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...

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Bibliographic Details
Published in:PloS one 2013-02, Vol.8 (2), p.e56432-e56432
Main Authors: Abu-Jamous, Basel, Fa, Rui, Roberts, David J, Nandi, Asoke K
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Artificial intelligence
Bioinformatics
Biology
Cell cycle
Cell Cycle - genetics
Cluster Analysis
Clustering
Clusters
Computational Biology - methods
Cores
Databases, Genetic
Datasets
Electrical engineering
Engineering
Experiments
Gene expression
Genes
Genes, Fungal - genetics
Matrix partitioning
Methods
Oligonucleotide Array Sequence Analysis
Partitions
Signal processing
Yeast
Yeasts - cytology
Yeasts - genetics
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0056432