outlier_analysis {blacksheepr} | R Documentation

## 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_analysis(grouptablist, fraction_table = NULL,
        fraction_samples_cutoff = 0.3,
        write_out_tables = FALSE, outfilepath = tempdir())

### Arguments

`grouptablist` | table generated by the count_outliers function. NOTE that the inputted grouptablist will be deciphered to determine its content. This means that user decides to input the outliertab or aggregate tab, and the output will analyze according to what positive and negative information is contained within the table
`fraction_table` | DEFAULT: NULL; Input a fraction table to filter to only include features that have x an outlier.
`fraction_samples_cutoff` | DEFAULT: 0.3; Input a fractional cut off for the of samples that need to have an outlier for feature to be considered. ex) 10 samples in ingroup - 3 need to have an outlier for feature to be considered significant
`write_out_tables` | DEFAULT: FALSE; utility in function to write out each of the analyses to a separate table to whereever <outfilepath> is specfied.
`outfilepath` | the full string path to where the file should output to, DEFAULT is a tempdir()

### Value

the analysis table with p.value, fdr, and raw data per comparison

### 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)

[Package blacksheepr version 1.8.0 Index]