Mouse Brain Alpha Shape Neighborhoods¶

In [1]:

%load_ext autoreload
%autoreload 2
%env ANYWIDGET_HMR=1

%load_ext autoreload %autoreload 2 %env ANYWIDGET_HMR=1

env: ANYWIDGET_HMR=1

In [2]:

# macOS requirement
import os
os.environ['DYLD_LIBRARY_PATH'] = '/opt/homebrew/lib:' + os.environ.get('DYLD_LIBRARY_PATH', '')

# macOS requirement import os os.environ['DYLD_LIBRARY_PATH'] = '/opt/homebrew/lib:' + os.environ.get('DYLD_LIBRARY_PATH', '')

In [3]:

from shapely import Point, MultiPoint, MultiPolygon
import geopandas as gpd
import numpy as np
import pandas as pd
import geopandas as gpd
from libpysal.cg import alpha_shape
import matplotlib.pyplot as plt
import json
from ipywidgets import Widget
import celldega as dega

from shapely import Point, MultiPoint, MultiPolygon import geopandas as gpd import numpy as np import pandas as pd import geopandas as gpd from libpysal.cg import alpha_shape import matplotlib.pyplot as plt import json from ipywidgets import Widget import celldega as dega

Load Data¶

In [4]:

base_path = 'https://raw.githubusercontent.com/broadinstitute/celldega_Xenium_Prime_Mouse_Brain_Coronal_FF_outs/main/Xenium_Prime_Mouse_Brain_Coronal_FF_outs/'

base_path = 'https://raw.githubusercontent.com/broadinstitute/celldega_Xenium_Prime_Mouse_Brain_Coronal_FF_outs/main/Xenium_Prime_Mouse_Brain_Coronal_FF_outs/'

In [5]:

meta_cell_ini = pd.read_parquet(base_path + 'cell_metadata.parquet')
cluster = pd.read_parquet(base_path + 'cell_clusters/cluster.parquet')
meta_cluster = pd.read_parquet(base_path + 'cell_clusters/meta_cluster.parquet')
meta_cell = pd.concat([meta_cell_ini, cluster], axis=1)

meta_cell_ini = pd.read_parquet(base_path + 'cell_metadata.parquet') cluster = pd.read_parquet(base_path + 'cell_clusters/cluster.parquet') meta_cluster = pd.read_parquet(base_path + 'cell_clusters/meta_cluster.parquet') meta_cell = pd.concat([meta_cell_ini, cluster], axis=1)

Calculate Alpha Shape Neighborhoods¶

In [6]:

gdf_alpha = dega.nbhd.alpha_shape_cell_clusters(meta_cell, cat='cluster', alphas=[100, 150, 200, 250, 300, 350])
geojson_alpha = dega.nbhd.alpha_shape_geojson(gdf_alpha, meta_cluster, inst_alpha=250)

gdf_alpha = dega.nbhd.alpha_shape_cell_clusters(meta_cell, cat='cluster', alphas=[100, 150, 200, 250, 300, 350]) geojson_alpha = dega.nbhd.alpha_shape_geojson(gdf_alpha, meta_cluster, inst_alpha=250)

In [7]:

Widget.close_all()
base_url = base_path.rstrip('/')
landscape_ist = dega.viz.Landscape(
    technology='Xenium',
    ini_zoom = -4.5,
    ini_x=6000,
    ini_y=8000,
    base_url = base_url,
    nbhd=geojson_alpha
)

landscape_ist

Widget.close_all() base_url = base_path.rstrip('/') landscape_ist = dega.viz.Landscape( technology='Xenium', ini_zoom = -4.5, ini_x=6000, ini_y=8000, base_url = base_url, nbhd=geojson_alpha ) landscape_ist

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