IntLIM: Integration using Linear Models of metabolomics and gene expression data. Interpretation of metabolomics data is very challenging. Yet it can be eased through integration of metabolomics with other ‘omics’ data. The IntLIM package, which includes a user-friendly RShiny web app, aims to integrate metabolomics data with transcriptomic data. Unlike other approaches, IntLIM is focused on understanding how specific gene-metabolite associations are affected by phenotypic features. To this end, we develop a linear modeling approach that describes how gene-metabolite associations are affected by phenotype. The workflow involves the following steps: 1) input gene expression/metabolomics data files, 2) filter data sets by gene and metabolite abundances and imputed values, 3) run the linear model to extract FDR-adjusted interaction p-values, 4) filter results by p-values and Spearman correlation differences, and 5) plot/visualize specific gene-metabolite associations.
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References in zbMATH (referenced in 1 article , 1 standard article )
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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: IntLIM: Integration using Linear Models of metabolomics and gene expression data (2018) arXiv