spatialHeterogeneity.plotting.visualization module

Summary

Functions:

channel	Plot challnels.
get_cmap	Return the cmap and cmap labels for a given attribute if available, else a default
infiltration	Visualises a heatmap of the featuer intensity.
interactions	Visualise interaction results.
napari_viewer	Starts interactive Napari viewer to visualise raw images
ripleysK	Plot results
spatial	Visualisation of samples.

Reference

spatial(so, spl, attr, *, mode='scatter', node_size=4, coordinate_keys=['x', 'y'], mask_key='cellmasks', graph_key='knn', edges=False, edge_width=0.5, edge_color='black', edge_zorder=2, background_color='white', ax=None, norm=None, set_title=True, cmap=None, cmap_labels=None, cbar=True, cbar_title=True, show=True, save=None, tight_layout=True)

Visualisation of samples.

Parameters

• so – SpatialOmics instance

• spl (str) – sample to visualise

• attr (str) – feature to visualise

• mode (str) – {scatter, mask}. In scatter mode, observations are represented by their centroid, in mask mode by their actual segmentation mask

• node_size (float) – size of the node when plotting the graph representation

• coordinate_keys (list) – column names in SpatialOmics.obs[spl] that indicates the x and y coordinates

• mask_key (str) – key for the segmentation masks when in mask mode

• graph_key (str) – which graph representation to use

• edges (bool) – whether to plot the graph or not

• edge_width (float) – width of edges

• edge_color (str) – color of edges as string

• edge_zorder (int) – z-order of edges

• background_color (str) – background color of plot

• ax (Optional[Axes]) – axes object in which to plot

• norm – normalisation instance to normalise the values of attr

• set_title (bool) – title of plot

• cmap – colormap to use

• cmap_labels (Optional[list]) – colormap labels to use

• cbar (bool) – whether to plot a colorbar or not

• cbar_title (bool) – whether to plot the attr name as title of the colorbar

• show (bool) – whether to show the plot or not

• save (Optional[str]) – path to the file in which the plot is saved

• tight_layout (bool) – whether to apply tight_layout or not.

Returns:

napari_viewer(so, spl, attrs, censor=0.95, add_masks='cellmasks', attrs_key='target', index_key='fullstack_index')

Starts interactive Napari viewer to visualise raw images

Parameters

• so – SpatialOmics instance

• spl (str) – sample to visualise

• attrs (list) – list of attributes to add as channels to the viewer

• censor (float) – percentil to use to censore pixle values in the raw images

• add_masks – segmentation masks to add as channels to the viewer

Returns:

channel(so, spl, attrs, ax=None, colors=None, censor=None, show=True)

Plot challnels. Decreapted, will be removed.

Parameters

• so –

• spl (str) –

• attrs (str) –

• ax –

• colors –

• censor (Optional[float]) –

• show –

Returns:

interactions(so, spl, attr, mode='proportion', prediction_type='diff', graph_key='knn', linewidths=0.5, cmap=None, norm=None, ax=None, show=True)

Visualise interaction results.

Parameters

• so – SpatialOmics instance

• spl – Spl for which to compute the metric

• attr – Categorical feature in SpatialOmics.obs to use for the grouping

• mode – One of {classic, histoCAT, proportion}, see notes

• prediction_type – prediction_type: One of {observation, pvalue, diff}

• graph_key – Specifies the graph representation to use in so.G[spl]

• linewidths – Space between tiles

• cmap – colormap to use

• norm – normalisation to use

• ax – axes object to use

• show – whether to show the plot

Returns:

get_cmap(so, attr, data)

Return the cmap and cmap labels for a given attribute if available, else a default

Parameters

• so (IMCData) – so object form which to fetch the data

• spl (str) – spl for which to get data

• attr (str) – attribute for which to get the cmap and cmap labels if available

Returns

Return type

cmap and cmap labels for attribute

ripleysK(so, spl, attr, ids, *, mode='K', correction='ripley', key=None, ax=None, legend='auto')

Plot results

Parameters

• so – SpatialOmics instance

• spl (str) – Spl for which to compute the metric

• attr (str) – Categorical feature in SpatialOmics.obs to use for the grouping

• ids – The category in the categorical feature attr, for which Ripley’s K should be plotted

• mode – {K, csr-deviation}. If K, Ripley’s K is estimated, with csr-deviation the deviation from a poission process is computed.

• correction – Correction method to use to correct for boarder effects, see [1].

• key – key to use in so.uns[‘ripleysK’] for the plot, if None it is constructed from spl,attr,ids,mode and correction

• ax – axes to use for the plot

Returns

None

infiltration(so, spl, attr='infiltration', step_size=10, interpolation='gaussian', cmap='plasma', collision_strategy='mean', ax=None, show=True)

Visualises a heatmap of the featuer intensity.

Approximates the sample with a grid representation and colors each grid square according to the value of the attribute. If multiple observations map to the same grid square a the aggregation specified in collision_strategy is employed (any value accepted by pandas aggregate function, i.e. ‘mean’, ‘max’, …)

Parameters

• so – SpatialOmics instance

• spl (str) – Spl for which to compute the metric

• attr (str) – feature in SpatialOmics.obs to plot

• step_size (int) – grid step size

• interpolation (str) – interpolation method to use between grid values, see [1]

• cmap (str) – colormap to use

• collision_strategy – aggragation strategy to use if multiple obseravtion values map to the same grid value

• ax – axes to use for the plot

• show – whether to show the plot or not. Will be set to False if axes is provided.

Returns

None

Notes

1

https://matplotlib.org/stable/gallery/images_contours_and_fields/interpolation_methods.html