{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/intlim-integration-using-linear-models-of","title":"IntLIM: Integration using Linear Models of metabolomics and gene expression data","arxiv_id":"1802.10588","date":"2018-02-28","proceeding":null,"authors":[],"abstract":"Integration of transcriptomic and metabolomic data improves functional\ninterpretation of disease-related metabolomic phenotypes, and facilitates\ndiscovery of putative metabolite biomarkers and gene targets. For this reason,\nthese data are increasingly collected in large cohorts, driving a need for the\ndevelopment of novel methods for their integration. Of note,\nclinical/translational studies typically provide snapshot gene and metabolite\nprofiles and, oftentimes, most metabolites are not identified. Thus, in these\ntypes of studies, pathway/network approaches that take into account the\ncomplexity of gene-metabolite relationships may neither be applicable nor\nreadily uncover novel relationships. With this in mind, we propose a simple\nlinear modeling approach to capture phenotype-specific gene-metabolite\nassociations, with the assumption that co-regulation patterns reflect\nfunctionally related genes and metabolites. The proposed linear model,\nmetabolite ~ gene + phenotype + gene:phenotype, specifically evaluates whether\ngene-metabolite relationships differ by phenotype, by testing whether the\nrelationship in one phenotype is significantly different from the relationship\nin another phenotype (via an interaction gene:phenotype p-value). Interaction\np-values for all possible gene-metabolite pairs are computed and significant\npairs are clustered by the directionality of associations. We implemented our\napproach as an R package, IntLIM, which includes a user-friendly Shiny app. We\napplied IntLIM to two published datasets, collected in NCI-60 cell lines and in\nhuman breast tumor and non-tumor tissue. We demonstrate that IntLIM captures\nrelevant tumor-specific gene-metabolite associations involved in cancer-related\npathways. and also uncover novel relationships that could be tested\nexperimentally. The IntLIM R package is publicly available in GitHub\n(https://github.com/mathelab/IntLIM).","url_abs":"http://arxiv.org/abs/1802.10588v1","url_pdf":"http://arxiv.org/pdf/1802.10588v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"intlim-integration-using-linear-models-of","repo_url":"https://github.com/mathelab/IntLIM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}