Cargando…
Fast tree aggregation for consensus hierarchical clustering
BACKGROUND: In unsupervised learning and clustering, data integration from different sources and types is a difficult question discussed in several research areas. For instance in omics analysis, dozen of clustering methods have been developed in the past decade. When a single source of data is at p...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085155/ https://www.ncbi.nlm.nih.gov/pubmed/32197576 http://dx.doi.org/10.1186/s12859-020-3453-6 |
_version_ | 1783508887045931008 |
---|---|
author | Hulot, Audrey Chiquet, Julien Jaffrézic, Florence Rigaill, Guillem |
author_facet | Hulot, Audrey Chiquet, Julien Jaffrézic, Florence Rigaill, Guillem |
author_sort | Hulot, Audrey |
collection | PubMed |
description | BACKGROUND: In unsupervised learning and clustering, data integration from different sources and types is a difficult question discussed in several research areas. For instance in omics analysis, dozen of clustering methods have been developed in the past decade. When a single source of data is at play, hierarchical clustering (HC) is extremely popular, as a tree structure is highly interpretable and arguably more informative than just a partition of the data. However, applying blindly HC to multiple sources of data raises computational and interpretation issues. RESULTS: We propose mergeTrees, a method that aggregates a set of trees with the same leaves to create a consensus tree. In our consensus tree, a cluster at height h contains the individuals that are in the same cluster for all the trees at height h. The method is exact and proven to be [Formula: see text] , n being the individuals and q being the number of trees to aggregate. Our implementation is extremely effective on simulations, allowing us to process many large trees at a time. We also rely on mergeTrees to perform the cluster analysis of two real -omics data sets, introducing a spectral variant as an efficient and robust by-product. CONCLUSIONS: Our tree aggregation method can be used in conjunction with hierarchical clustering to perform efficient cluster analysis. This approach was found to be robust to the absence of clustering information in some of the data sets as well as an increased variability within true clusters. The method is implemented in R/C++ and available as an R package named mergeTrees, which makes it easy to integrate in existing or new pipelines in several research areas. |
format | Online Article Text |
id | pubmed-7085155 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-70851552020-03-23 Fast tree aggregation for consensus hierarchical clustering Hulot, Audrey Chiquet, Julien Jaffrézic, Florence Rigaill, Guillem BMC Bioinformatics Methodology Article BACKGROUND: In unsupervised learning and clustering, data integration from different sources and types is a difficult question discussed in several research areas. For instance in omics analysis, dozen of clustering methods have been developed in the past decade. When a single source of data is at play, hierarchical clustering (HC) is extremely popular, as a tree structure is highly interpretable and arguably more informative than just a partition of the data. However, applying blindly HC to multiple sources of data raises computational and interpretation issues. RESULTS: We propose mergeTrees, a method that aggregates a set of trees with the same leaves to create a consensus tree. In our consensus tree, a cluster at height h contains the individuals that are in the same cluster for all the trees at height h. The method is exact and proven to be [Formula: see text] , n being the individuals and q being the number of trees to aggregate. Our implementation is extremely effective on simulations, allowing us to process many large trees at a time. We also rely on mergeTrees to perform the cluster analysis of two real -omics data sets, introducing a spectral variant as an efficient and robust by-product. CONCLUSIONS: Our tree aggregation method can be used in conjunction with hierarchical clustering to perform efficient cluster analysis. This approach was found to be robust to the absence of clustering information in some of the data sets as well as an increased variability within true clusters. The method is implemented in R/C++ and available as an R package named mergeTrees, which makes it easy to integrate in existing or new pipelines in several research areas. BioMed Central 2020-03-20 /pmc/articles/PMC7085155/ /pubmed/32197576 http://dx.doi.org/10.1186/s12859-020-3453-6 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Methodology Article Hulot, Audrey Chiquet, Julien Jaffrézic, Florence Rigaill, Guillem Fast tree aggregation for consensus hierarchical clustering |
title | Fast tree aggregation for consensus hierarchical clustering |
title_full | Fast tree aggregation for consensus hierarchical clustering |
title_fullStr | Fast tree aggregation for consensus hierarchical clustering |
title_full_unstemmed | Fast tree aggregation for consensus hierarchical clustering |
title_short | Fast tree aggregation for consensus hierarchical clustering |
title_sort | fast tree aggregation for consensus hierarchical clustering |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085155/ https://www.ncbi.nlm.nih.gov/pubmed/32197576 http://dx.doi.org/10.1186/s12859-020-3453-6 |
work_keys_str_mv | AT hulotaudrey fasttreeaggregationforconsensushierarchicalclustering AT chiquetjulien fasttreeaggregationforconsensushierarchicalclustering AT jaffrezicflorence fasttreeaggregationforconsensushierarchicalclustering AT rigaillguillem fasttreeaggregationforconsensushierarchicalclustering |