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M3C: Monte Carlo reference-based consensus clustering
Genome-wide data is used to stratify patients into classes for precision medicine using clustering algorithms. A common problem in this area is selection of the number of clusters (K). The Monti consensus clustering algorithm is a widely used method which uses stability selection to estimate K. Howe...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7000518/ https://www.ncbi.nlm.nih.gov/pubmed/32020004 http://dx.doi.org/10.1038/s41598-020-58766-1 |
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author | John, Christopher R. Watson, David Russ, Dominic Goldmann, Katriona Ehrenstein, Michael Pitzalis, Costantino Lewis, Myles Barnes, Michael |
author_facet | John, Christopher R. Watson, David Russ, Dominic Goldmann, Katriona Ehrenstein, Michael Pitzalis, Costantino Lewis, Myles Barnes, Michael |
author_sort | John, Christopher R. |
collection | PubMed |
description | Genome-wide data is used to stratify patients into classes for precision medicine using clustering algorithms. A common problem in this area is selection of the number of clusters (K). The Monti consensus clustering algorithm is a widely used method which uses stability selection to estimate K. However, the method has bias towards higher values of K and yields high numbers of false positives. As a solution, we developed Monte Carlo reference-based consensus clustering (M3C), which is based on this algorithm. M3C simulates null distributions of stability scores for a range of K values thus enabling a comparison with real data to remove bias and statistically test for the presence of structure. M3C corrects the inherent bias of consensus clustering as demonstrated on simulated and real expression data from The Cancer Genome Atlas (TCGA). For testing M3C, we developed clusterlab, a new method for simulating multivariate Gaussian clusters. |
format | Online Article Text |
id | pubmed-7000518 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-70005182020-02-11 M3C: Monte Carlo reference-based consensus clustering John, Christopher R. Watson, David Russ, Dominic Goldmann, Katriona Ehrenstein, Michael Pitzalis, Costantino Lewis, Myles Barnes, Michael Sci Rep Article Genome-wide data is used to stratify patients into classes for precision medicine using clustering algorithms. A common problem in this area is selection of the number of clusters (K). The Monti consensus clustering algorithm is a widely used method which uses stability selection to estimate K. However, the method has bias towards higher values of K and yields high numbers of false positives. As a solution, we developed Monte Carlo reference-based consensus clustering (M3C), which is based on this algorithm. M3C simulates null distributions of stability scores for a range of K values thus enabling a comparison with real data to remove bias and statistically test for the presence of structure. M3C corrects the inherent bias of consensus clustering as demonstrated on simulated and real expression data from The Cancer Genome Atlas (TCGA). For testing M3C, we developed clusterlab, a new method for simulating multivariate Gaussian clusters. Nature Publishing Group UK 2020-02-04 /pmc/articles/PMC7000518/ /pubmed/32020004 http://dx.doi.org/10.1038/s41598-020-58766-1 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article John, Christopher R. Watson, David Russ, Dominic Goldmann, Katriona Ehrenstein, Michael Pitzalis, Costantino Lewis, Myles Barnes, Michael M3C: Monte Carlo reference-based consensus clustering |
title | M3C: Monte Carlo reference-based consensus clustering |
title_full | M3C: Monte Carlo reference-based consensus clustering |
title_fullStr | M3C: Monte Carlo reference-based consensus clustering |
title_full_unstemmed | M3C: Monte Carlo reference-based consensus clustering |
title_short | M3C: Monte Carlo reference-based consensus clustering |
title_sort | m3c: monte carlo reference-based consensus clustering |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7000518/ https://www.ncbi.nlm.nih.gov/pubmed/32020004 http://dx.doi.org/10.1038/s41598-020-58766-1 |
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