Zainul Abidin, FN and Westhead, DR orcid.org/0000-0002-0519-3820 (2017) Flexible model-based clustering of mixed binary and continuous data: application to genetic regulation and cancer. Nucleic Acids Research, 45 (7). ISSN 0305-1048
Abstract
Clustering is used widely in ‘omics’ studies and is often tackled with standard methods, e.g. hierarchical clustering. However, the increasing need for integration of multiple data sets leads to a requirement for clustering methods applicable to mixed data types, where the straightforward application of standard methods is not necessarily the best approach. A particularly common problem involves clustering entities characterized by a mixture of binary data (e.g. presence/absence of mutations, binding, motifs and epigenetic marks) and continuous data (e.g. gene expression, protein abundance, metabolite levels). Here we present a generic method based on a probabilistic model for clustering this type of data, and illustrate its application to genetic regulation and the clustering of cancer samples. We show that the resulting clusters lead to useful hypotheses: in the case of genetic regulation these concern regulation of groups of genes by specific sets of transcription factors and in the case of cancer samples combinations of gene mutations are related to patterns of gene expression. The clusters have potential mechanistic significance and in the latter case are significantly linked to survival. The method is available as a stand-alone software package (GNU General Public Licence) from https://github.com/BioToolsLeeds/FlexiCoClusteringPackage.git.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | (c) The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research. 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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Molecular and Cellular Biology (Leeds) |
Funding Information: | Funder Grant number Bloodwise 13052 BBSRC BB/I001220/1 Bloodwise 15002 |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Dec 2016 10:17 |
Last Modified: | 23 Jun 2023 22:18 |
Published Version: | https://doi.org/10.1093/nar/gkw1270 |
Status: | Published |
Publisher: | Oxford University Press |
Identification Number: | 10.1093/nar/gkw1270 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:108953 |