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README.md

IntLIM: Integration through LInear Modeling

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New! IntLIM app is accessible via a server (no installation needed!).

Please click here. And let us know if additional functionalities would be useful (see contact info below).

IntLIM

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".

Citation

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

To access, click here

IntLIM prerequisites

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/

Installation from Github

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")

Vignette

A detailed vignette can be found here: https://mathelab.github.io/IntLIM/IntLIMVignette.html

Formatted Data and Analysis Codes

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

Running IntLIM's user-friendly web app:

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()

Contact

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.

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