Abstract
A relation exists between network proximity of molecular entities in interaction networks, functional similarity and association with diseases. The identification of network regions associated with biological functions and pathologies is a major goal in systems biology. We describe a network diffusion-based pipeline for the interpretation of different types of omics in the context of molecular interaction networks. We introduce the network smoothing index, a network-based quantity that allows to jointly quantify the amount of omics information in genes and in their network neighbourhood, using network diffusion to define network proximity. The approach is applicable to both descriptive and inferential statistics calculated on omics data. We also show that network resampling, applied to gene lists ranked by quantities derived from the network smoothing index, indicates the presence of significantly connected genes. As a proof of principle, we identified gene modules enriched in somatic mutations and transcriptional variations observed in samples of prostate adenocarcinoma (PRAD). In line with the local hypothesis, network smoothing index and network resampling underlined the existence of a connected component of genes harbouring molecular alterations in PRAD.
Highlights
Network diffusion-based approaches, which simulate the diffusion of a quantity throughout a network in order to calculate a global measure of network proximity, have been successfully proposed in several applications, taking advantage of the local hypothesis
We show that the network smoothing index (S), a network diffusion-based quantity introduced here, is a simple and informative measure to jointly quantify the amount of omics information associated with a molecular entity and the information in network proximity to it
As a proof of principle, we apply these tools to spot PPI network regions differentially enriched in somatic mutations (SM) and gene expression (GE) variations between two prognostic groups of patients affected by prostate adenocarcinoma (PRAD)
Summary
Network diffusion-based approaches, which simulate the diffusion of a quantity throughout a network in order to calculate a global measure of network proximity, have been successfully proposed in several applications, taking advantage of the local hypothesis. Hotnet[8] uses statistics derived from somatic mutations as input for a diffusion process in order to identify active network regions. TieDie[10] and ResponseNet[11] use two different approaches to find the subnetwork that connects two sets (sources and targets) of network vertices, which can represent genomic perturbations and gene expression variations. RegMod[12] was proposed to find disease-associated modules using interactions and gene expression data; this approach uses the support vector regression method with a diffusion kernel in order to find active modules. We show that the network smoothing index (S), a network diffusion-based quantity introduced here, is a simple and informative measure to jointly quantify the amount of omics information associated with a molecular entity (e.g. gene, mRNA, protein) and the information in network proximity to it. We implemented the pipeline used in our study into an R package (http:// www.interomics.eu/tools)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
