CASI-package: Canonical Analysis of Set Interactions
In USCbiostats/CASI: Canonical Analysis of Set Interactions

Description Details Author(s) References

Assuming a case-control type study design, two sets of features are measured in both cases and controls. The objective of CASI hypothesis testing is to evaluate evidence of statistical interactions between these sets in relation to outcome status.

Package:	CASI
Type:	Package
Version:	1.0
Date:	2018-04-28
License:	Artistic-2.0
LazyLoad:	yes

Assuming a case-control type study design, two sets of features are measured in both cases and controls. The objective of CASI hypothesis testing is to evaluate evidence of statistical interactions between these sets in relation to outcome status. The two feature sets could be sets of SNPs underlying two genes, a set of environmental exposures and a set of SNPs, a set of SNPs and a set of DNA methylation probes, etc. The null hypothesis is that there are no two linear combinations of the two sets whose correlation differs between cases and controls. Such a difference in correlation would imply a statistical interaction. The distribution of the CASI statistic is unknown, so rather than provide a p-value, the CASI function generates the test statistic under the observed and null hypothesis conditions. The null conditions are imposed by randomly permuting case-control labels and repeating the analysis n.perm times. It is anticipated by the developers that an FDR approach will be applied to CASI function results generated from applications to multiple, perhaps thousands of pairs of features. However, a permutation-based p-value could be computed if the number of permutations is sufficiently large.

Joshua Millstein, Vladimir Kogan

Maintainer: Joshua Millstein <joshua.millstein@usc.edu> Joshua Millstein

Vladimir Kogan and Joshua Millstein. 2018. Genetic-Epigenetic Interactions in Asthma Revealed by a Genome-Wide Gene-Centric Search. Human Heredity (in review)

USCbiostats/CASI documentation built on May 29, 2019, 11:04 p.m.