Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization

Single-cell RNA-Sequencing (scRNA-Seq) is a fast-evolving technology that enables the understanding of biological processes at an unprecedentedly high resolution. However, well-suited bioinformatics tools to analyze the data generated from this new technology are still lacking. Here we investigate t...
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Gespeichert in:

Autor*in:	Xun Zhu [verfasserIn] Travers Ching [verfasserIn] Xinghua Pan [verfasserIn] Sherman M. Weissman [verfasserIn] Lana Garmire [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Single-cell RNA-Seq Heterogeneity Non-negative matrix factorization Modularity Clustering

Übergeordnetes Werk:	In: PeerJ - PeerJ Inc., 2013, 5, p e2888(2017)
Übergeordnetes Werk:	volume:5, p e2888 ; year:2017

Links:	Link aufrufen Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.7717/peerj.2888

Katalog-ID:	DOAJ035442077

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allfieldsSound	10.7717/peerj.2888 doi (DE-627)DOAJ035442077 (DE-599)DOAJ3d667e316ee549fa8c518c270d742270 DE-627 ger DE-627 rakwb eng QH301-705.5 Xun Zhu verfasserin aut Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Single-cell RNA-Sequencing (scRNA-Seq) is a fast-evolving technology that enables the understanding of biological processes at an unprecedentedly high resolution. However, well-suited bioinformatics tools to analyze the data generated from this new technology are still lacking. Here we investigate the performance of non-negative matrix factorization (NMF) method to analyze a wide variety of scRNA-Seq datasets, ranging from mouse hematopoietic stem cells to human glioblastoma data. In comparison to other unsupervised clustering methods including K-means and hierarchical clustering, NMF has higher accuracy in separating similar groups in various datasets. We ranked genes by their importance scores (D-scores) in separating these groups, and discovered that NMF uniquely identifies genes expressed at intermediate levels as top-ranked genes. Finally, we show that in conjugation with the modularity detection method FEM, NMF reveals meaningful protein-protein interaction modules. In summary, we propose that NMF is a desirable method to analyze heterogeneous single-cell RNA-Seq data. The NMF based subpopulation detection package is available at: https://github.com/lanagarmire/NMFEM. Single-cell RNA-Seq Heterogeneity Non-negative matrix factorization Modularity Clustering Medicine R Biology (General) Travers Ching verfasserin aut Xinghua Pan verfasserin aut Sherman M. Weissman verfasserin aut Lana Garmire verfasserin aut In PeerJ PeerJ Inc., 2013 5, p e2888(2017) (DE-627)736558624 (DE-600)2703241-3 21678359 nnns volume:5, p e2888 year:2017 https://doi.org/10.7717/peerj.2888 kostenfrei https://doaj.org/article/3d667e316ee549fa8c518c270d742270 kostenfrei https://peerj.com/articles/2888.pdf kostenfrei https://peerj.com/articles/2888/ kostenfrei https://doaj.org/toc/2167-8359 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5, p e2888 2017
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callnumber-first	Q - Science
author	Xun Zhu
spellingShingle	Xun Zhu misc QH301-705.5 misc Single-cell misc RNA-Seq misc Heterogeneity misc Non-negative matrix factorization misc Modularity misc Clustering misc Medicine misc R misc Biology (General) Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization
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topic_title	QH301-705.5 Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization Single-cell RNA-Seq Heterogeneity Non-negative matrix factorization Modularity Clustering
topic	misc QH301-705.5 misc Single-cell misc RNA-Seq misc Heterogeneity misc Non-negative matrix factorization misc Modularity misc Clustering misc Medicine misc R misc Biology (General)
topic_unstemmed	misc QH301-705.5 misc Single-cell misc RNA-Seq misc Heterogeneity misc Non-negative matrix factorization misc Modularity misc Clustering misc Medicine misc R misc Biology (General)
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title	Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization
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title_full	Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization
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title_sort	detecting heterogeneity in single-cell rna-seq data by non-negative matrix factorization
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title_auth	Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization
abstract	Single-cell RNA-Sequencing (scRNA-Seq) is a fast-evolving technology that enables the understanding of biological processes at an unprecedentedly high resolution. However, well-suited bioinformatics tools to analyze the data generated from this new technology are still lacking. Here we investigate the performance of non-negative matrix factorization (NMF) method to analyze a wide variety of scRNA-Seq datasets, ranging from mouse hematopoietic stem cells to human glioblastoma data. In comparison to other unsupervised clustering methods including K-means and hierarchical clustering, NMF has higher accuracy in separating similar groups in various datasets. We ranked genes by their importance scores (D-scores) in separating these groups, and discovered that NMF uniquely identifies genes expressed at intermediate levels as top-ranked genes. Finally, we show that in conjugation with the modularity detection method FEM, NMF reveals meaningful protein-protein interaction modules. In summary, we propose that NMF is a desirable method to analyze heterogeneous single-cell RNA-Seq data. The NMF based subpopulation detection package is available at: https://github.com/lanagarmire/NMFEM.
abstractGer	Single-cell RNA-Sequencing (scRNA-Seq) is a fast-evolving technology that enables the understanding of biological processes at an unprecedentedly high resolution. However, well-suited bioinformatics tools to analyze the data generated from this new technology are still lacking. Here we investigate the performance of non-negative matrix factorization (NMF) method to analyze a wide variety of scRNA-Seq datasets, ranging from mouse hematopoietic stem cells to human glioblastoma data. In comparison to other unsupervised clustering methods including K-means and hierarchical clustering, NMF has higher accuracy in separating similar groups in various datasets. We ranked genes by their importance scores (D-scores) in separating these groups, and discovered that NMF uniquely identifies genes expressed at intermediate levels as top-ranked genes. Finally, we show that in conjugation with the modularity detection method FEM, NMF reveals meaningful protein-protein interaction modules. In summary, we propose that NMF is a desirable method to analyze heterogeneous single-cell RNA-Seq data. The NMF based subpopulation detection package is available at: https://github.com/lanagarmire/NMFEM.
abstract_unstemmed	Single-cell RNA-Sequencing (scRNA-Seq) is a fast-evolving technology that enables the understanding of biological processes at an unprecedentedly high resolution. However, well-suited bioinformatics tools to analyze the data generated from this new technology are still lacking. Here we investigate the performance of non-negative matrix factorization (NMF) method to analyze a wide variety of scRNA-Seq datasets, ranging from mouse hematopoietic stem cells to human glioblastoma data. In comparison to other unsupervised clustering methods including K-means and hierarchical clustering, NMF has higher accuracy in separating similar groups in various datasets. We ranked genes by their importance scores (D-scores) in separating these groups, and discovered that NMF uniquely identifies genes expressed at intermediate levels as top-ranked genes. Finally, we show that in conjugation with the modularity detection method FEM, NMF reveals meaningful protein-protein interaction modules. In summary, we propose that NMF is a desirable method to analyze heterogeneous single-cell RNA-Seq data. The NMF based subpopulation detection package is available at: https://github.com/lanagarmire/NMFEM.
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title_short	Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization
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Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization

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