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PRASE: PageRank-based Active Subnetwork Extraction

Integrating protein-protein interaction networks with gene expression data to extract active subnetworks is shown to be promising in detecting meaningful biomarkers for cancer and other diseases. Lately, the RNA-Seq technology became the new standard for gene expression. Existing algorithms either cannot handle the RNA-Seq data or return large subnetworks which are hard to analyze. Therefore, new approaches to utilize and integrate the RNA-Seq data to the subnetwork extraction process are needed. In this work, using the RNA-Seq data, we propose a new workflow PRASE to obtain more focused subnetworks which contain important genes even if they are not differentially expressed. Although the hub nodes in the PPI network may be good candidates for such genes, they are not the only ones. A gene which is not differentially expressed and which does not have many interactions with the other genes can still be functional on many critical pathways. To prioritize such genes, PRASE employs the famous PageRank algorithm and apply a preprocessing on the gene expression p-values. Then, it applies a scaling function to construct new p-values for the genes which are then used with the existing active subnetwork extraction tools to generate the final subnetwork. We applied our workflow on colorectal cancer, oligodendroglioma tumor, and breast cancer datasets. Our evaluation shows that, using PRASE, we can obtain more specialized subnetworks which contain information that is overlooked by existing approaches.