Identification of differentially expressed subnetworks based on multivariate ANOVA.
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.


CONCLUSIONS
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.
Hwang
Taeyoung
T
Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Republic of Korea. hwangty@snu.ac.kr
Park
Taesung
T
D000465:Algorithms; D000704:Analysis of Variance; D003198:Computer Simulation; D005347:Fibroblasts / Q000378:metabolism; D020869:Gene Expression Profiling; D006801:Humans; D008297:Male; D020411:Oligonucleotide Array Sequence Analysis; D011471:Prostatic Neoplasms / Q000378:metabolism; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping / Q000379:methods*; D040901:Proteomics / Q000379:methods*; D012680:Sensitivity and Specificity