Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations

When different types of functional genomics data are generated on single cells from different samples of cells from the same heterogeneous population, the clustering of cells in the different samples should be coupled. We formulate this “coupled clustering” problem as an optimization problem and pro...

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Detalles Bibliográficos
Autores principales: Duren, Zhana, Chen, Xi, Zamanighomi, Mahdi, Zeng, Wanwen, Satpathy, Ansuman T., Chang, Howard Y., Wang, Yong, Wong, Wing Hung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2018
Materias:
Physical Sciences
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6065048/
https://www.ncbi.nlm.nih.gov/pubmed/29987051
http://dx.doi.org/10.1073/pnas.1805681115
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author Duren, Zhana
Chen, Xi
Zamanighomi, Mahdi
Zeng, Wanwen
Satpathy, Ansuman T.
Chang, Howard Y.
Wang, Yong
Wong, Wing Hung
author_facet Duren, Zhana
Chen, Xi
Zamanighomi, Mahdi
Zeng, Wanwen
Satpathy, Ansuman T.
Chang, Howard Y.
Wang, Yong
Wong, Wing Hung
author_sort Duren, Zhana
collection PubMed
description When different types of functional genomics data are generated on single cells from different samples of cells from the same heterogeneous population, the clustering of cells in the different samples should be coupled. We formulate this “coupled clustering” problem as an optimization problem and propose the method of coupled nonnegative matrix factorizations (coupled NMF) for its solution. The method is illustrated by the integrative analysis of single-cell RNA-sequencing (RNA-seq) and single-cell ATAC-sequencing (ATAC-seq) data.
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spelling pubmed-60650482018-07-31 Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations Duren, Zhana Chen, Xi Zamanighomi, Mahdi Zeng, Wanwen Satpathy, Ansuman T. Chang, Howard Y. Wang, Yong Wong, Wing Hung Proc Natl Acad Sci U S A Physical Sciences When different types of functional genomics data are generated on single cells from different samples of cells from the same heterogeneous population, the clustering of cells in the different samples should be coupled. We formulate this “coupled clustering” problem as an optimization problem and propose the method of coupled nonnegative matrix factorizations (coupled NMF) for its solution. The method is illustrated by the integrative analysis of single-cell RNA-sequencing (RNA-seq) and single-cell ATAC-sequencing (ATAC-seq) data. National Academy of Sciences 2018-07-24 2018-07-09 /pmc/articles/PMC6065048/ /pubmed/29987051 http://dx.doi.org/10.1073/pnas.1805681115 Text en Copyright © 2018 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/ This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Physical Sciences
Duren, Zhana
Chen, Xi
Zamanighomi, Mahdi
Zeng, Wanwen
Satpathy, Ansuman T.
Chang, Howard Y.
Wang, Yong
Wong, Wing Hung
Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations
title Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations
title_full Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations
title_fullStr Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations
title_full_unstemmed Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations
title_short Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations
title_sort integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations
topic Physical Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6065048/
https://www.ncbi.nlm.nih.gov/pubmed/29987051
http://dx.doi.org/10.1073/pnas.1805681115
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