AOV2way: Two-Way ANOVA

In axm323/dataVisEasy: datavisEasy: Data visualizations Using Annotations Made Easy

Two-Way ANOVA

Description

A two-way ANOVA for the samples in the supplied data matrix based on annotations stored in the params list object.

Usage

AOV2way(data.to.aov, category1, category2, pthreshold = 0.05, additional.report = "NONE")

Arguments

data.to.aov	numeric data matrix with samples/observations in the columns and genes/variables in the rows
category1	Character string pointing to a column of the annotations data frame stores in params$annotations
category2	Character string pointing to a column of the annotations data frame stores in params$annotations
pthreshold	pvalue threshold for which genes will be considered significant
additional.report	Option to include full output of aov or TukeyHSD, options are "NONE", "AOV","TUKEY", or "ALL"

Details

Runs a standard two-way ANOVA and subsequent TukeyHSD on the data provided using stored annotations as the categories for ANOVA groupings

Value

List object

AOV.Results	dataframe giving the F statistic and pvalue of each gene in the original input matrix for each category indepenently and the interaction values
Category1_Sig.Genes	character vector of the genes with pvalues below the specified pthreshold for the levels in Category1
Category2_Sig.Genes	character vector of the genes with pvalues below the specified pthreshold for the levels in Category2
Interaction_Sig.Genes	character vector of the genes with pvalues below the specified pthreshold for the interaction between Category1 and Category2
All.Sig.Genes	character vector of the all genes with pvalues below the specified pthreshold for any contrast
NonSig.Genes	character vector of the genes with pvalues that did not pass the specified pthreshold for any contrast
Category1_Tukey.pVals	dataframe containing the adjusted pvalues from TukeyHSD for each contrast in Category2 for each gene in the original input matrix
Category2_Tukey.pVals	dataframe containing the adjusted pvalues from TukeyHSD for each contrast in Category2 for each gene in the original input matrix
Interaction_Tukey.pVals	dataframe containing the adjusted pvalues from TukeyHSD for each contrast of the interaction between Category1 and Category2 for each gene in the original input matrix
Category1_Tukey.diffs	dataframe containing the difference values from TukeyHSD for each contrast in Category1 for each gene in the original input matrix
Category2_Tukey.diffs	dataframe containing the difference values from TukeyHSD for each contrast in Category2 for each gene in the original input matrix
Interaction_Tukey.diffs	dataframe containing the difference values from TukeyHSD for each contrast of the interaction between Category1 and Category2 for each gene in the original input matrix

optional items

AOV.output	list object containing the aov output for all genes in the original input matrix
Tukey.output	list object containing the TUkeyHSD output for all genes in the original input matrix

Note

The function itself determines significant and non-significant genes based on the pthreshold provided. However, the AOV.Results provdied in the output supply the F value and p value, additional information can also be retrieved by setting additional report = TRUE, user can use any of this information for custom filtering of results.

If the the two categories provided overlap in some way (usually in cases where one of the groups from each category are the same), the effects may not be estimable. In these scenarios, an error will occuring suggesting the use of AOV1way with only one of the categories originally provided.

For cases where there are no non-missing arguments for one or more of the groups in the supplied categories, a warning will be generated indicating which genes or observations this is true for. If there is only one group with no non-missing arguments, that gene or observation will be removed from the analysis (and will be indicated with a warning). Please pay attention to these warnings as they may affect how the results should be interpretted.

Author(s)

~~Alison Moss~~

Examples

##initiate parameteres and set up annotations

initiate_params()
data(RAGP_norm)
data(RAGP_annots)
set_annotations(RAGP_annots)

##AOV for Connectivity and State
aov2 <- AOV2way(RAGP_norm, "Connectivity","State", pthreshold = 0.01)
summary(aov2)
head(aov2$AOV.Results)  ##shows F vals and pVals for both categories and the interaction
head(aov2$Category1_Sig.Genes)
head(aov2$Category2_Sig.Genes)
head(aov2$Interaction_Sig.Genes)

##visualize significant genes in heatmap
int.genes <- aov2$Interaction_Sig.Genes
myHeatmapByAnnotation(RAGP_norm, int.genes, groupings = c("Connectivity","State"))

axm323/dataVisEasy documentation built on Feb. 1, 2024, 11:53 p.m.