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cff-version: 1.2.0
message: 'To cite package "pairkat" in publications use:'
type: software
license: GPL-3.0-only
title: 'pairkat: PaIRKAT'
version: 1.11.0
abstract: 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 ‘graphs’ or
  ‘networks.’ 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 ‘smoothing’
  approach for managing this issue.
authors:
- family-names: Carpenter
  given-names: Charlie
  email: charles.carpenter@cuanschutz.edu
- family-names: Severn
  given-names: Cameron
  email: cameron.severn@gmail.com
- family-names: McGrath
  given-names: Max
  email: max.mcgrath@ucdenver.edu
repository: https://bioc.r-universe.dev
repository-code: https://github.com/Ghoshlab/pairkat
url: https://github.com/Ghoshlab/pairkat
contact:
- family-names: McGrath
  given-names: Max
  email: max.mcgrath@ucdenver.edu