The Mergeomics pipeline serves as a flexible framework for integrating multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It includes two main parts, 1) Marker set enrichment analysis (MSEA); 2) Weighted Key Driver Analysis (wKDA).
|License:||GPL (>= 2)|
|Depends: R (>= 3.0.1)|
Mergeomics amalgamates disease association information derived from multidimensional omics data (e.g., genome, epigenome, transcriptome, metablome) with functional genomics (e.g., eQTLs, ENCODE), canonical pathways (e.g., KEGG, Reactome), and molecular networks (e.g., gene regulatory networks, protein-protein interaction networks). Two main steps of the pipeline are: Marker set enrichment analysis (MSEA) and weighted key driver analysis (wKDA). MSEA takes the following data as input: i) disease association data (GWAS, EWAS, TWAS...), ii) functional genomics (eQTLs and/or ENCODE information), and iii) functionally related genes information extracted from knowledge-based biological pathways or data-driven network modules (e.g., coexpressed genes in a given tissue relevant to a disease of interest). These datasets are integrated via MSEA to return gene sets that are significantly enriched for markers showing low p value associations with a given disease. Then, the disease related gene sets are examined to detect the key drivers by using the wKDA step of the pipeline, which requires pre-defined directional networks such as tissue-specific Bayesian networks, protein-protein interaction networks, etc. wKDA maps the disease related gene sets to the pre-defined directional networks to identify key driver genes that are more likely regulators of the disease gene sets based on their central positions in the gene networks. The key drivers and their local network topology can be viewed and downloaded after the completion of the analysis via Visualization step. Our pipeline provides users to perform MSEA and wKDA together or separately using either their own input data or selecting preloaded sample datasets. The details of the functions and parameter settings are described in the Manual of the package.
Ville-Petteri Makinen, Le Shu, Yuqi Zhao, Zeyneb Kurt, Bin Zhang, Xia Yang Maintainer: <email@example.com>
Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD, Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X. Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems. BMC genomics. 2016;17(1):874.
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