scirpy.pl.group_abundance

scirpy.pl.group_abundance(adata, groupby, target_col='has_ir', *, normalize=None, max_cols=None, sort='count', **kwargs)

Plots the number of cells per group, split up by a categorical variable.
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Plots the number of cells per group, split up by a categorical variable.

Generates a stacked bar chart with one bar per group. Stacks are colored according to the categorical variable specified in target_col.

Ignores NaN values.

Parameters

adata : dict | AnnDataUnion[dict, AnnData]

AnnData object to work on.

groupby : strstr

Group by this column from obs. For instance, “sample” or “diagnosis”.

target_col : strstr (default: 'has_ir')

Column on which to compute the abundance. Defaults to has_ir which computes the number of all cells that have a T-cell receptor.

normalize : None | str | boolUnion[None, str, bool] (default: None)

If True, compute fractions of abundances relative to the groupby column rather than reporting abosolute numbers. Alternatively, the name of a column containing a categorical variable can be provided, according to which the values will be normalized.

max_cols : int | NoneOptional[int] (default: None)

Only plot the first max_cols columns. If set to None (the default) the function will raise a ValueError if attempting to plot more than 100 columns. Set to 0 to disable.

sort : {‘count’, ‘alphabetical’} | Sequence[str]Union[Literal[‘count’, ‘alphabetical’], Sequence[str]] (default: 'count')

How to arrange the dataframe columns. Default is by the category count (“count”). Other options are “alphabetical” or to provide a list of column names. By providing an explicit list, the DataFrame can also be subsetted to specific categories. Sorting (and subsetting) occurs before max_cols is applied.

**kwargs

Additional arguments passed to scirpy.pl.base.bar().

Return type

AxesAxes

Returns

Axes object