis, Manolis
Bielecki, Piotr
Flavell, Richard A.
Nadler, Boaz
Kluger, Yuval
author_sort Linderman, George C.
collection PubMed
description 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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spelling pubmed-87526632022-01-20 Zero-preserving imputation of single-cell RNA-seq data Linderman, George C. Zhao, Jun Roulis, Manolis Bielecki, Piotr Flavell, Richard A. Nadler, Boaz Kluger, Yuval Nat Commun Article 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. Nature Publishing Group UK 2022-01-11 /pmc/articles/PMC8752663/ /pubmed/35017482 http://dx.doi.org/10.1038/s41467-021-27729-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Linderman, George C.
Zhao, Jun
Roulis, Manolis
Bielecki, Piotr
Flavell, Richard A.
Nadler, Boaz
Kluger, Yuval
Zero-preserving imputation of single-cell RNA-seq data
title Zero-preserving imputation of single-cell RNA-seq data
title_full Zero-preserving imputation of single-cell RNA-seq data
title_fullStr Zero-preserving imputation of single-cell RNA-seq data
title_full_unstemmed Zero-preserving imputation of single-cell RNA-seq data
title_short Zero-preserving imputation of single-cell RNA-seq data
title_sort zero-preserving imputation of single-cell rna-seq data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8752663/
https://www.ncbi.nlm.nih.gov/pubmed/35017482
http://dx.doi.org/10.1038/s41467-021-27729-z
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