Given a features x cells matrix, perform one-vs-all differential tests to find markers.

## Arguments

- mat
IterableMatrix object of dimensions features x cells

- groups
Character/factor vector of cell groups/clusters. Length #cells

- method
Test method to use. Current options are:

`wilcoxon`

: Wilconxon rank-sum test a.k.a Mann-Whitney U test

## Value

tibble with the following columns:

**foreground**: Group ID used for the foreground**background**: Group ID used for the background (or NA if comparing to rest of cells)**feature**: ID of the feature**p_val_raw**: Unadjusted p-value for differential test**foreground_mean**: Average value in the foreground group**background_mean**: Average value in the background group

## Details

Tips for using the values from this function:

Use

`dplyr::mutate()`

to add columns for e.g. adjusted p-value and log fold change.Use

`dplyr::filter()`

to get only differential genes above some given thresholdTo get adjusted p-values, use R

`p.adjust()`

, recommended method is "BH"To get log2 fold change: if your input matrix was already log-transformed, calculate

`(foreground_mean - background_mean)/log(2)`

. If your input matrix was not log-transformed, calculate`log2(forground_mean/background_mean)`