Abstract
Validation of pluripotent modules in diverse networks holds enormous potential for systems biology and network pharmacology. An arising challenge is how to assess the accuracy of discovering all potential modules from multi-omic networks and validating their architectural characteristics based on innovative computational methods beyond function enrichment and biological validation. To display the framework progress in this domain, we systematically divided the existing Computational Validation Approaches based on Modular Architecture (CVAMA) into topology-based approaches (TBA) and statistics-based approaches (SBA). We compared the available module validation methods based on 11 gene expression datasets, and partially consistent results in the form of homogeneous models were obtained with each individual approach, whereas discrepant contradictory results were found between TBA and SBA. The TBA of the Zsummary value had a higher Validation Success Ratio (VSR) (51%) and a higher Fluctuation Ratio (FR) (80.92%), whereas the SBA of the approximately unbiased (AU) p-value had a lower VSR (12.3%) and a lower FR (45.84%). The Gray area simulated study revealed a consistent result for these two models and indicated a lower Variation Ratio (VR) (8.10%) of TBA at 6 simulated levels. Despite facing many novel challenges and evidence limitations, CVAMA may offer novel insights into modular networks.
Highlights
Several published studies have employed Computational Validation Approaches based on Modular Architecture (CVAMA) to evaluate modules’ authenticity, reproducibility, and significance or to identify phenotype-related functional modules[31,32,33,34,35]
We summarized the available CVAMA methods, which were divided into topology-based approaches (TBA) and statistics-based approaches (SBA)
The feasibility of CVAMA was demonstrated in our applications of TBA and SBA-based module validation methods in genomic network modules
Summary
Several published studies have employed Computational Validation Approaches based on Modular Architecture (CVAMA) to evaluate modules’ authenticity, reproducibility, and significance or to identify phenotype-related functional modules[31,32,33,34,35]. These approaches are not limited by module size and supporting databases. With an increasing number of omics technologies and module analysis methods, CVAMA may become the new focus. One representative method of each was selected to validate modules obtained from genomic datasets, and comparative analyses were performed to illuminate the feasibility and challenges in CVAMA
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