ehrapy.plot.rank_features_groups_heatmap

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

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

Parameters:

• edata (EHRData) – 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.

• 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.

• **kwds – Passed to heatmap().

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_heatmap(edata, key="rank_features_groups")

Preview: