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Identification of differentially expressed subnetworks based on multivariate ANOVA.

Hwang T, Park T - BMC Bioinformatics (2009)

Bottom Line: Our approach was successfully applied to human microarray datasets.Each identified subnetwork was annotated with the Gene Ontology (GO) term, resulting in the phenotype-related functional pathway or complex.We also compared these results with those of other scoring methods such as t statistic- and mutual information-based scoring methods.

View Article: PubMed Central - HTML - PubMed

Affiliation: Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Republic of Korea. hwangty@snu.ac.kr

ABSTRACT

Background: Since high-throughput protein-protein interaction (PPI) data has recently become available for humans, there has been a growing interest in combining PPI data with other genome-wide data. In particular, the identification of phenotype-related PPI subnetworks using gene expression data has been of great concern. Successful integration for the identification of significant subnetworks requires the use of a search algorithm with a proper scoring method. Here we propose a multivariate analysis of variance (MANOVA)-based scoring method with a greedy search for identifying differentially expressed PPI subnetworks.

Results: Given the MANOVA-based scoring method, we performed a greedy search to identify the subnetworks with the maximum scores in the PPI network. Our approach was successfully applied to human microarray datasets. Each identified subnetwork was annotated with the Gene Ontology (GO) term, resulting in the phenotype-related functional pathway or complex. We also compared these results with those of other scoring methods such as t statistic- and mutual information-based scoring methods. The MANOVA-based method produced subnetworks with a larger number of proteins than the other methods. Furthermore, the subnetworks identified by the MANOVA-based method tended to consist of highly correlated proteins.

Conclusion: This article proposes a MANOVA-based scoring method to combine PPI data with expression data using a greedy search. This method is recommended for the highly sensitive detection of large subnetworks.

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Related in: MedlinePlus

The distribution of the number of proteins in a subnetwork. (a) Serum response data. (b) Prostate cancer metastasis data.
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Figure 1: The distribution of the number of proteins in a subnetwork. (a) Serum response data. (b) Prostate cancer metastasis data.

Mentions: Next, we examined the sizes of the significant subnetworks commonly identified by all three methods. Figure 1 shows the distribution of number of proteins in the commonly identified subnetworks. The MANOVA-based scoring method usually identified subnetworks with a larger number of nodes (genes) than the other methods. In summary, the MANOVA-based scoring method tended to yield a smaller number of significant subnetworks with larger numbers of proteins than the other scoring methods.


Identification of differentially expressed subnetworks based on multivariate ANOVA.

Hwang T, Park T - BMC Bioinformatics (2009)

The distribution of the number of proteins in a subnetwork. (a) Serum response data. (b) Prostate cancer metastasis data.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2696448&req=5

Figure 1: The distribution of the number of proteins in a subnetwork. (a) Serum response data. (b) Prostate cancer metastasis data.
Mentions: Next, we examined the sizes of the significant subnetworks commonly identified by all three methods. Figure 1 shows the distribution of number of proteins in the commonly identified subnetworks. The MANOVA-based scoring method usually identified subnetworks with a larger number of nodes (genes) than the other methods. In summary, the MANOVA-based scoring method tended to yield a smaller number of significant subnetworks with larger numbers of proteins than the other scoring methods.

Bottom Line: Our approach was successfully applied to human microarray datasets.Each identified subnetwork was annotated with the Gene Ontology (GO) term, resulting in the phenotype-related functional pathway or complex.We also compared these results with those of other scoring methods such as t statistic- and mutual information-based scoring methods.

View Article: PubMed Central - HTML - PubMed

Affiliation: Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Republic of Korea. hwangty@snu.ac.kr

ABSTRACT

Background: Since high-throughput protein-protein interaction (PPI) data has recently become available for humans, there has been a growing interest in combining PPI data with other genome-wide data. In particular, the identification of phenotype-related PPI subnetworks using gene expression data has been of great concern. Successful integration for the identification of significant subnetworks requires the use of a search algorithm with a proper scoring method. Here we propose a multivariate analysis of variance (MANOVA)-based scoring method with a greedy search for identifying differentially expressed PPI subnetworks.

Results: Given the MANOVA-based scoring method, we performed a greedy search to identify the subnetworks with the maximum scores in the PPI network. Our approach was successfully applied to human microarray datasets. Each identified subnetwork was annotated with the Gene Ontology (GO) term, resulting in the phenotype-related functional pathway or complex. We also compared these results with those of other scoring methods such as t statistic- and mutual information-based scoring methods. The MANOVA-based method produced subnetworks with a larger number of proteins than the other methods. Furthermore, the subnetworks identified by the MANOVA-based method tended to consist of highly correlated proteins.

Conclusion: This article proposes a MANOVA-based scoring method to combine PPI data with expression data using a greedy search. This method is recommended for the highly sensitive detection of large subnetworks.

Show MeSH
Related in: MedlinePlus