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netNMF-sc: leveraging gene–gene interactions for imputation and dimensionality reduction in single-cell expression analysis
Single-cell RNA-sequencing (scRNA-seq) enables high-throughput measurement of RNA expression in single cells. However, because of technical limitations, scRNA-seq data often contain zero counts for many transcripts in individual cells. These zero counts, or dropout events, complicate the analysis of...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7050525/ https://www.ncbi.nlm.nih.gov/pubmed/31992614 http://dx.doi.org/10.1101/gr.251603.119 |
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author | Elyanow, Rebecca Dumitrascu, Bianca Engelhardt, Barbara E. Raphael, Benjamin J. |
author_facet | Elyanow, Rebecca Dumitrascu, Bianca Engelhardt, Barbara E. Raphael, Benjamin J. |
author_sort | Elyanow, Rebecca |
collection | PubMed |
description | Single-cell RNA-sequencing (scRNA-seq) enables high-throughput measurement of RNA expression in single cells. However, because of technical limitations, scRNA-seq data often contain zero counts for many transcripts in individual cells. These zero counts, or dropout events, complicate the analysis of scRNA-seq data using standard methods developed for bulk RNA-seq data. Current scRNA-seq analysis methods typically overcome dropout by combining information across cells in a lower-dimensional space, leveraging the observation that cells generally occupy a small number of RNA expression states. We introduce netNMF-sc, an algorithm for scRNA-seq analysis that leverages information across both cells and genes. netNMF-sc learns a low-dimensional representation of scRNA-seq transcript counts using network-regularized non-negative matrix factorization. The network regularization takes advantage of prior knowledge of gene–gene interactions, encouraging pairs of genes with known interactions to be nearby each other in the low-dimensional representation. The resulting matrix factorization imputes gene abundance for both zero and nonzero counts and can be used to cluster cells into meaningful subpopulations. We show that netNMF-sc outperforms existing methods at clustering cells and estimating gene–gene covariance using both simulated and real scRNA-seq data, with increasing advantages at higher dropout rates (e.g., >60%). We also show that the results from netNMF-sc are robust to variation in the input network, with more representative networks leading to greater performance gains. |
format | Online Article Text |
id | pubmed-7050525 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Cold Spring Harbor Laboratory Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-70505252020-08-01 netNMF-sc: leveraging gene–gene interactions for imputation and dimensionality reduction in single-cell expression analysis Elyanow, Rebecca Dumitrascu, Bianca Engelhardt, Barbara E. Raphael, Benjamin J. Genome Res Method Single-cell RNA-sequencing (scRNA-seq) enables high-throughput measurement of RNA expression in single cells. However, because of technical limitations, scRNA-seq data often contain zero counts for many transcripts in individual cells. These zero counts, or dropout events, complicate the analysis of scRNA-seq data using standard methods developed for bulk RNA-seq data. Current scRNA-seq analysis methods typically overcome dropout by combining information across cells in a lower-dimensional space, leveraging the observation that cells generally occupy a small number of RNA expression states. We introduce netNMF-sc, an algorithm for scRNA-seq analysis that leverages information across both cells and genes. netNMF-sc learns a low-dimensional representation of scRNA-seq transcript counts using network-regularized non-negative matrix factorization. The network regularization takes advantage of prior knowledge of gene–gene interactions, encouraging pairs of genes with known interactions to be nearby each other in the low-dimensional representation. The resulting matrix factorization imputes gene abundance for both zero and nonzero counts and can be used to cluster cells into meaningful subpopulations. We show that netNMF-sc outperforms existing methods at clustering cells and estimating gene–gene covariance using both simulated and real scRNA-seq data, with increasing advantages at higher dropout rates (e.g., >60%). We also show that the results from netNMF-sc are robust to variation in the input network, with more representative networks leading to greater performance gains. Cold Spring Harbor Laboratory Press 2020-02 /pmc/articles/PMC7050525/ /pubmed/31992614 http://dx.doi.org/10.1101/gr.251603.119 Text en © 2020 Elyanow et al.; Published by Cold Spring Harbor Laboratory Press http://creativecommons.org/licenses/by-nc/4.0/ This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Method Elyanow, Rebecca Dumitrascu, Bianca Engelhardt, Barbara E. Raphael, Benjamin J. netNMF-sc: leveraging gene–gene interactions for imputation and dimensionality reduction in single-cell expression analysis |
title | netNMF-sc: leveraging gene–gene interactions for imputation and dimensionality reduction in single-cell expression analysis |
title_full | netNMF-sc: leveraging gene–gene interactions for imputation and dimensionality reduction in single-cell expression analysis |
title_fullStr | netNMF-sc: leveraging gene–gene interactions for imputation and dimensionality reduction in single-cell expression analysis |
title_full_unstemmed | netNMF-sc: leveraging gene–gene interactions for imputation and dimensionality reduction in single-cell expression analysis |
title_short | netNMF-sc: leveraging gene–gene interactions for imputation and dimensionality reduction in single-cell expression analysis |
title_sort | netnmf-sc: leveraging gene–gene interactions for imputation and dimensionality reduction in single-cell expression analysis |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7050525/ https://www.ncbi.nlm.nih.gov/pubmed/31992614 http://dx.doi.org/10.1101/gr.251603.119 |
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