Age-related Subpathway Detection through Meta-analysis of Multiple Gene Expression Datasets

A novel perspective of systems biology is the incorporation of pathway structure data along with transcriptomics studies. In parallel, the plethora of high-throughput experimental studies necessitates employment of meta-analysis approaches in order to obtain more biologically consistent results. Towards this orientation we developed a subpathway-based meta-analysis method that integrates human pathway maps along with multiple human mRNA expression experiments. Our method succeeded to identify known age-related subpathways as differentially expressed exploiting several independent muscle-specific aging studies. Finally, our method is applicable in several complex biological problems where massive amount of time series expression data is available.