ehrapy.plot.rank_features_groups_violin

ehrapy.plot.rank_features_groups_violin(edata, groups=None, n_features=20, feature_names=None, feature_symbols=None, key=None, split=True, scale='width', strip=True, jitter=True, size=1, ax=None, show=None, save=None)

Plot ranking of features for all tested comparisons as violin plots.

Parameters:

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

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

• n_features (int, default: 20) – Number of features to show. Is ignored if feature_names is passed.

• feature_names (Iterable[str] | None, default: None) – List of features to plot. Is only useful if interested in a custom feature list, which is not the result of rank_features_groups().

• 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.

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

• split (bool, default: True) – Whether to split the violins or not.

• scale (str, default: 'width') – See violinplot().

• strip (bool, default: True) – Show a strip plot on top of the violin plot.

• jitter (int | float | bool, default: True) – If set to 0, no points are drawn. See stripplot().

• size (int, default: 1) – Size of the jitter points.

• show (bool | None, default: None) – Whether to display the figure or return axis.

• save (bool | None, default: None) – If True or a str, save the figure. A string is appended to the default filename. Infer the filetype if ending on {‘.pdf’, ‘.png’, ‘.svg’}.

• ax (Axes | None, default: None) – A matplotlib axes object. Only works if plotting a single component.

Examples

>>> import ehrdata as ed
>>> import ehrapy as ep
>>> edata = ed.dt.mimic_2()
>>> ep.pp.knn_impute(edata)
>>> ep.pp.log_norm(edata, offset=1)
>>> ep.pp.neighbors(edata)
>>> ep.tl.leiden(edata, resolution=0.15, key_added="leiden_0_5")
>>> ep.tl.rank_features_groups(edata, groupby="leiden_0_5")
>>> ep.pl.rank_features_groups_violin(edata, key="rank_features_groups", n_features=5)

Preview: