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plot_connected_component.py
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plot_connected_component.py
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import numpy as np
import csv
import pickle
from scipy import sparse
import scipy.io as sio
import scanpy as sc
import matplotlib
matplotlib.use('Agg')
#matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap, to_hex, rgb2hex
from typing import List
from scipy.sparse import csr_matrix
from scipy.sparse.csgraph import connected_components
import argparse
parser = argparse.ArgumentParser()
parser.add_argument( '--embedding_data_path', type=str, default='new_alignment/Embedding_data_ccc_rgcn/' , help='The path to attention') #'/cluster/projects/schwartzgroup/fatema/pancreatic_cancer_visium/210827_A00827_0396_BHJLJTDRXY_Notta_Karen/V10M25-61_D1_PDA_64630_Pa_P_Spatial10x_new/outs/'
parser.add_argument( '--data_name', type=str, default='V10M25-61_D1_PDA_64630_Pa_P_Spatial10x_new', help='The name of dataset')
parser.add_argument( '--model_name', type=str, default='gat_r1_2attr', help='model name')
args = parser.parse_args()
def rgb_to_hex(rgb):
return '%02x%02x%02x' % rgb
def get_colour_scheme(palette_name: str, num_colours: int) -> List[str]:
"""Extend a colour scheme using colour interpolation.
Parameters
----------
palette_name: The matplotlib colour scheme name that will be extended.
num_colours: The number of colours in the output colour scheme.
Returns
-------
New colour scheme containing 'num_colours' of colours. Each colour is a hex
colour code.
"""
scheme = [rgb2hex(c) for c in plt.get_cmap(palette_name).colors]
if len(scheme) >= num_colours:
return scheme[:num_colours]
else:
cmap = LinearSegmentedColormap.from_list("cmap", scheme)
extended_scheme = cmap(np.linspace(0, 1, num_colours))
return [to_hex(c, keep_alpha=False) for c in extended_scheme]
############
pathologist_label_file='/cluster/home/t116508uhn/64630/IX_annotation_artifacts.csv' #IX_annotation_artifacts.csv' #
pathologist_label=[]
with open(pathologist_label_file) as file:
csv_file = csv.reader(file, delimiter=",")
for line in csv_file:
pathologist_label.append(line)
barcode_type=dict()
for i in range (1, len(pathologist_label)):
if pathologist_label[i][1] == 'tumor': #'Tumour':
barcode_type[pathologist_label[i][0]] = 1
elif pathologist_label[i][1] =='stroma_deserted':
barcode_type[pathologist_label[i][0]] = 0
elif pathologist_label[i][1] =='acinar_reactive':
barcode_type[pathologist_label[i][0]] = 2
else:
barcode_type[pathologist_label[i][0]] = 0
############
coordinates = np.load('/cluster/projects/schwartzgroup/fatema/CCST/generated_data_new/V10M25-61_D1_PDA_64630_Pa_P_Spatial10x_new/'+'coordinates.npy')
barcode_file='/cluster/home/t116508uhn/64630/spaceranger_output_new/unzipped/barcodes.tsv'
#coordinates = np.load('/cluster/projects/schwartzgroup/fatema/CCST/generated_data_noPCA_QuantileTransform_wighted_TDistance_2k/V10M25-61_D1_PDA_64630_Pa_P_Spatial10x/'+'coordinates.npy')
#barcode_file='/cluster/home/t116508uhn/64630/barcodes.tsv'
barcode_info=[]
#barcode_info.append("")
i=0
with open(barcode_file) as file:
tsv_file = csv.reader(file, delimiter="\t")
for line in tsv_file:
barcode_info.append([line[0], coordinates[i,0],coordinates[i,1],0])
i=i+1
#X_attention_filename = args.embedding_data_path + args.data_name + '/' + 'gat_r2_2attr_withFeature_STnCCC_97'+ '_attention.npy'
#X_attention_filename = args.embedding_data_path + args.data_name + '/' + 'gat_r1_2attr_withfeature_onlyccc_97'+ '_attention.npy'
#X_attention_filename = args.embedding_data_path + args.data_name + '/' + 'gat_r2_2attr_withFeature_97_onehop'+ '_attention.npy'
#X_attention_filename = args.embedding_data_path + args.data_name + '/' + 'gat_r1_2attr_nofeature_onlyccc_97_attention.npy'
#X_attention_filename = args.embedding_data_path + args.data_name + '/' + 'synccc_gat_r1_2attr_withFeature_70_reg1_attention.npy'
#X_attention_filename = args.embedding_data_path + args.data_name + '/' + 'synccc_gat_r1_2attr_withFeature_STnCCC_70_reg1_attention.npy'
#X_attention_filename = args.embedding_data_path + args.data_name + '/' + 'synccc_gat_r1_2attr_noFeature_STnCCC_70_reg1_attention.npy'
#X_attention_filename = args.embedding_data_path + args.data_name + '/' + 'synccc_gat_r1_2attr_noFeature_70_reg1_attention.npy'
#X_attention_filename = args.embedding_data_path + args.data_name + '/' + 'synccc_gat_r1_2attr_withQuantFeature_70_reg1_attention.npy'
#X_attention_filename = args.embedding_data_path + args.data_name + '/' + 'synccc_gat_r1_2attr_withQuantFeature_STnCCC_70_reg1_attention.npy'
X_attention_filename = args.embedding_data_path + args.data_name + '/' + 'totalsynccc_gat_r1_2attr_noFeature_STnCCC_region1_attention.npy'
X_attention_bundle = np.load(X_attention_filename, allow_pickle=True)
######
with gzip.open("/cluster/projects/schwartzgroup/fatema/find_ccc/" + 'scRNAseq_spatial_location_synthetic_2', 'rb') as fp:
temp_x, temp_y = pickle.load(fp)
coordinates = np.zeros((temp_x.shape[0],2))
for i in range (0, coordinates.shape[0]):
coordinates[i][0] = temp_x[i]
coordinates[i][1] = temp_y[i]
######
barcode_info = []
for i in range (0, coordinates.shape[0]):
barcode_info.append([[],coordinates[i][0],coordinates[i][1],0])
################################################################
attention_scores = np.zeros((len(barcode_info),len(barcode_info)))
distribution = []
for index in range (0, X_attention_bundle[0].shape[1]):
i = X_attention_bundle[0][0][index]
j = X_attention_bundle[0][1][index]
attention_scores[i][j] = X_attention_bundle[1][index][0]
distribution.append(attention_scores[i][j])
'''for i in range (0, len(barcode_info)):
if attention_scores[i][192]!=0:
print('%d is %g'%(i, attention_scores[i][192]))'''
percentile_value = 50
threshold = np.percentile(sorted(distribution), percentile_value)
connecting_edges = np.zeros((len(barcode_info),len(barcode_info)))
for j in range (0, attention_scores.shape[1]):
#threshold = np.percentile(sorted(attention_scores[:,j]), 97) #
for i in range (0, attention_scores.shape[0]):
if attention_scores[i][j] > threshold: #np.percentile(sorted(attention_scores[:,i]), 50): #np.percentile(sorted(distribution), 50):
connecting_edges[i][j] = 1
############
graph = csr_matrix(connecting_edges)
n_components, labels = connected_components(csgraph=graph,directed=True, connection = 'weak', return_labels=True) #
print('number of component %d'%n_components)
count_points_component = np.zeros((n_components))
for i in range (0, len(labels)):
count_points_component[labels[i]] = count_points_component[labels[i]] + 1
print(count_points_component)
id_label = 0
index_dict = dict()
for i in range (0, count_points_component.shape[0]):
if count_points_component[i]>1:
id_label = id_label+1
index_dict[i] = id_label
print(id_label)
######################################
'''coordinates = np.load('/cluster/projects/schwartzgroup/fatema/CCST/generated_data_new/V10M25-61_D1_PDA_64630_Pa_P_Spatial10x_new/'+'coordinates.npy')
barcode_file='/cluster/home/t116508uhn/64630/spaceranger_output_new/unzipped/barcodes.tsv'
barcode_info=[]
i=0
with open(barcode_file) as file:
tsv_file = csv.reader(file, delimiter="\t")
for line in tsv_file:
barcode_info.append([line[0], coordinates[i,0],coordinates[i,1],0])
i=i+1'''
####################################
for i in range (0, len(barcode_info)):
# if barcode_info[i][0] in barcode_label:
if count_points_component[labels[i]] > 1:
barcode_info[i][3] = index_dict[labels[i]]
########
number = 20
cmap = plt.get_cmap('tab20')
colors = [cmap(i) for i in np.linspace(0, 1, number)]
number = 20
cmap = plt.get_cmap('tab20b')
colors_2 = [cmap(i) for i in np.linspace(0, 1, number)]
colors=colors+colors_2
number = 8
cmap = plt.get_cmap('Set2')
colors_2 = [cmap(i) for i in np.linspace(0, 1, number)]
colors=colors+colors_2
number = 12
cmap = plt.get_cmap('Set3')
colors_2 = [cmap(i) for i in np.linspace(0, 1, number)]
colors=colors+colors_2
number = 20
cmap = plt.get_cmap('tab20c')
colors_2 = [cmap(i) for i in np.linspace(0, 1, number)]
colors=colors+colors_2
cell_count_cluster=np.zeros((labels.shape[0]))
filltype='none'
for j in range (0, n_components):
label_i = j
x_index=[]
y_index=[]
marker_size = []
#fillstyles_type = []
for i in range (0, len(barcode_info)):
if barcode_info[i][3] == label_i:
x_index.append(barcode_info[i][1])
y_index.append(barcode_info[i][2])
'''if barcode_type[barcode_info[i][0]] == 0:
marker_size.append('o')
#fillstyles_type.append('full')
elif barcode_type[barcode_info[i][0]] == 1:
marker_size.append('^')
#fillstyles_type.append('full')
else:
marker_size.append('*')
#fillstyles_type.append('full') '''
###############
###############
for i in range (0, len(x_index)):
plt.scatter(x=x_index[i], y=y_index[i], label = j, color=colors[j], s=15)
#plt.scatter(x=x_index[i], y=-y_index[i], label = j, color=colors[j], marker=matplotlib.markers.MarkerStyle(marker=marker_size[i], fillstyle=filltype), s=15)
filltype = 'full'
#plt.scatter(x=np.array(x_index), y=-np.array(y_index), label = j, color=spot_color, marker=marker_size)
#plt.scatter(x=np.array(x_index), y=-np.array(y_index), label = j+10)
#plt.legend(fontsize=4,loc='upper right')
save_path = '/cluster/home/t116508uhn/64630/'
plt.savefig(save_path+'toomanycells_PCA_64embedding_pathologist_label_l1mp5_temp_plot.svg', dpi=400)
#plt.savefig(save_path+'toomanycells_PCA_64embedding_pathologist_label_l1mp5_temp_plot.svg', dpi=400)
plt.clf()
ids = []
x_index=[]
y_index=[]
colors_point = []
for i in range (0, len(barcode_info)):
ids.append(i)
x_index.append(barcode_info[i][1])
y_index.append(barcode_info[i][2])
colors_point.append(colors[barcode_info[i][3]])
max_x = np.max(x_index)
max_y = np.max(y_index)
min_x = np.min(x_index)
min_y = np.min(y_index)
for i in range (0, len(barcode_info)):
x_index[i] = ((x_index[i]-min_x)/(max_x-min_x))*499
y_index[i] = ((y_index[i]-min_y)/(max_y-min_y))*421
from pyvis.network import Network
g = Network("500px", "500px", directed=True)
for i in range (0, len(barcode_info)):
if barcode_type[barcode_info[i][0]] == 0:
marker_size = 'circle'
elif barcode_type[barcode_info[i][0]] == 1:
marker_size = 'box'
else:
marker_size = 'ellipse'
g.add_node(ids[i], x=x_index[i], y=y_index[i], label=str(ids[i]), physics=True, shape = marker_size, color=matplotlib.colors.rgb2hex(colors_point[i]))
for i in range (0, attention_scores.shape[0]):
for j in range (0, attention_scores.shape[1]):
if attention_scores[i][j] > threshold:
g.add_edge(i, j, weight=attention_scores[i][j])
g.toggle_physics(True)
g.show('mygraph.html')
cp mygraph.html /cluster/home/t116508uhn/64630/mygraph.html
##############################
g = Network(directed=True)
g.add_nodes([1,2,3], value=[10, 100, 400],x=[21.4, 54.2, 11.2],y=[100.2, 23.54, 32.1],label=['NODE 1', 'NODE 2', 'NODE 3'],color=['#00ff1e', '#162347', '#dd4b39'])
g.add_edge(1, 2, weight=.87)
g.add_edge(1, 3, weight=.01)
g.toggle_physics(True)
g.show('mygraph.html')
for i in range (0, len(colors)):
colors[i] = matplotlib.colors.to_hex([colors[i][0], colors[i][1], colors[i][2], colors[i][3]])
#####
print(count)
print(len(cluster_label))
####
index_array = dict()
for i in range (0, len(cluster_label)):
index_array[cluster_label[i]] = i
data_list=dict()
data_list['cluster_label']=[]
data_list['X']=[]
data_list['Y']=[]
for i in range (0, len(barcode_info)):
data_list['cluster_label'].append(barcode_info[i][3])
data_list['X'].append(barcode_info[i][1])
data_list['Y'].append(-barcode_info[i][2])
data_list_pd = pd.DataFrame(data_list)
#######
chart = alt.Chart(data_list_pd).mark_point(filled=True).encode(
alt.X('X', scale=alt.Scale(zero=False)),
alt.Y('Y', scale=alt.Scale(zero=False)),
#alt.Size('pop:Q'),
color=alt.Color('cluster_label:N', scale=alt.Scale(range=colors))
).configure_legend(labelFontSize=6, symbolLimit=50)
save_path = '/cluster/home/t116508uhn/64630/'
chart.save(save_path+'toomanycells_PCA_64embedding_pathologist_label_l1mp5_temp_plot.html')