Discovering subgroups of patients from DNA copy number data using NMF on compacted matrices.

In the study of complex genetic diseases, the identification of subgroups of patients sharing similar genetic characteristics represents a challenging task, for example, to improve treatment decision. One type of genetic lesion, frequently investigated in such disorders, is the change of the DNA cop...

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Autores principales:	Cassio P de Campos, Paola M V Rancoita, Ivo Kwee, Emanuele Zucca, Marco Zaffalon, Francesco Bertoni
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3b3b6f275b3c4c6bbcf1e0907df737e6
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spelling	oai:doaj.org-article:3b3b6f275b3c4c6bbcf1e0907df737e62021-11-18T08:45:33ZDiscovering subgroups of patients from DNA copy number data using NMF on compacted matrices.1932-620310.1371/journal.pone.0079720https://doaj.org/article/3b3b6f275b3c4c6bbcf1e0907df737e62013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24278162/?tool=EBIhttps://doaj.org/toc/1932-6203In the study of complex genetic diseases, the identification of subgroups of patients sharing similar genetic characteristics represents a challenging task, for example, to improve treatment decision. One type of genetic lesion, frequently investigated in such disorders, is the change of the DNA copy number (CN) at specific genomic traits. Non-negative Matrix Factorization (NMF) is a standard technique to reduce the dimensionality of a data set and to cluster data samples, while keeping its most relevant information in meaningful components. Thus, it can be used to discover subgroups of patients from CN profiles. It is however computationally impractical for very high dimensional data, such as CN microarray data. Deciding the most suitable number of subgroups is also a challenging problem. The aim of this work is to derive a procedure to compact high dimensional data, in order to improve NMF applicability without compromising the quality of the clustering. This is particularly important for analyzing high-resolution microarray data. Many commonly used quality measures, as well as our own measures, are employed to decide the number of subgroups and to assess the quality of the results. Our measures are based on the idea of identifying robust subgroups, inspired by biologically/clinically relevance instead of simply aiming at well-separated clusters. We evaluate our procedure using four real independent data sets. In these data sets, our method was able to find accurate subgroups with individual molecular and clinical features and outperformed the standard NMF in terms of accuracy in the factorization fitness function. Hence, it can be useful for the discovery of subgroups of patients with similar CN profiles in the study of heterogeneous diseases.Cassio P de CamposPaola M V RancoitaIvo KweeEmanuele ZuccaMarco ZaffalonFrancesco BertoniPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 11, p e79720 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Cassio P de Campos Paola M V Rancoita Ivo Kwee Emanuele Zucca Marco Zaffalon Francesco Bertoni Discovering subgroups of patients from DNA copy number data using NMF on compacted matrices.
description	In the study of complex genetic diseases, the identification of subgroups of patients sharing similar genetic characteristics represents a challenging task, for example, to improve treatment decision. One type of genetic lesion, frequently investigated in such disorders, is the change of the DNA copy number (CN) at specific genomic traits. Non-negative Matrix Factorization (NMF) is a standard technique to reduce the dimensionality of a data set and to cluster data samples, while keeping its most relevant information in meaningful components. Thus, it can be used to discover subgroups of patients from CN profiles. It is however computationally impractical for very high dimensional data, such as CN microarray data. Deciding the most suitable number of subgroups is also a challenging problem. The aim of this work is to derive a procedure to compact high dimensional data, in order to improve NMF applicability without compromising the quality of the clustering. This is particularly important for analyzing high-resolution microarray data. Many commonly used quality measures, as well as our own measures, are employed to decide the number of subgroups and to assess the quality of the results. Our measures are based on the idea of identifying robust subgroups, inspired by biologically/clinically relevance instead of simply aiming at well-separated clusters. We evaluate our procedure using four real independent data sets. In these data sets, our method was able to find accurate subgroups with individual molecular and clinical features and outperformed the standard NMF in terms of accuracy in the factorization fitness function. Hence, it can be useful for the discovery of subgroups of patients with similar CN profiles in the study of heterogeneous diseases.
format	article
author	Cassio P de Campos Paola M V Rancoita Ivo Kwee Emanuele Zucca Marco Zaffalon Francesco Bertoni
author_facet	Cassio P de Campos Paola M V Rancoita Ivo Kwee Emanuele Zucca Marco Zaffalon Francesco Bertoni
author_sort	Cassio P de Campos
title	Discovering subgroups of patients from DNA copy number data using NMF on compacted matrices.
title_short	Discovering subgroups of patients from DNA copy number data using NMF on compacted matrices.
title_full	Discovering subgroups of patients from DNA copy number data using NMF on compacted matrices.
title_fullStr	Discovering subgroups of patients from DNA copy number data using NMF on compacted matrices.
title_full_unstemmed	Discovering subgroups of patients from DNA copy number data using NMF on compacted matrices.
title_sort	discovering subgroups of patients from dna copy number data using nmf on compacted matrices.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/3b3b6f275b3c4c6bbcf1e0907df737e6
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_version_	1718421315965157376

Discovering subgroups of patients from DNA copy number data using NMF on compacted matrices.

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