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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.
An example data set is provided within the package, and is a subset of the NCI-60 gene expression and metabolomics data (https://wiki.nci.nih.gov/display/NCIDTPdata/Molecular+Target+Data). The vignette outlines how to run the workflow. More details can be found in our publication "IntLIM: integration using linear models of metabolomics and gene expression data".
If you use IntLIM, please cite the following work:
Siddiqui JK, Baskin E, Liu M, Cantemir-Stone CZ, Zhang B, Bonneville R, McElroy JP, Coombes KR, Mathé EA. IntLIM: integration using linear models of metabolomics and gene expression data. BMC Bioinformatics. 2018 Mar 5;19(1):81. doi: 10.1186/s12859-018-2085-6.
PMID: 229506475; PMCID: PMC5838881 DOI: 10.1186/s12859-018-2085-6
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IntLIM is an R package and can be run on version >= 3.2.0.
Download (or upgrade) R here: https://cloud.r-project.org/
RStudio (an interface to R than can make R easier to use) can be download here (not required): https://www.rstudio.com/products/rstudio/download3/
Prior to installing IntLIM, it is necessary to have the Bioconductor package MultiDataSet (Hernandez-Ferrer et al, 2017).
The following command then installs MultiDataSet.
## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("MultiDataSet")
If you have R version >= 3.6, install MultiDataset by typing:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("MultiDataSet")
To install IntLIM, simply type the following in the R terminal:
install.packages("devtools")
library(devtools)
devtools::install_github("mathelab/IntLIM")
A detailed vignette can be found here: https://mathelab.github.io/IntLIM/IntLIMVignette.html
Formatted data and codes to reproduce the NCI-60 analyses can be obtained from the following GitHub repository:
https://github.com/Mathelab/NCI60_GeneMetabolite_Data
Formatted data and codes to reproduce the breast cancer analyses can be obtained from the following GitHub repository:
https://github.com/Mathelab/BreastCancerAmbs_GeneMetabolite_Data
The package functions can be run directly in the R console.
Alternatively, to launch the web app, type the following in your R console:
library(IntLIM)
runIntLIMApp()
If you encounter any problems running on the software, or find installation problems or bugs, please start an issue on the Issues tab or email Ewy Mathe at Ewy.Mathe@osumc.edu or Jalal Siddiqui at jalal.siddiqui@osumc.edu. We are also very open to any comments, including how we can improve and ameliorate the package.