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Visualization

Two‑level API: quick one‑liners (spatial_scatter) and fine‑grained custom figure assembly (plot_* functions).

Scatter / spot plot

from metainformant.spatial.visualization import spatial_scatter
spatial_scatter(
    adata,
    color='EPCAM',                 # gene name or obs column
    cmap='viridis',
    size=adata.obs['area'] / 1000,   # spot size proportional to area
    vmin=0, vmax=10,
    title='Epithelial marker',
    show=False,
    save='epcam.pdf',
)

Domain outlines

from metainformant.spatial.visualization import domain_outlines
domain_outlines(
    adata,
    domain_column='domain',
    linewidth=0.7,
    palette='tab20',
    show=True,
)

Overlays polygon boundaries around each cluster on the tissue image (if H&E exists, pass img=adata.uns['tissue_image']).

Expression overlay

from metainformant.spatial.visualization import expression_heatmap
expression_heatmap(
    adata,
    genes=['EPCAM','VIM','CD3D'],
    ncols=3,
    standard_scale='var',
)

A multi‑panel figure with shared colour bar.

QC plots

  • Library size per spot: plot_library_size(adata)
  • Mitochondrial %: plot_mt_fraction(adata)
  • Spatial autocorrelation heatmap: plot_moran_summary(adata, genes_of_interest)

Interactive (Plotly)

from metainformant.spatial.visualization.interactive import spatial_scatter_plotly
fig = spatial_scatter_plotly(
    adata,
    color='domain',
    hover_data=['n_genes_by_counts', 'pct_counts_mt'],
)
fig.write_html('spatial_interactive.html')

Hover shows spot barcode and metadata.

Customisation

All plot_* functions accept Matplotlib Axes via ax= and return the figure and axes for further tweaking:

fig, ax = spatial_scatter(adata, color='VIM', show=False)
ax.set_title('Vimentin', fontsize=14, fontweight='bold')
fig.savefig('vimentin.pdf', bbox_inches='tight')

Common kwargs: show, save, dpi, palette, alpha, size (scalar or array).