scirpy.pl.group_abundance

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.
`

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 (Union[AnnData, MuData]) – AnnData object to work on.

  • groupby (str) – Group by this column from obs. For instance, “sample” or “diagnosis”.

  • target_col (str (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 (Union[str, bool, None] (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 (Optional[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 (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:

Axes

Returns:

Axes object

Contents