Paradigm of tunable clustering using Binarization of Consensus Partition Matrices (Bi-CoPaM) for gene discovery.

Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several...

Full description

Bibliographic Details
Main Authors: Basel Abu-Jamous, Rui Fa, David J Roberts, Asoke K Nandi
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2013-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC3569426?pdf=render
id doaj-53646716474f4941b588cf16091ad15e
record_format Article
spelling doaj-53646716474f4941b588cf16091ad15e2020-11-25T02:25:27ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0182e5643210.1371/journal.pone.0056432Paradigm of tunable clustering using Binarization of Consensus Partition Matrices (Bi-CoPaM) for gene discovery.Basel Abu-JamousRui FaDavid J RobertsAsoke K NandiClustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight clusters that focus on their cores or wide clusters that overlap and contain all possibly relevant genes. In this paper, a new clustering paradigm is proposed. In this paradigm, all three eventualities of a gene being exclusively assigned to a single cluster, being assigned to multiple clusters, and being not assigned to any cluster are possible. These possibilities are realised through the primary novelty of the introduction of tunable binarization techniques. Results from multiple clustering experiments are aggregated to generate one fuzzy consensus partition matrix (CoPaM), which is then binarized to obtain the final binary partitions. This is referred to as Binarization of Consensus Partition Matrices (Bi-CoPaM). The method has been tested with a set of synthetic datasets and a set of five real yeast cell-cycle datasets. The results demonstrate its validity in generating relevant tight, wide, and complementary clusters that can meet requirements of different gene discovery studies.http://europepmc.org/articles/PMC3569426?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Basel Abu-Jamous
Rui Fa
David J Roberts
Asoke K Nandi
spellingShingle Basel Abu-Jamous
Rui Fa
David J Roberts
Asoke K Nandi
Paradigm of tunable clustering using Binarization of Consensus Partition Matrices (Bi-CoPaM) for gene discovery.
PLoS ONE
author_facet Basel Abu-Jamous
Rui Fa
David J Roberts
Asoke K Nandi
author_sort Basel Abu-Jamous
title Paradigm of tunable clustering using Binarization of Consensus Partition Matrices (Bi-CoPaM) for gene discovery.
title_short Paradigm of tunable clustering using Binarization of Consensus Partition Matrices (Bi-CoPaM) for gene discovery.
title_full Paradigm of tunable clustering using Binarization of Consensus Partition Matrices (Bi-CoPaM) for gene discovery.
title_fullStr Paradigm of tunable clustering using Binarization of Consensus Partition Matrices (Bi-CoPaM) for gene discovery.
title_full_unstemmed Paradigm of tunable clustering using Binarization of Consensus Partition Matrices (Bi-CoPaM) for gene discovery.
title_sort paradigm of tunable clustering using binarization of consensus partition matrices (bi-copam) for gene discovery.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2013-01-01
description Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight clusters that focus on their cores or wide clusters that overlap and contain all possibly relevant genes. In this paper, a new clustering paradigm is proposed. In this paradigm, all three eventualities of a gene being exclusively assigned to a single cluster, being assigned to multiple clusters, and being not assigned to any cluster are possible. These possibilities are realised through the primary novelty of the introduction of tunable binarization techniques. Results from multiple clustering experiments are aggregated to generate one fuzzy consensus partition matrix (CoPaM), which is then binarized to obtain the final binary partitions. This is referred to as Binarization of Consensus Partition Matrices (Bi-CoPaM). The method has been tested with a set of synthetic datasets and a set of five real yeast cell-cycle datasets. The results demonstrate its validity in generating relevant tight, wide, and complementary clusters that can meet requirements of different gene discovery studies.
url http://europepmc.org/articles/PMC3569426?pdf=render
work_keys_str_mv AT baselabujamous paradigmoftunableclusteringusingbinarizationofconsensuspartitionmatricesbicopamforgenediscovery
AT ruifa paradigmoftunableclusteringusingbinarizationofconsensuspartitionmatricesbicopamforgenediscovery
AT davidjroberts paradigmoftunableclusteringusingbinarizationofconsensuspartitionmatricesbicopamforgenediscovery
AT asokeknandi paradigmoftunableclusteringusingbinarizationofconsensuspartitionmatricesbicopamforgenediscovery
_version_ 1724851195030274048