IntLIM 2.0: identifying multi-omic relationships dependent on discrete or continuous phenotypic measurements

Bioinform Adv. 2023 Feb 1;3(1):vbad009. doi: 10.1093/bioadv/vbad009. eCollection 2023.


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 ( and as an R Shiny app (see Supplementary Figs S1-S6) (

Supplementary information: Supplementary data are available at Bioinformatics Advances online.