annotateNhoods {miloR} | R Documentation

## Add annotations from colData to DA testing results

### Description

This function assigns a categorical label to neighbourhoods in the differential abundance results data.frame (output of `testNhoods`), based on the most frequent label among cells in each neighbourhood. This can be useful to stratify DA testing results by cell types or samples. Also the fraction of cells carrying that label is stored.

### Usage

    annotateNhoods(x, da.res, coldata_col)

### Arguments

`x` | A `Milo` object containing single-cell gene expression and neighbourhoods.
`da.res` | A `data.frame` containing DA results, as expected from running `testNhoods`.
`coldata_col` | A character scalar determining which column of `colData(x)` stores the annotation to be added to the neighbourhoods

### Details

For each neighbourhood, this calculates the most frequent value of `colData(x)[coldata_col]` among cells in the neighbourhood and assigns that value as annotation for the neighbourhood, adding a column in the `da.res` data.frame. In addition, a `coldata_col_fraction` column will be added, storing the fraction of cells carrying the assigned label. While in practice neighbourhoods are often homogeneous, one might choose to remove an annotation label when the fraction of cells with the label is too low (e.g. below 0.6).

### Value

A `data.frame` of model results (as `da.res` input) with two new columns: (1) `coldata_col` storing the assigned label for each neighbourhood; (2) `coldata_col_fraction` storing the fraction of cells in the neighbourhood with the assigned label.

### Author(s)

Emma Dann

### Examples

    NULL

[Package miloR version 1.2.0 Index]