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Updated: Aug 6, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
Published on: September 20, 2024
IntLIM 2.0: identifying multi-omic relationships dependent on discrete or continuous phenotypic measurements
Tara Eicher1,2, Kyle D Spencer1,3, Jalal K Siddiqui4
1Division of Preclinical Innovation, National Center for Advancing Translational Sciences, NIH, Rockville, MD 20892, USA.
Motivation:
IntLIM uncovers phenotype-dependent linear associations between two types of analytes (e.g. genes and metabolites) in a multi-omic dataset, which may reflect chemically or biologically relevant relationships.
Results:
The new IntLIM R package includes newly added support for generalized data types, covariate correction, continuous phenotypic measurements, model validation and unit testing. IntLIM analysis uncovered biologically relevant gene-metabolite associations in two separate datasets, and the run time is improved over baseline R functions by multiple orders of magnitude.
Availability And Implementation:
IntLIM is available as an R package with a detailed vignette (https://github.com/ncats/IntLIM) and as an R Shiny app (see Supplementary Figs S1-S6) (https://intlim.ncats.io/).
Supplementary Information:
Supplementary data are available at Bioinformatics Advances online.
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