IntLIM: Integration using Linear Models of metabolomics and gene expression data

Jalal K. Siddiqui; Elizabeth Baskin; Mingrui Liu; Carmen Z. Cantemir-Stone; Bofei Zhang; Russell Bonneville; Joseph P. McElroy; Kevin R. Coombes; Ewy A. Mathé

arXiv · 1802.10588

IntLIM: Integration using Linear Models of metabolomics and gene expression data

Jalal K. Siddiqui·Elizabeth Baskin·Mingrui Liu·Carmen Z. Cantemir-Stone·Bofei Zhang·Russell Bonneville·Joseph P. McElroy·Kevin R. Coombes·Ewy A. Mathé

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Abstract

Integration of transcriptomic and metabolomic data improves functional interpretation of disease-related metabolomic phenotypes, and facilitates discovery of putative metabolite biomarkers and gene targets. For this reason, these data are increasingly collected in large cohorts, driving a need for the development of novel methods for their integration. Of note, clinical/translational studies typically provide snapshot gene and metabolite profiles and, oftentimes, most metabolites are not identified. Thus, in these types of studies, pathway/network approaches that take into account the complexity of gene-metabolite relationships may neither be applicable nor readily uncover novel relationships. With this in mind, we propose a simple linear modeling approach to capture phenotype-specific gene-metabolite associations, with the assumption that co-regulation patterns reflect functionally related genes and metabolites. The proposed linear model, metabolite ~ gene + phenotype + gene:phenotype, specifically evaluates whether gene-metabolite relationships differ by phenotype, by testing whether the relationship in one phenotype is significantly different from the relationship in another phenotype (via an interaction gene:phenotype p-value). Interaction p-values for all possible gene-metabolite pairs are computed and significant pairs are clustered by the directionality of associations. We implemented our approach as an R package, IntLIM, which includes a user-friendly Shiny app. We applied IntLIM to two published datasets, collected in NCI-60 cell lines and in human breast tumor and non-tumor tissue. We demonstrate that IntLIM captures relevant tumor-specific gene-metabolite associations involved in cancer-related pathways. and also uncover novel relationships that could be tested experimentally. The IntLIM R package is publicly available in GitHub (https://github.com/mathelab/IntLIM).

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Jalal K. Siddiqui, Elizabeth Baskin, Mingrui Liu, Carmen Z. Cantemir-Stone, Bofei Zhang, Russell Bonneville, Joseph P. McElroy, Kevin R. Coombes, Ewy A. Mathé. 2018-02-28. IntLIM: Integration using Linear Models of metabolomics and gene expression data. https://arxiv.org/abs/1802.10588

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