Create an Object of InteractionResult Class for Testing Interaction

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Description

Based on the result from the interaction test by looking at the result from the regressResult object, this function partitions tne orignal data, an ExpressionSetinto groups, one contains the genes without the interaction and others contains the genes with the interaction across different level of covariates.

Usage

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postInteraction(eSet, regressObject, mainVar, compare1, compare2, method = regressionMethod(regressObject), adj = adjustment(regressObject))

Arguments

eSet

an ExpressionSet

regressObject

a regressResult

mainVar

variable of main interest

compare1

the first value of the mainVar. For example, suppose that mainVar is "drug", and there are three unique values: "drug1", "drug2", and "placebo". You would like to compare "drug1" to "drug2". Then you would use "drug1" as compare1

compare2

Based on the example for compare1, "drug2" will be the compare2

method

It is used to run regression within each level of the effect modifier. choose the follwoing three options: "limma" (LIMMA), "regression" (ordinary linear regression), "permutation" (permutation test)

adj

adjustment method for multiple comparison test, including "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". The default value is "none". Type help(p.adjust) for more detail.

Value

an interactionResult class. The first component contains all the result for all the genes. The second component contains the genes without the interaction effect. The rest of the components contains genes with the interactions.

Author(s)

Xiwei Wu, Arthur Li

Examples

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data(eSetExample)
design.int<- new("designMatrix", target=pData(eSetExample), covariates = c("Treatment", "Group"),
    intIndex = c(1, 2))
contrast.int<- new("contrastMatrix", design.matrix = design.int, interaction=TRUE)
result.int<- regress(eSetExample, contrast.int)
sigResult.int <- selectSigGene(result.int)
intResult <- postInteraction(eSetExample, sigResult.int, mainVar ="Treatment",
   compare1 = "Treated", compare2 = "Control")

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