This is the released version of pairkat; for the devel version, see pairkat.
PaIRKATBioconductor version: Release (3.21)
PaIRKAT is model framework for assessing statistical relationships between networks of metabolites (pathways) and an outcome of interest (phenotype). PaIRKAT queries the KEGG database to determine interactions between metabolites from which network connectivity is constructed. This model framework improves testing power on high dimensional data by including graph topography in the kernel machine regression setting. Studies on high dimensional data can struggle to include the complex relationships between variables. The semi-parametric kernel machine regression model is a powerful tool for capturing these types of relationships. They provide a framework for testing for relationships between outcomes of interest and high dimensional data such as metabolomic, genomic, or proteomic pathways. PaIRKAT uses known biological connections between high dimensional variables by representing them as edges of âgraphsâ or ânetworks.â It is common for nodes (e.g. metabolites) to be disconnected from all others within the graph, which leads to meaningful decreases in testing power whether or not the graph information is included. We include a graph regularization or âsmoothingâ approach for managing this issue.
Author: Charlie Carpenter [aut], Cameron Severn [aut], Max McGrath [cre, aut]
Maintainer: Max McGrath <max.mcgrath at ucdenver.edu>
Citation (from within R, entercitation("pairkat")): Installation
To install this package, start R (version "4.5") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("pairkat")
For older versions of R, please refer to the appropriate Bioconductor release.
DocumentationTo view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("pairkat")Follow Installation instructions to use this package in your R session.
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