Abstract
Genes are organized in functional modules (or pathways), thus their action and their dysregulation in diseases may be better understood by the identification of the modules most affected by the disease (aka disease modules, or active subnetworks). We describe how an algorithm based on the Core&Peel method is used to detect disease modules in co-expression networks of genes. We first validate Core&Peel for the general task of functional module detection by comparison with 42 methods participating in the Disease Module Identification DREAM challenge. Next, we use four specific disease test cases (colorectal cancer, prostate cancer, asthma, and rheumatoid arthritis), four state-of-the-art algorithms (ModuleDiscoverer, Degas, KeyPathwayMiner, and ClustEx), and several pathway databases to validate the proposed algorithm. Core&Peel is the only method able to find significant associations of the predicted disease module with known validated relevant pathways for all four diseases. Moreover, for the two cancer datasets, Core&Peel detects further eight relevant pathways not discovered by the other methods used in the comparative analysis. Finally, we apply Core&Peel and other methods to explore the transcriptional response of human cells to SARS-CoV-2 infection, finding supporting evidence for drug repositioning efforts at a pre-clinical level.
Highlights
Genes are organized in functional modules, their action and their dysregulation in diseases may be better understood by the identification of the modules most affected by the disease
Like ModuleDiscoverer, we use the list of differentially expressed genes (DEGs) in case-control experiments to compute the enrichment of each module in DEGs, and we report as active modules those with an enrichment below a given False Discovery Rate (FDR) threshold
Adapting the DREAM challenge evaluation methodology to the case of overlapping functional modules we conclude that, in terms of the number of enriched detected modules, Core&Peel is competitive with 42 functional module detection methods participating in the DREAM Challenge, and with the recent method by Raman’s group[23]
Summary
Genes are organized in functional modules (or pathways), their action and their dysregulation in diseases may be better understood by the identification of the modules most affected by the disease (aka disease modules, or active subnetworks). In a typical systems biology paradigm, large amount of molecular data collected via high throughput ’omics’ experiments are stored in curated databases filtered and reorganized in the form of an interaction network among molecular species (for example, co-expression networks are built via measures of the co-expression of genes under a variety of conditions)[1,2] Such a network is analyzed to detect interesting phenomena from a biological point of view, potentially relevant for a phenotype of interest or a specific biological process. In the second task (Fig. 1b), we selected only the Core&Peel modules significantly enriched for the differentially expressed genes to obtain active subnetworks in two cancer types (prostate and colorectal cancer), two inflammatory diseases (asthma and rheumatoid arthritis) and COVID-19 infection
Sign-in/Register to view highlights & summary 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.
