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

Abu-Jamous B, Fa R, Roberts DJ, Nandi AK - PLoS ONE (2013)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering and Electronics, The University of Liverpool, Brownlow Hill, Liverpool, United Kingdom.

ABSTRACT
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.

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False-positives index (FPI).False-positives index (FPI) is plotted in log scale versus a subset of binarization configurations for three representative synthetic datasets out of 60. It should be noted that the binarization configurations are not entirely ordered according to their tightness.
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pone-0056432-g003: False-positives index (FPI).False-positives index (FPI) is plotted in log scale versus a subset of binarization configurations for three representative synthetic datasets out of 60. It should be noted that the binarization configurations are not entirely ordered according to their tightness.

Mentions: Figure 3 plots the FPI for three representative datasets, same as in Figure 2, at different binarization configurations with ε = 0.1 and A = 100. Configurations which result in many multi-assigned genes show large numbers of false-positives, thus they are not included in this plot. Note that the very strict IB can result in totally empty clusters with 450 unassigned genes, resulting in division by zero in FPI values, and are shown in the Figure as missing values.


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

Abu-Jamous B, Fa R, Roberts DJ, Nandi AK - PLoS ONE (2013)

False-positives index (FPI).False-positives index (FPI) is plotted in log scale versus a subset of binarization configurations for three representative synthetic datasets out of 60. It should be noted that the binarization configurations are not entirely ordered according to their tightness.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3569426&req=5

pone-0056432-g003: False-positives index (FPI).False-positives index (FPI) is plotted in log scale versus a subset of binarization configurations for three representative synthetic datasets out of 60. It should be noted that the binarization configurations are not entirely ordered according to their tightness.
Mentions: Figure 3 plots the FPI for three representative datasets, same as in Figure 2, at different binarization configurations with ε = 0.1 and A = 100. Configurations which result in many multi-assigned genes show large numbers of false-positives, thus they are not included in this plot. Note that the very strict IB can result in totally empty clusters with 450 unassigned genes, resulting in division by zero in FPI values, and are shown in the Figure as missing values.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering and Electronics, The University of Liverpool, Brownlow Hill, Liverpool, United Kingdom.

ABSTRACT
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.

Show MeSH