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").
postHocTest(aov_fits, post_hoc_test = "TukeyHSD")
a list containing aov fitted model objects
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.
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
classic1wayAnova. Two types of tests are currently
available: The Tukey HSD's test and the Dunnett's test. Default is Tukey's
The Tukey HSD's test compares all possible pairs of means, and is based on a
studentized range distribution. Here is used the
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.
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.
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utils::data(Exp1_R25_prot, package='DAPARdata') obj <- Exp1_R25_prot[1:1000] keepThat <- mvFilterGetIndices(obj, condition='WholeMatrix', threshold=ncol(obj)) obj <- mvFilterFromIndices(obj, keepThat) anova_tests <- t(apply(Biobase::exprs(obj),1, classic1wayAnova, conditions=as.factor(Biobase::pData(obj)$Condition))) names(anova_tests) <- rownames(Biobase::exprs(obj)) pht <- postHocTest(aov_fits = anova_tests)
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