Results of performing Gene Ontology over-representation analysis (Fisher Exact / Hypergeometric test) on each of the modules produced by Weighted Gene Co-expression Network Analysis (WGCNA).
TRNSCRPT_MODULE_ORA # transcriptomics
PROT_MODULE_ORA # proteomics
METAB_MODULE_ORA # metabolomicsAn object of class data.table (inherits from data.frame) with 2615 rows and 12 columns.
The same gene sets and RefMet subclasses that were used to generate the FGSEA results were also used here.
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., Harris, M. A., Hill, D. P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J. C., Richardson, J. E., Ringwald, M., Rubin, G. M., & Sherlock, G. (2000). Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature genetics, 25(1), 25–29. https://doi.org/10.1038/75556
Dolgalev, I. msigdbr: MSigDB Gene Sets for Multiple Organisms in a Tidy Data Format. R package version 7.5.1, https:://igordot.github.io/msigdbr
Gene Ontology Consortium (2021). The Gene Ontology resource: enriching a GOld mine. Nucleic acids research, 49(D1), D325–D334. https://doi.org/10.1093/nar/gkaa1113
Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015). The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell systems, 1(6), 417–425. https://doi.org/10.1016/j.cels.2015.12.004