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Identification of coherent patterns in gene expression data using an efficient biclustering algorithm and parallel coordinate visualization.

Cheng KO, Law NF, Siu WC, Liew AW - BMC Bioinformatics (2008)

Bottom Line: Unfortunately, many useful formulations result in NP-complete problems.Biologically significant biclusters have been validated on the yeast cell-cycle expression dataset using Gene Ontology annotations.Comparative study shows that the proposed approach outperforms several existing biclustering algorithms.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Information and Communication Technology, Griffith University, Gold Coast Campus, QLD 4222, Queensland, Australia. k.o.cheng@polyu.edu.hk

ABSTRACT

Background: The DNA microarray technology allows the measurement of expression levels of thousands of genes under tens/hundreds of different conditions. In microarray data, genes with similar functions usually co-express under certain conditions only 1. Thus, biclustering which clusters genes and conditions simultaneously is preferred over the traditional clustering technique in discovering these coherent genes. Various biclustering algorithms have been developed using different bicluster formulations. Unfortunately, many useful formulations result in NP-complete problems. In this article, we investigate an efficient method for identifying a popular type of biclusters called additive model. Furthermore, parallel coordinate (PC) plots are used for bicluster visualization and analysis.

Results: We develop a novel and efficient biclustering algorithm which can be regarded as a greedy version of an existing algorithm known as pCluster algorithm. By relaxing the constraint in homogeneity, the proposed algorithm has polynomial-time complexity in the worst case instead of exponential-time complexity as in the pCluster algorithm. Experiments on artificial datasets verify that our algorithm can identify both additive-related and multiplicative-related biclusters in the presence of overlap and noise. Biologically significant biclusters have been validated on the yeast cell-cycle expression dataset using Gene Ontology annotations. Comparative study shows that the proposed approach outperforms several existing biclustering algorithms. We also provide an interactive exploratory tool based on PC plot visualization for determining the parameters of our biclustering algorithm.

Conclusion: We have proposed a novel biclustering algorithm which works with PC plots for an interactive exploratory analysis of gene expression data. Experiments show that the biclustering algorithm is efficient and is capable of detecting co-regulated genes. The interactive analysis enables an optimum parameter determination in the biclustering algorithm so as to achieve the best result. In future, we will modify the proposed algorithm for other bicluster models such as the coherent evolution model.

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The difference matrix for the dataset shown in Figure 2.
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Figure 3: The difference matrix for the dataset shown in Figure 2.

Mentions: The biclusters given in Figure 1(A)–(D) can be described by an additive model in which each pair of rows has the same difference in all the related columns or each pair of columns has the same difference in all the related rows. Thus, a difference matrix, each column of which represents the column differences between a pair of columns in a data matrix, provides useful information for identification of additive-related biclusters. Consider the data in Figure 2, there are two biclusters: the first one (shown in blue color) is a constant bicluster while the second one (shown in yellow color) is an additive-related bicluster. As the rows in a bicluster is supposed to correlate in a subset of columns, the column difference between every two columns is computed so as to identify this column subset. There are altogether 6(6-1)/2 = 15 permutations as shown in the difference matrix in Figure 3. In the difference matrix, we can find special features that are related to the biclusters. For example, consider column "C5-C3". There are only three distinct difference values: 0 (5 counts), 1 (1 count), 2 (5 counts). This suggests the existence of three biclusters formed between "C5" and "C3":


Identification of coherent patterns in gene expression data using an efficient biclustering algorithm and parallel coordinate visualization.

Cheng KO, Law NF, Siu WC, Liew AW - BMC Bioinformatics (2008)

The difference matrix for the dataset shown in Figure 2.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2396181&req=5

Figure 3: The difference matrix for the dataset shown in Figure 2.
Mentions: The biclusters given in Figure 1(A)–(D) can be described by an additive model in which each pair of rows has the same difference in all the related columns or each pair of columns has the same difference in all the related rows. Thus, a difference matrix, each column of which represents the column differences between a pair of columns in a data matrix, provides useful information for identification of additive-related biclusters. Consider the data in Figure 2, there are two biclusters: the first one (shown in blue color) is a constant bicluster while the second one (shown in yellow color) is an additive-related bicluster. As the rows in a bicluster is supposed to correlate in a subset of columns, the column difference between every two columns is computed so as to identify this column subset. There are altogether 6(6-1)/2 = 15 permutations as shown in the difference matrix in Figure 3. In the difference matrix, we can find special features that are related to the biclusters. For example, consider column "C5-C3". There are only three distinct difference values: 0 (5 counts), 1 (1 count), 2 (5 counts). This suggests the existence of three biclusters formed between "C5" and "C3":

Bottom Line: Unfortunately, many useful formulations result in NP-complete problems.Biologically significant biclusters have been validated on the yeast cell-cycle expression dataset using Gene Ontology annotations.Comparative study shows that the proposed approach outperforms several existing biclustering algorithms.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Information and Communication Technology, Griffith University, Gold Coast Campus, QLD 4222, Queensland, Australia. k.o.cheng@polyu.edu.hk

ABSTRACT

Background: The DNA microarray technology allows the measurement of expression levels of thousands of genes under tens/hundreds of different conditions. In microarray data, genes with similar functions usually co-express under certain conditions only 1. Thus, biclustering which clusters genes and conditions simultaneously is preferred over the traditional clustering technique in discovering these coherent genes. Various biclustering algorithms have been developed using different bicluster formulations. Unfortunately, many useful formulations result in NP-complete problems. In this article, we investigate an efficient method for identifying a popular type of biclusters called additive model. Furthermore, parallel coordinate (PC) plots are used for bicluster visualization and analysis.

Results: We develop a novel and efficient biclustering algorithm which can be regarded as a greedy version of an existing algorithm known as pCluster algorithm. By relaxing the constraint in homogeneity, the proposed algorithm has polynomial-time complexity in the worst case instead of exponential-time complexity as in the pCluster algorithm. Experiments on artificial datasets verify that our algorithm can identify both additive-related and multiplicative-related biclusters in the presence of overlap and noise. Biologically significant biclusters have been validated on the yeast cell-cycle expression dataset using Gene Ontology annotations. Comparative study shows that the proposed approach outperforms several existing biclustering algorithms. We also provide an interactive exploratory tool based on PC plot visualization for determining the parameters of our biclustering algorithm.

Conclusion: We have proposed a novel biclustering algorithm which works with PC plots for an interactive exploratory analysis of gene expression data. Experiments show that the biclustering algorithm is efficient and is capable of detecting co-regulated genes. The interactive analysis enables an optimum parameter determination in the biclustering algorithm so as to achieve the best result. In future, we will modify the proposed algorithm for other bicluster models such as the coherent evolution model.

Show MeSH
Related in: MedlinePlus