Integration of Omics Data Using Linear Modeling

This workflow takes analyte levels from two different types of analytes (e.g. gene expression and metabolite abundance), meta-information on each analyte type, and sample outcome and metadata to identify analyte pairs that are significantly associated with a continuous or discrete outcome (e.g. drug response or tumor type). The following references describe the methods in this package: (1) Jalal K. Siddiqui, et al. (2018) , (2) Andrew Patt, et al. (2019) .

IntLIM: Integration through LInear Modeling

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 multiple types of omics data. Unlike other approaches, IntLIM is focused on understanding how specific analyte associations are affected by phenotypic features. To this end, we develop a linear modeling approach that describes how analyte associations are affected by phenotype. The workflow involves the following steps: 1) input analyte level (e.g., expression or abundance) data files, 2) filter data sets by analyte level and imputed values, 3) run the linear model to extract FDR-adjusted interaction p-values, 4) filter results by p-values, interaction coefficient percentile, r-squared value, and Spearman correlation differences, and 5) plot/visualize specific analyte 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: 29506475; PMCID: PMC5838881 DOI: 10.1186/s12859-018-2085-6

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IntLIM prerequisites

IntLIM is an R package and can be run on version >= 3.5.0.

Installation from Github

To install IntLIM, simply type the following in the R terminal:

install.packages("devtools")
library(devtools)
devtools::install_github("ncats/IntLIM")

Vignette

A detailed vignette can be found here.

Formatted Data and Analysis Codes

Formatted data and codes to reproduce the NCI-60 and breast cancer analyses can be obtained from the following GitHub repository:

https://github.com/ncats/IntLIMVignettes

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 [email protected] or Tara Eicher at [email protected]. We are also very open to any comments, including how we can improve and ameliorate the package.