Finding disease modules for cancer and COVID-19 in gene co-expression networks with the Core&Peel method
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Abstract
Diseases imply dysregulation of cell’s functions at several levels. The study of differentially expressed genes in case-control cohorts of patients is often the first step in understanding the details of the cell’s dysregulation. A further level of analysis is introduced by noticing that genes are organized in functional modules (often called pathways), thus their action and their dysregulation 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 developed originally for detecting protein complexes in PPI networks, can be adapted to detect disease modules in co-expression networks of genes. We first validate Core&Peel for the easier general task of functional module detection by comparison with 42 methods participating in the Disease Module Identification DREAM challenge of 2019. 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 data sets, Core&Peel detects further nine relevant pathways enriched in the predicted disease module, not discovered by the other methods used in the comparative analysis. Finally we apply Core&Peel, along with other methods, to explore the transcriptional response of human cells to SARS-CoV-2 infection, at a modular level, aiming at finding supporting evidence for drug repositioning efforts.
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SciScore for 10.1101/2020.05.27.118414: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Within these caveats, we follow strictly the performance measurement methodology of DREAM. We use separately the GWAS leaderboard and the final datasets (see Figure 1). The results on the GWAS leaderboard are used to optimize the choice of the parameters of the Core&Peel method. Figure 1 reports good qualitative concordance of the relative …
SciScore for 10.1101/2020.05.27.118414: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Within these caveats, we follow strictly the performance measurement methodology of DREAM. We use separately the GWAS leaderboard and the final datasets (see Figure 1). The results on the GWAS leaderboard are used to optimize the choice of the parameters of the Core&Peel method. Figure 1 reports good qualitative concordance of the relative performance of Core&Peel between leaderboard and final GWAS datasets, for a wide range of parameters. There is a discrepancy for the density value d = 1.0, which corresponds to detecting full cliques in the input graph. Since even the best co-expr networks are approximations to the true network of interactions, missing edges (false negatives) are to be expected in the input, thus in effect implying that quasi-cliques may be more relevant to functional module detection than full-cliques. Figure 2 reports the comparison of the absolute number of enriched modules found by Core&Peel (with the selected parameters) versus the DREAM methods. Both for the leaderboard and the final GWAS data, Core&Peel restricted to the top k modules finds more enriched modules most of the times (for final GWAS 39/42 times, for leaderboard GWAS 35/42 times). Comparing the method of Tripathi et al. 2019 [18], results in Table 1 and Figure 3 show that Core&Peel is able to detect many more significant modules (with a common Jaccard coefficient maximum threshold of 0.8), and that Core&Peel has a larger fraction of the reported modules enriched for GWAS data. We also per...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- No funding statement was detected.
- No protocol registration statement was detected.
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