ehrapy.plot.rank_features_groups_dotplot

ehrapy.plot.rank_features_groups_dotplot(edata, groups=None, n_features=None, groupby=None, values_to_plot=None, var_names=None, feature_symbols=None, min_logfoldchange=None, key=None, show=None, save=None, return_fig=False, **kwds)

Plot ranking of genes using dotplot plot (see dotplot()).

Parameters:

• edata (EHRData | AnnData) – Central data object.

• groups (str | Sequence[str] | None, default: None) – List of group names.

• n_features (int | None, default: None) – Number of features to show. Is ignored if feature_names is passed.

• groupby (str | None, default: None) – Which key to group the features by.

• feature_symbols (str | None, default: None) – Key for field in .var that stores feature symbols if you do not want to use .var_names displayed in the plot.

• values_to_plot (None | Literal['scores', 'logfoldchanges', 'pvals', 'pvals_adj', 'log10_pvals', 'log10_pvals_adj'], default: None) – Key to plot. One of ‘scores’, ‘logfoldchanges’, ‘pvals’, ‘pvals_adj’, ‘log10_pvals’, ‘log10_pvals_adj’.

• var_names (Sequence[str] | Mapping[str, Sequence[str]] | None, default: None) – Feature names.

• min_logfoldchange (float | None, default: None) – Minimum log fold change to consider.

• key (str | None, default: None) – The key of the calculated feature group rankings (default: ‘rank_features_groups’).

• show (bool | None, default: None) – Whether to show the plot.

• save (bool | None, default: None) – Where to save the plot.

• return_fig (bool, default: False) – Returns ehrapy.plot.StackedViolin object. Useful for fine-tuning the plot. Takes precedence over show=False.

• **kwds – Passed to dotplot().

Returns:

If return_fig is True, returns a ehrapy.plot.StackedViolin object, else if show is false, return axes dict

Example

>>> import ehrdata as ed
>>> import ehrapy as ep
>>> edata = ed.dt.mimic_2()
>>> ep.pp.knn_impute(edata)
>>> ep.pp.neighbors(edata)
>>> ep.tl.leiden(edata, resolution=0.5, key_added="leiden_0_5")
>>> ep.tl.rank_features_groups(edata, groupby="leiden_0_5")
>>> ep.pl.rank_features_groups_dotplot(edata, key="rank_features_groups", groupby="leiden_0_5")

Preview: