outlier_heatmap: With the grouptablist generated by count_outliers - run...

In ruggleslab/blackSheepR: Outlier Analysis for pairwise differential comparison

With the grouptablist generated by count_outliers - run through and run a fisher exact test to get the p.value for the difference in outlier count for each feature in each of your comparisons

Description

With the grouptablist generated by count_outliers - run through and run a fisher exact test to get the p.value for the difference in outlier count for each feature in each of your comparisons

Usage

outlier_heatmap(outlier_analysis_out, analysis_num = NULL,
    counttab, metatable, fdrcutoffvalue = 0.1)

Arguments

outlier_analysis_out	the full outlier_analysis data objet
analysis_num	DEFAULT: NULL; if you only want to plot the heatmap for a particular analysis, enter number of that analysis
counttab	the raw data before outlier analysis
metatable	the complete metatable that was used to generate the comparisons, will be used for annotation of the heatmap
fdrcutoffvalue	DEFAULT: 0.1; The FDR value for significance

Value

outputs a pdf with the heatmap in the current working directory

Examples

data("sample_phosphodata")
reftable_function_out <- make_outlier_table(sample_phosphodata[1:1000,])
outliertab <- reftable_function_out$outliertab

data("sample_annotationdata")
groupings <- comparison_groupings(sample_annotationdata)

count_outliers_out <- count_outliers(groupings, outliertab,
    aggregate_features = FALSE)
grouptablist <- count_outliers_out$grouptablist
fractiontab <- count_outliers_out$fractiontab

outlier_analysis_out <- outlier_analysis(grouptablist,
    fraction_table = fractiontab)

metatable <- sample_annotationdata
counttab <- sample_phosphodata

hm1 <- outlier_heatmap(outlier_analysis_out, analysis_num = NULL,
    fractiontab, metatable, fdrcutoffvalue = 0.1)

ruggleslab/blackSheepR documentation built on Feb. 27, 2023, 10:39 p.m.