PaIRKAT: Perform PaIRKAT on the output from the GatherNetworks...
In Ghoshlab/pairkat: PaIRKAT

Description Usage Arguments Details Value References Examples

View source: R/PaIRKAT.R

Pathway Integrated Regression-based Kernel Association Test (PaIRKAT) is a model framework for assessing statistical relationships between networks and some outcome of interest while adjusting for potential confounders and covariates.

Use of PaIRKAT is motivated by the analysis of networks of metabolites from a metabolomics assay and the relationship of those networks with a phenotype or clinical outcome of interest, though the method can be generalized to other domains.

1	PaIRKAT(formula.H0, networks, tau = 1)

`formula.H0`	The null model in the "formula" format used in `lm` and `glm` functions
`networks`	networks object obtained with `GatherNetworks`
`tau`	A parameter to control the amount of smoothing, analagous to a bandwidth parameter in kernel smoothing. We found 1 often gave reasonable results, as over-smoothing can lead to inflated Type I errors.

The PaIRKAT method is to update the feature matrix, Z, with the regularized normalized Laplacian, L_R, before performing the kernel association test. L_R is calculated using a "linear" regularization,

L_R = (I +τ L)^-1,

where I is the identity matrix, τ is a regularization parameter that controls the amount of smoothing, and L is the graph's normalized Laplacian. The updated feature matrix, Z*L_R is matrix used for the kernel association test.
The linear regularization and Gaussian kernel is used for all tests. See Carpenter 2021 for complete details on PaIRKAT and Smola 2003 for information about graph regularization

a list object containing the formula call and results by pathway

Carpenter CM, Zhang W, Gillenwater L, Severn C, Ghosh T, Bowler R, et al. PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes. bioRxiv. 2021 Apr 26;2021.04.23.440821.

Smola AJ, Kondor R. Kernels and Regularization on Graphs. In: Schölkopf B, Warmuth MK, editors. Learning Theory and Kernel Machines. Berlin, Heidelberg: Springer Berlin Heidelberg; 2003. p. 144–58. (Goos G, Hartmanis J, van Leeuwen J, editors. Lecture Notes in Computer Science; vol. 2777). http://link.springer.com/10.1007/978-3-540-45167-9_12

library(SummarizedExperiment)
data(smokers)

# Query KEGGREST API
networks <- GatherNetworks(SE = smokers, keggID = "kegg_id",
species = "hsa", minPathwaySize = 5)

# Run PaIRKAT Analysis
output <- PaIRKAT(log_FEV1_FVC_ratio ~ age, networks = networks)

# View Results
output$results