peach.pl.archetype_positions

peach.pl.archetype_positions#

peach.pl.archetype_positions(adata, *, coords_key='archetype_coordinates', title='Archetype Positions in PCA Space', figsize=(15, 6), cmap='tab10', show_distances=True, save_path=None, **kwargs)[source]#

Visualize archetype positions in PCA space with distance matrix.

Creates a two-panel visualization showing archetype positions in the first two principal components and a pairwise distance matrix heatmap.

Parameters:

  • adata (AnnData) – Annotated data object with archetype coordinates

  • coords_key (str, default: "archetype_coordinates") – Key in adata.uns containing archetype coordinates

  • title (str, default: "Archetype Positions in PCA Space") – Main figure title

  • figsize (tuple, default: (15, 6)) – Figure size as (width, height)

  • cmap (str, default: 'tab10') – Colormap for archetype points

  • show_distances (bool, default: True) – Whether to show distance matrix panel

  • save_path (str | None, default: None) – Path to save the figure

  • **kwargs – Additional arguments passed to plot_archetype_positions

Returns:

Figure with archetype position visualizations

Return type:

matplotlib.figure.Figure

Examples

>>> fig = pc.pl.archetype_positions(adata)
>>> plt.show()

>>> # Save high-resolution figure
>>> fig = pc.pl.archetype_positions(adata, title="Helsinki EOC Archetype Positions", save_path="archetypes.png")

Notes

The visualization includes: - Left panel: Archetype positions in PC1-PC2 space with convex hull - Right panel: Pairwise distance matrix with values

Requires at least 2 dimensions in archetype coordinates. For 3D visualization, use archetype_positions_3d().