ehrapy.plot.pca_loadings

ehrapy.plot.pca_loadings(edata, components=None, include_lowest=True, show=None, save=None)

Rank features according to contributions to PCs.

Parameters:

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

• components (str | Sequence[int] | None, default: None) – For example, '1,2,3' means [1, 2, 3], first, second, third principal component.

• include_lowest (bool, default: True) – Whether to show the features with both highest and lowest loadings.

• show (bool | None, default: None) – Show the plot, do not return axis.

• save (str | 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’}.

Return type:

Axes | list[Axes] | None

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.pp.pca(edata)
>>> ep.pl.pca_loadings(edata, components="1,2,3")

Preview: