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.
annotateNhoods(x, da.res, coldata_col)
A character scalar determining which column of
For each neighbourhood, this calculates the most frequent value of
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).
data.frame of model results (as
da.res input) with two new columns: (1)
the assigned label for each neighbourhood; (2)
coldata_col_fraction storing the fraction of cells in the neighbourhood with
the assigned label.
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