Note

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6.Layer & column digitization#

This notebook demonstrates how to divide a predefined spatial area into layers and columns.

Packages#

[1]:
import spateo as st
import dynamo as dyn

import numpy as np

import plotly.express as px

st.configuration.set_pub_style_mpltex()
%matplotlib inline
2022-11-13 22:52:29.181679: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
network.py (36): The next major release of pysal/spaghetti (2.0.0) will drop support for all ``libpysal.cg`` geometries. This change is a first step in refactoring ``spaghetti`` that is expected to result in dramatically reduced runtimes for network instantiation and operations. Users currently requiring network and point pattern input as ``libpysal.cg`` geometries should prepare for this simply by converting to ``shapely`` geometries.
|-----> setting visualization default mode in dynamo. Your customized matplotlib settings might be overritten.

Data source#

bin60_clustered_h5ad: https://www.dropbox.com/s/wxgkim87uhpaz1c/mousebrain_bin60_clustered.h5ad?dl=0

bin30_h5ad: https://www.dropbox.com/s/tyvhndoyj8se5xt/mousebrain_bin30.h5ad?dl=0

[2]:
# Load annotated binning data
fname_bin60 = "mousebrain_bin60_clustered.h5ad"
adata_bin60 = st.sample_data.mousebrain(fname_bin60)

# Load higher resolution binning data (optional)
fname_bin30 = "mousebrain_bin30.h5ad"
adata_bin30 = st.sample_data.mousebrain(fname_bin30)

adata_bin60, adata_bin30
[2]:
(AnnData object with n_obs × n_vars = 7765 × 21667
     obs: 'area', 'n_counts', 'Size_Factor', 'initial_cell_size', 'louvain', 'scc', 'scc_anno'
     var: 'pass_basic_filter'
     uns: '__type', 'louvain', 'louvain_colors', 'neighbors', 'pp', 'scc', 'scc_anno_colors', 'scc_colors', 'spatial', 'spatial_neighbors'
     obsm: 'X_pca', 'X_spatial', 'bbox', 'contour', 'spatial'
     layers: 'count', 'spliced', 'unspliced'
     obsp: 'connectivities', 'distances', 'spatial_connectivities', 'spatial_distances',
 AnnData object with n_obs × n_vars = 31040 × 25691
     obs: 'area'
     uns: '__type', 'pp', 'spatial'
     obsm: 'X_spatial', 'bbox', 'contour', 'spatial'
     layers: 'count', 'spliced', 'unspliced')

Data preparation#

[3]:
# Extract spatial domains from SCC clusters.
# Transfer domain annotation to higher resolution anndata object for better visualization (optional).
st.dd.set_domains(
    adata_high_res=adata_bin30,
    adata_low_res=adata_bin60,
    bin_size_high=30,
    bin_size_low=60,
    cluster_key="scc_anno",
    k_size=1.8,
    min_area=16,
)
|-----> Generate the cluster label image with `gen_cluster_image`.
|-----> Set up the color for the clusters with the tab20 colormap.
|-----> Saving integer labels for clusters into adata.obs['cluster_img_label'].
|-----> Prepare a mask image and assign each pixel to the corresponding cluster id.
|-----> Iterate through each cluster and identify contours with `extract_cluster_contours`.
|-----> Get selected areas with label id(s): 13.
|-----> Close morphology of the area formed by cluster 13.
|-----> Remove small region(s).
|-----> Extract contours.
|-----> Get selected areas with label id(s): 7.
|-----> Close morphology of the area formed by cluster 7.
|-----> Remove small region(s).
|-----> Extract contours.
|-----> Get selected areas with label id(s): 5.
|-----> Close morphology of the area formed by cluster 5.
|-----> Remove small region(s).
|-----> Extract contours.
|-----> Get selected areas with label id(s): 1.
|-----> Close morphology of the area formed by cluster 1.
|-----> Remove small region(s).
|-----> Extract contours.
|-----> Get selected areas with label id(s): 12.
|-----> Close morphology of the area formed by cluster 12.
|-----> Remove small region(s).
|-----> Extract contours.
|-----> Get selected areas with label id(s): 9.
|-----> Close morphology of the area formed by cluster 9.
|-----> Remove small region(s).
|-----> Extract contours.
|-----> Get selected areas with label id(s): 6.
|-----> Close morphology of the area formed by cluster 6.
|-----> Remove small region(s).
|-----> Extract contours.
|-----> Get selected areas with label id(s): 8.
|-----> Close morphology of the area formed by cluster 8.
|-----> Remove small region(s).
|-----> Extract contours.
|-----> Get selected areas with label id(s): 10.
|-----> Close morphology of the area formed by cluster 10.
|-----> Remove small region(s).
|-----> Extract contours.
|-----> Get selected areas with label id(s): 17.
|-----> Close morphology of the area formed by cluster 17.
|-----> Remove small region(s).
|-----> Extract contours.
|-----> Get selected areas with label id(s): 3.
|-----> Close morphology of the area formed by cluster 3.
|-----> Remove small region(s).
|-----> Extract contours.
|-----> Get selected areas with label id(s): 15.
|-----> Close morphology of the area formed by cluster 15.
|-----> Remove small region(s).
|-----> Extract contours.
|-----> Get selected areas with label id(s): 11.
|-----> Close morphology of the area formed by cluster 11.
|-----> Remove small region(s).
|-----> Extract contours.
|-----> Get selected areas with label id(s): 14.
|-----> Close morphology of the area formed by cluster 14.
|-----> Remove small region(s).
|-----> Extract contours.
|-----> Get selected areas with label id(s): 4.
|-----> Close morphology of the area formed by cluster 4.
|-----> Remove small region(s).
|-----> Extract contours.
|-----> Get selected areas with label id(s): 2.
|-----> Close morphology of the area formed by cluster 2.
|-----> Remove small region(s).
|-----> Extract contours.
|-----> Get selected areas with label id(s): 16.
|-----> Close morphology of the area formed by cluster 16.
|-----> Remove small region(s).
|-----> Extract contours.
|-----> Get selected areas with label id(s): 18.
|-----> Close morphology of the area formed by cluster 18.
|-----> Remove small region(s).
|-----> Extract contours.
[4]:
# Visualize transfered spatial domain
st.pl.space(
    adata_bin30,
    color=['domain_scc_anno'],
    show_legend="upper left",
    figsize=(4, 3.5),
    color_key_cmap="tab20",
)
../../../_images/tutorials_notebooks_2_cluster_digitization_6_layer_column_digitization_7_0.png

Extract region of interest#

[5]:
# Extract an area of interest
adata_bin30.obsm['spatial_bin30'] = adata_bin30.obsm['spatial']//30
cluster_label_image_lowres = st.dd.gen_cluster_image(adata_bin30, bin_size=1, spatial_key="spatial_bin30", cluster_key='domain_scc_anno', show=False)
cluster_label_list = np.unique(adata_bin30[adata_bin30.obs['domain_scc_anno'].isin(['Isocortex L4','Isocortex L5']), :].obs["cluster_img_label"])
contours, cluster_image_close, cluster_image_contour = st.dd.extract_cluster_contours(cluster_label_image_lowres, cluster_label_list, bin_size=1, k_size=6, show=False)

px.imshow(cluster_image_contour, width=500, height=500)
|-----> Set up the color for the clusters with the tab20 colormap.
|-----> Saving integer labels for clusters into adata.obs['cluster_img_label'].
|-----> Prepare a mask image and assign each pixel to the corresponding cluster id.
|-----> Get selected areas with label id(s): [11 12].
|-----> Close morphology of the area formed by cluster [11 12].
|-----> Remove small region(s).
|-----> Extract contours.