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

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

Abstract

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.

https://academic.oup.com/bioinformaticsadvances/article/3/1/vbad009/7022005