postHocTest {DAPAR} | R Documentation

## Post-hoc tests for classic 1-way ANOVA

### Description

This function allows to compute a post-hoc test after a 1-way ANOVA analysis. It expects as input an object obtained with the function `classic1wayAnova`. The second parameter allows to choose between 2 different post-hoc tests: the Tukey Honest Significant Differences (specified as "TukeyHSD") and the Dunnett test (specified as "Dunnett").

### Usage

    postHocTest(aov_fits, post_hoc_test = "TukeyHSD")

### Arguments

`aov_fits` | a list containing aov fitted model objects
`post_hoc_test` | a character string indicating which post-hoc test to use. Possible values are "TukeyHSD" or "Dunnett". See details for what to choose according to your experimental design.

### Details

This is a function allowing to realise post-hoc tests for a set of proteins/peptides for which a classic 1-way anova has been performed with the function `classic1wayAnova`. Two types of tests are currently available: The Tukey HSD's test and the Dunnett's test. Default is Tukey's test. The Tukey HSD's test compares all possible pairs of means, and is based on a studentized range distribution. Here is used the `TukeyHSD()` function, which can be applied to balanced designs (same number of samples in each group), but also to midly unbalanced designs. The Dunnett's test compares a single control group to all other groups. Make sure the factor levels are properly ordered.

### Value

a list of 2 dataframes: first one called "LogFC" contains all pairwise comparisons logFC values (one column for one comparison) for each analysed feature; The second one named "P_Value" contains the corresponding pvalues.

### Author(s)

Hélène Borges

### Examples

    utils::data(Exp1_R25_prot, package='DAPARdata')
    obj <- Exp1_R25_prot[1:1000]
    level <- obj@experimentData@other$typeOfData
    metacell.mask <- match.metacell(GetMetacell(obj), 'missing', level)
    indices <- GetIndices_WholeMatrix(metacell.mask, op='>=', th=1)
    obj <- MetaCellFiltering(obj, indices, cmd='delete')
    anova_tests <- t(apply(Biobase::exprs(obj$new),1, classic1wayAnova,
    conditions=as.factor(Biobase::pData(obj$new)$Condition)))
    names(anova_tests) <- rownames(Biobase::exprs(obj$new))
    pht <- postHocTest(aov_fits = anova_tests)

[Package DAPAR version 1.24.8 Index]