Spatial operations

In [1]:

import anndata
import seaborn as sns
import matplotlib.pyplot as plt

import sopa

heatmap_kwargs = {"vmax": 40, "cmap": sns.cm.rocket_r, "cbar_kws": {'label': 'Mean hop distance'}}

import anndata import seaborn as sns import matplotlib.pyplot as plt import sopa heatmap_kwargs = {"vmax": 40, "cmap": sns.cm.rocket_r, "cbar_kws": {'label': 'Mean hop distance'}}

1. Prepare your data¶

You'll need the AnnData output of Sopa. If using the SpatialData object itself, simply extract the table.

Make sure you have at least a cell-type annotation (i.e. a column in adata.obs corresponding to cell-types), and eventually a niche annotation (with algorithms such as Novae).

(Optional) Download the tutorial data¶

The .h5ad file used in this tutorial is publicly available on Zenodo here.

In [2]:

adata = anndata.read_h5ad("adata_liver_merscope.h5ad")

adata = anndata.read_h5ad("adata_liver_merscope.h5ad")

Then, compute the Delaunay graph on your data. Especially, use the radius argument to drop long edges. In this examples, edges longer than 50 microns are removed.

The later function comes from Squidpy.

In [3]:

sopa.spatial.spatial_neighbors(adata, radius=[0, 50])

sopa.spatial.spatial_neighbors(adata, radius=[0, 50])

[INFO] (sopa.spatial._build) Computing delaunay graph

2. Distances between cell categories¶

You can compute the mean hop-distance between all pairs of cell-types:

Below, 'cell_type' is the name of the column of adata.obs containing the cell-type annotation

In [4]:

cell_type_to_cell_type = sopa.spatial.mean_distance(adata, "cell_type", "cell_type")

cell_type_to_cell_type = sopa.spatial.mean_distance(adata, "cell_type", "cell_type")

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In [5]:

plt.figure(figsize=(7, 6))
sns.heatmap(cell_type_to_cell_type, **heatmap_kwargs)

plt.figure(figsize=(7, 6)) sns.heatmap(cell_type_to_cell_type, **heatmap_kwargs)

Out[5]:

<Axes: xlabel='cell_type', ylabel='cell_type'>

Similary, you can compute the mean hop-distance between all pairs of cell-types and niches:

In [6]:

cell_type_to_niche = sopa.spatial.mean_distance(adata, "cell_type", "niches")

cell_type_to_niche = sopa.spatial.mean_distance(adata, "cell_type", "niches")

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In [7]:

plt.figure(figsize=(3, 6))
sns.heatmap(cell_type_to_niche, **heatmap_kwargs)

plt.figure(figsize=(3, 6)) sns.heatmap(cell_type_to_niche, **heatmap_kwargs)

Out[7]:

<Axes: xlabel='niches', ylabel='cell_type'>

Same between niches and niches:

In [8]:

niche_to_niche = sopa.spatial.mean_distance(adata, "niches")

niche_to_niche = sopa.spatial.mean_distance(adata, "niches")

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In [9]:

plt.figure(figsize=(3, 3))
sns.heatmap(niche_to_niche, **heatmap_kwargs)

plt.figure(figsize=(3, 3)) sns.heatmap(niche_to_niche, **heatmap_kwargs)

Out[9]:

<Axes: xlabel='niches', ylabel='niches'>

3. Transform niches into shapes¶

If desired, niches can be transformed into Shapely geometries. Each occurence of a specific niche will correspond to one Polygon. This makes efficient further operations on niches, such as the one in the next section.

In [10]:

gdf = sopa.spatial.geometrize_niches(adata, "niches")
gdf

gdf = sopa.spatial.geometrize_niches(adata, "niches") gdf

Out[10]:

	geometry	niches	length	area	roundness
0	POLYGON ((11256.961 7834.639, 11257.304 7835.6...	Bile duct	371.136469	6941.104125	0.633244
3	POLYGON ((11122.354 8163.108, 11123.162 8163.7...	Bile duct	393.638585	5539.287922	0.449230
6	POLYGON ((10936.163 381.622, 10936.478 381.898...	Bile duct	1595.710237	24099.793633	0.118936
8	POLYGON ((11021.489 747.492, 11019.723 748.834...	Bile duct	561.516074	5763.341860	0.229699
17	POLYGON ((11042.181 3981.166, 11043.124 3980.8...	Bile duct	584.173216	6296.007422	0.231842
...	...	...	...	...	...
2292	POLYGON ((2805.196 4508.688, 2805.687 4509.714...	Vascular	589.849425	13760.673500	0.497012
2370	POLYGON ((459.355 560.935, 460.631 562.344, 46...	Vascular	727.372986	7949.503667	0.188815
2378	POLYGON ((192.733 4605.956, 193.558 4606.628, ...	Vascular	601.631439	8236.973094	0.285967
2388	POLYGON ((78.743 2939.361, 79.118 2940.362, 80...	Vascular	390.753989	8232.263635	0.677520
2390	POLYGON ((10.051 4012.561, 10.497 4013.717, 18...	Vascular	395.425837	9050.534716	0.727368

118 rows × 5 columns

Now, each occurence (or connected component) of each niche category is a Polygon. On this example, the Necrosis niche has 3 components, as shown below.

In [11]:

legend_kwds = {"bbox_to_anchor": (1.04, 0.5), "loc": "center left", "borderaxespad": 0, "frameon": False, "title": "Niches"}

gdf.plot(column="niches", legend=True, legend_kwds=legend_kwds)

legend_kwds = {"bbox_to_anchor": (1.04, 0.5), "loc": "center left", "borderaxespad": 0, "frameon": False, "title": "Niches"} gdf.plot(column="niches", legend=True, legend_kwds=legend_kwds)

Out[11]:

<Axes: >

4. Niches geometries¶

For each niche, we can compute geometric properties. Here, we computed some simple properties of each niche: their mean length (or perimeter), their mean area, and their mean roundness (score between 0 and 1, where high values means "circle"-like shape).

NB: Since one niche can be divided into multiple connected components (or multiple occurences), we indeed need to average the above geometric properties over all connected components of one niche category

In [12]:

df_niches_geometries = sopa.spatial.niches_geometry_stats(adata, "niches")
df_niches_geometries

df_niches_geometries = sopa.spatial.niches_geometry_stats(adata, "niches") df_niches_geometries

[INFO] (sopa.spatial.morpho) Computing pairwise distances between 118 components

Out[12]:

	n_components	length	area	roundness	min_distance_to_niche_Bile duct	min_distance_to_niche_Lymphoid structure	min_distance_to_niche_Necrosis	min_distance_to_niche_Stroma	min_distance_to_niche_Stromal border	min_distance_to_niche_Tumour	min_distance_to_niche_Tumour-myeloid	min_distance_to_niche_Vascular
niches
Bile duct	53	871.163413	1.968860e+04	0.337805	0.000000	2380.538268	1449.461569	73.698113	606.466864	503.188672	1458.109531	554.878833
Lymphoid structure	2	1036.895946	6.074089e+04	0.655267	99.283752	0.000000	1293.705232	0.000000	484.004416	288.855609	778.042413	727.563948
Necrosis	3	14679.900601	1.859571e+06	0.144873	215.188214	2613.268586	0.000000	0.000000	530.994922	24.705659	0.000000	206.323638
Stroma	1	159551.459121	2.385822e+07	0.011777	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
Stromal border	10	32107.645354	1.562288e+06	0.023618	1282.747393	4533.620464	1263.667065	356.928689	0.000000	0.000000	624.666420	365.469128
Tumour	8	22964.491358	6.363175e+06	0.178266	258.930767	2124.252221	433.590282	2.529358	40.851952	0.000000	257.084459	121.688532
Tumour-myeloid	13	5625.146904	2.741475e+05	0.116237	531.479998	3349.152789	603.320975	0.000000	332.532881	79.196333	0.000000	694.707711
Vascular	28	808.856057	2.111648e+04	0.403680	1065.886005	3766.261346	1702.920627	401.167661	477.280906	283.654705	1370.371248	0.000000

In [13]:

fig, axes = plt.subplots(1, 4, figsize=(15, 6))

for i, name in enumerate(["n_components", "length", "area", "roundness"]):
    vmax = df_niches_geometries[name].sort_values()[-2:].mean()
    sns.heatmap(df_niches_geometries[[name]], cmap="viridis", annot=True, fmt=".2f", vmax=vmax, ax=axes[i])

plt.subplots_adjust(wspace=1.5)

fig, axes = plt.subplots(1, 4, figsize=(15, 6)) for i, name in enumerate(["n_components", "length", "area", "roundness"]): vmax = df_niches_geometries[name].sort_values()[-2:].mean() sns.heatmap(df_niches_geometries[[name]], cmap="viridis", annot=True, fmt=".2f", vmax=vmax, ax=axes[i]) plt.subplots_adjust(wspace=1.5)

5. Cell-type / Niche network¶

The distances between cell-types and/or niches can be summerized into one network, and plot with the Netgraph library. It provides a quick overview of the interactions happening in the micro-environment of one slide.

To continue, you'll need to install Louvain and Netgraph:

!pip install python-louvain
!pip install netgraph

In [ ]:

import networkx as nx
from community import community_louvain
from netgraph import Graph

import networkx as nx from community import community_louvain from netgraph import Graph

In [15]:

weights, node_color, node_size, node_shape = sopa.spatial.prepare_network(adata, "cell_type", "niches")

weights, node_color, node_size, node_shape = sopa.spatial.prepare_network(adata, "cell_type", "niches")

[INFO] (sopa.spatial.distance) Computing all distances for the 4 pairs of categories
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In [16]:

g = nx.from_pandas_adjacency(weights)
node_to_community = community_louvain.best_partition(g, resolution=1.35)

g = nx.from_pandas_adjacency(weights) node_to_community = community_louvain.best_partition(g, resolution=1.35)

In [17]:

Graph(g,
      node_size=node_size,
      node_color=node_color,
      node_shape=node_shape,
      node_edge_width=0,
      node_layout='community',
      node_layout_kwargs=dict(node_to_community=node_to_community),
      node_labels=True,
      node_label_fontdict=dict(size=6, weight="bold"),
      edge_alpha=1,
      edge_width=0.5,
      edge_layout_kwargs=dict(k=2000),
      edge_layout='bundled',
)

Graph(g, node_size=node_size, node_color=node_color, node_shape=node_shape, node_edge_width=0, node_layout='community', node_layout_kwargs=dict(node_to_community=node_to_community), node_labels=True, node_label_fontdict=dict(size=6, weight="bold"), edge_alpha=1, edge_width=0.5, edge_layout_kwargs=dict(k=2000), edge_layout='bundled', )

/Users/quentinblampey/mambaforge/envs/spatial/lib/python3.10/site-packages/netgraph/_edge_layout.py:978: RuntimeWarning: invalid value encountered in divide
  displacement = compatibility * delta / distance_squared[..., None]
/Users/quentinblampey/mambaforge/envs/spatial/lib/python3.10/site-packages/netgraph/_utils.py:360: RuntimeWarning: invalid value encountered in divide
  v = v / np.linalg.norm(v, axis=-1)[:, None] # unit vector

Out[17]:

<netgraph._main.Graph at 0x2b1668fd0>