0.3.0 Codex 4-Tile 5K-Cell Example¶

In [1]:

import pandas as pd
df = {}

In [2]:

from clustergrammer2 import net

import ipywidgets as widgets
import numpy as np
from bqplot import pyplot as plt
import bqplot

from copy import deepcopy
from glob import glob
from scipy.spatial.distance import pdist, squareform
from scipy.spatial import Voronoi

import warnings
warnings.filterwarnings('ignore')

>> clustergrammer2 backend version 0.5.1

Load Expression Data¶

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net.load_file('../data/codex_data/4-tile_5k-cells_expression-cat.txt')
df['tile-neighbor'] = net.export_df()
df['tile-neighbor'].shape

Out[3]:

(29, 5018)

Load Location Data¶

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net.load_file('../data/codex_data/4-tile_5k-cells_location-cat.txt')
df['tile-loc'] = net.export_df()
df['tile-loc'].shape

Out[4]:

(5146, 2)

In [5]:

cat_colors = net.load_json_to_dict('../data/codex_data/cat_colors.json')

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net.load_df(df['tile-neighbor'])
net.set_cat_colors(axis='col', cat_colors=cat_colors, cat_index=1, cat_title='Cell Type')
net.set_cat_colors(axis='col', cat_colors=cat_colors, cat_index=2, cat_title='Neighbor')

Expression Levels of All Cells¶

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In [7]:

def cat_highlight(inst_value):

    
    if inst_value != 'reset_cats':
        
        inst_cat = inst_value.split(': ')[1]

        list_opacities = []

        for inst_label in region_labels:
            inst_opacity = 0.25
            if inst_label == inst_cat:
                inst_opacity = 1
            list_opacities.append(inst_opacity)

    else:
        list_opacities = [1 for x in region_labels]
        patch.opacities = list_opacities

    patch.opacities = list_opacities

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def on_value_change(change):
    
    if change['new'] == 'null':
        cat_highlight('reset_cats')            
    else: 
        # mousing over category
        if 'cat-' in change['new']:
            inst_cat = change['new'].split(' -> ')[1]
            cat_highlight(inst_cat)       
            
        # mousing over marker
        else:
            inst_marker = change['new'].split(' -> ')[1]
            ser_opacity = df['tile-neighbor'].loc[inst_marker]
            list_opacities = [float(x/ser_opacity.max()) for x in list(ser_opacity.get_values())]
            patch.opacities = list_opacities

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net.load_df(df['tile-neighbor'])
net.normalize(axis='row', norm_type='zscore')
net.clip(-5,5)
net.widget()
net.widget_instance.observe(on_value_change, names='value')
# tmp = net.widget()

Voronoi Plot¶

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vor = Voronoi(df['tile-loc'])

point_list = df['tile-loc'].index.tolist()
point_names = [x[0] for x in point_list]
cat_names   = [x[1].split(': ')[1] for x in point_list]

patch_data = {}
patch_data['x'] = []
patch_data['y'] = []
patch_data['colors'] = []
region_labels = []

region_point_dict = {}
for point_index in range(vor.point_region.shape[0]):
    region_index = vor.point_region[point_index]
    region_point_dict[region_index] = point_index

for region_index in range(len(vor.regions)):
    
    inst_region = vor.regions[region_index]

    if -1 not in inst_region and len(inst_region) > 0:

        point_index = region_point_dict[region_index]
        point_cat = cat_names[point_index]
        region_labels.append(point_cat)
        
        # save cat_colors
        inst_color = cat_colors[point_cat]
        patch_data['colors'].append(inst_color)
        
        x_list = []
        y_list = []
        for inst_vertex in inst_region:
            inst_pos = vor.vertices[inst_vertex]
            x_list.append(inst_pos[0])
            y_list.append(inst_pos[1])
            
        patch_data['x'].append(x_list)
        patch_data['y'].append(y_list)    

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x_dim = 1200
y_dim = 1000

def_tt = bqplot.Tooltip(fields=['name'], formats=[''])

fig = plt.figure(animation_duration=1000)
patch = plt.plot([], [], 
                 fill='inside',
                 fill_colors=patch_data['colors'],
                 stroke_width=1,
                 close_path=True,
                 labels=region_labels,
                 tooltip=def_tt,
                 axes_options={'x': {'visible': False}, 'y': {'visible': False}},
                )

scatter = plt.scatter(df['tile-loc']['X.X'], 
                      df['tile-loc']['Y.Y'],
#                       tooltip=def_tt, 
                      names=point_names,
                      display_names=False, default_size=2)


inst_width = 950
fig.layout.min_height = str(inst_width/(1.15)) + 'px'
fig.layout.min_width  = str(inst_width) + 'px'

patch.x = patch_data['x']
patch.y = patch_data['y']

plt.xlim(0, 2.0*x_dim)
plt.ylim(0, 2.0*y_dim)

# plt.show()

Out[11]:

LinearScale(max=2000.0, min=0.0)

In [12]:

from ipywidgets import HBox
HBox([net.widget_instance, fig])

HBox(children=(ExampleWidget(network='{"row_nodes": [{"name": "CD45", "ini": 29, "clust": 6, "rank": 13, "rank…

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# net.widget_instance

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# fig

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In [15]:

# def mouseover_highlight(self, target):
#     # print('cat name', target['data']['name'])
#     list_opacities = []
#     for inst_label in region_labels:
#         inst_opacity = 0.25
#         if inst_label == target['data']['name']:
#             inst_opacity = 1
#         list_opacities.append(inst_opacity)

#     self.opacities = list_opacities

In [16]:

# def reset_highlight(self, target):
# #     print('CLICKING')
#     list_opacities = [1 for x in region_labels]
#     self.opacities = list_opacities

In [17]:

# patch.on_hover(mouseover_highlight)
# patch.on_element_click(reset_highlight)

Heatmap and Voronoi¶

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