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Clusternomics: Integrative context-dependent clustering for heterogeneous datasets
Integrative clustering is used to identify groups of samples by jointly analysing multiple datasets describing the same set of biological samples, such as gene expression, copy number, methylation etc. Most existing algorithms for integrative clustering assume that there is a shared consistent set o...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5658176/ https://www.ncbi.nlm.nih.gov/pubmed/29036190 http://dx.doi.org/10.1371/journal.pcbi.1005781 |
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author | Gabasova, Evelina Reid, John Wernisch, Lorenz |
author_facet | Gabasova, Evelina Reid, John Wernisch, Lorenz |
author_sort | Gabasova, Evelina |
collection | PubMed |
description | Integrative clustering is used to identify groups of samples by jointly analysing multiple datasets describing the same set of biological samples, such as gene expression, copy number, methylation etc. Most existing algorithms for integrative clustering assume that there is a shared consistent set of clusters across all datasets, and most of the data samples follow this structure. However in practice, the structure across heterogeneous datasets can be more varied, with clusters being joined in some datasets and separated in others. In this paper, we present a probabilistic clustering method to identify groups across datasets that do not share the same cluster structure. The proposed algorithm, Clusternomics, identifies groups of samples that share their global behaviour across heterogeneous datasets. The algorithm models clusters on the level of individual datasets, while also extracting global structure that arises from the local cluster assignments. Clusters on both the local and the global level are modelled using a hierarchical Dirichlet mixture model to identify structure on both levels. We evaluated the model both on simulated and on real-world datasets. The simulated data exemplifies datasets with varying degrees of common structure. In such a setting Clusternomics outperforms existing algorithms for integrative and consensus clustering. In a real-world application, we used the algorithm for cancer subtyping, identifying subtypes of cancer from heterogeneous datasets. We applied the algorithm to TCGA breast cancer dataset, integrating gene expression, miRNA expression, DNA methylation and proteomics. The algorithm extracted clinically meaningful clusters with significantly different survival probabilities. We also evaluated the algorithm on lung and kidney cancer TCGA datasets with high dimensionality, again showing clinically significant results and scalability of the algorithm. |
format | Online Article Text |
id | pubmed-5658176 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-56581762017-11-09 Clusternomics: Integrative context-dependent clustering for heterogeneous datasets Gabasova, Evelina Reid, John Wernisch, Lorenz PLoS Comput Biol Research Article Integrative clustering is used to identify groups of samples by jointly analysing multiple datasets describing the same set of biological samples, such as gene expression, copy number, methylation etc. Most existing algorithms for integrative clustering assume that there is a shared consistent set of clusters across all datasets, and most of the data samples follow this structure. However in practice, the structure across heterogeneous datasets can be more varied, with clusters being joined in some datasets and separated in others. In this paper, we present a probabilistic clustering method to identify groups across datasets that do not share the same cluster structure. The proposed algorithm, Clusternomics, identifies groups of samples that share their global behaviour across heterogeneous datasets. The algorithm models clusters on the level of individual datasets, while also extracting global structure that arises from the local cluster assignments. Clusters on both the local and the global level are modelled using a hierarchical Dirichlet mixture model to identify structure on both levels. We evaluated the model both on simulated and on real-world datasets. The simulated data exemplifies datasets with varying degrees of common structure. In such a setting Clusternomics outperforms existing algorithms for integrative and consensus clustering. In a real-world application, we used the algorithm for cancer subtyping, identifying subtypes of cancer from heterogeneous datasets. We applied the algorithm to TCGA breast cancer dataset, integrating gene expression, miRNA expression, DNA methylation and proteomics. The algorithm extracted clinically meaningful clusters with significantly different survival probabilities. We also evaluated the algorithm on lung and kidney cancer TCGA datasets with high dimensionality, again showing clinically significant results and scalability of the algorithm. Public Library of Science 2017-10-16 /pmc/articles/PMC5658176/ /pubmed/29036190 http://dx.doi.org/10.1371/journal.pcbi.1005781 Text en © 2017 Gabasova et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Gabasova, Evelina Reid, John Wernisch, Lorenz Clusternomics: Integrative context-dependent clustering for heterogeneous datasets |
title | Clusternomics: Integrative context-dependent clustering for heterogeneous datasets |
title_full | Clusternomics: Integrative context-dependent clustering for heterogeneous datasets |
title_fullStr | Clusternomics: Integrative context-dependent clustering for heterogeneous datasets |
title_full_unstemmed | Clusternomics: Integrative context-dependent clustering for heterogeneous datasets |
title_short | Clusternomics: Integrative context-dependent clustering for heterogeneous datasets |
title_sort | clusternomics: integrative context-dependent clustering for heterogeneous datasets |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5658176/ https://www.ncbi.nlm.nih.gov/pubmed/29036190 http://dx.doi.org/10.1371/journal.pcbi.1005781 |
work_keys_str_mv | AT gabasovaevelina clusternomicsintegrativecontextdependentclusteringforheterogeneousdatasets AT reidjohn clusternomicsintegrativecontextdependentclusteringforheterogeneousdatasets AT wernischlorenz clusternomicsintegrativecontextdependentclusteringforheterogeneousdatasets |