forked from owkin/HE2RNA_code
-
Notifications
You must be signed in to change notification settings - Fork 0
/
spatialization.py
189 lines (152 loc) · 6.86 KB
/
spatialization.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
"""
HE2RNA: Extract prediction of gene expression per tile and compare to ground truth
Copyright (C) 2020 Owkin Inc.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
"""
import os
import argparse
import openslide
import openslide.deepzoom
import pickle as pkl
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import gridspec
import torch
from tqdm import tqdm
from torch import nn
from sklearn.metrics import roc_curve, roc_auc_score
from scipy.stats import pearsonr, spearmanr
def compute_heatmap(path_to_model, path_to_tile_features):
X_he = np.load(path_to_tile_features)
coords = X_he[:, :3]
all_scores = []
x = torch.Tensor(X_he[np.newaxis].transpose(1, 2, 0))
clusters = np.arange(X_he.shape[0])
# Load all models from cross_validation on TCGA
models = [torch.load(f'{path_to_model}/model_' +
str(k) + '/model.pt', map_location='cpu') for k in range(5)]
for model in tqdm(models):
all_scores.append(model.conv(x).detach().numpy())
# Average over genes and cross-val folds
tile_scores = np.mean(all_scores, axis=(0, 2))[:, 0]
return coords, tile_scores
def display_heatmap(path_to_slide, coords, tile_scores, path=None):
slide_he = openslide.OpenSlide(path_to_slide)
print(f'Dimensions of the slide: {slide_he.dimensions}')
fig, (ax1, ax2) = plt.subplots(1, 2, gridspec_kw={'wspace': 0, 'hspace': 0})
fig.set_size_inches((15, 10))
zoom_he = openslide.deepzoom.DeepZoomGenerator(slide_he, tile_size=224, overlap=0)
im = np.array(slide_he.get_thumbnail((1000, 1000)))
ax1.imshow(im)
ax1.set_xticks([])
ax1.set_yticks([])
n_tiles = zoom_he.level_tiles[int(coords[0, 0])]
grid = (np.array(im.shape[:2]) / n_tiles[::-1])
score = tile_scores
# Clip scores to increase contrast
score = np.clip(score, np.percentile(score, 10), np.percentile(score, 99))
mask = np.zeros_like(im[:, :, 0]).astype(float)
for s, coord in zip(score, coords):
x = int((coord[2] + 6))
y = int((coord[1] + 3))
mask[int(x * grid[0]): int((x + 1) * grid[0]),
int(y * grid[0]): int((y + 1) * grid[0])] = s
ims = ax2.imshow(mask, cmap='inferno')
ax2.set_xticks([])
ax2.set_yticks([])
cbar = plt.colorbar(ims, ax=ax2)
ims.set_clim(np.min(mask[mask > 0]), np.max(mask[mask > 0]))
cbar.ax.tick_params(labelsize=16)
if path is not None:
plt.savefig(path)
else:
plt.show()
plt.close()
def compute_aucs_CRC(path_to_model, path_to_tiles):
scores = []
cats = ['LYM', 'ADI', 'STR', 'NORM', 'TUM', 'DEB', 'MUS', 'MUC', 'BACK']
for cat in tqdm(cats):
all_scores = []
X_he = np.load(os.path.join(path_to_tiles, f'{cat}.npy'))
x = torch.Tensor(X_he.transpose(1, 2, 0))
clusters = np.arange(X_he.shape[0])
models = [torch.load(f'{path_to_model}/model_' + str(k) +
'/model.pt', map_location='cpu') for k in range(5)]
for model in models:
all_scores.append(model.conv(x).detach().numpy())
all_scores = np.mean(all_scores, axis=(0, 1, 2))
scores.append(all_scores)
labels = np.concatenate([np.ones_like(scores[0]), np.zeros_like(np.concatenate(scores[1:]))])
auc_lym_vs_all = roc_auc_score(labels, np.concatenate(scores))
print(f'AUC for lymphocytes vs all other classes: {auc_lym_vs_all:.4f}')
dic = {}
for i in range(1, 8):
labels = np.concatenate([np.ones_like(scores[0]), np.zeros_like(scores[i])])
auc = roc_auc_score(labels, np.concatenate([scores[0], scores[i]]))
print(f'AUC for lymphocytes vs class {cats[i]}: {auc:.4f}')
dic[f'AUC LYM vs {cats[i]}'] = auc
return auc_lym_vs_all, dic
def post_processing(seg):
seg = seg[:, :, 0]
seg = (seg > 1).astype(float)
return np.mean(np.clip(seg, 0, 1))
def compute_correlation_PESO(path_to_model, path_to_tiles, path_to_masks, corr='pearson'):
scores = []
gts = []
files = os.listdir(path_to_tiles)
ns = np.unique([file.split('_')[1] for file in files])
models = [torch.load(f'{path_to_model}/model_' + str(k) +
'/model.pt', map_location='cpu') for k in range(5)]
for n in tqdm(ns):
X_he = np.load(os.path.join(path_to_tiles, '0.50_mpp', f'pds_{n}_HE.npy'))
coords = X_he[:, :3]
mask_ = openslide.OpenSlide(os.path.join(path_to_masks, f'pds_{n}_HE_training_mask.tif'))
zoom_mask = openslide.deepzoom.DeepZoomGenerator(mask_, tile_size=224, overlap=0)
tile_scores = []
x = torch.Tensor(X_he[np.newaxis].transpose(1, 2, 0))
clusters = np.arange(X_he.shape[0])
for model in models:
tile_scores.append(model.conv(x).detach().numpy())
tile_scores = np.mean(tile_scores, axis=(0, 2, 3))
scores.append(tile_scores)
gt = []
for coord in tqdm(coords):
img_mask = np.array(
zoom_mask.get_tile(int(coord[0]), (int(coord[1]), int(coord[2]))))
ep = post_processing(img_mask)
gt.append(np.mean(ep))
gt = np.array(gt)
gts.append(gt)
gts = np.concatenate(gts)
scores = np.concatenate(scores)
if corr == 'pearson':
return pearsonr(gts, scores)
elif corr == 'spearman':
return spearmanr(gts, scores)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--experiment", help="dataset on which to carry spatialization experiment, CRC or PESO")
parser.add_argument("--path_to_model", help="path to the folder containing the models trained by cross-validation",
default='epithelium_selection')
parser.add_argument("--path_to_tiles", help="path to folder containing .npy files of tile features")
parser.add_argument("--path_to_masks", help="path to folder containing training masks from PESO")
parser.add_argument("--corr", help="type of correlation to compute, pearson or spearman", default='pearson')
args = parser.parse_args()
if args.experiment == 'CRC':
compute_aucs_CRC(args.path_to_model, args.path_to_tiles)
elif args.experiment == 'PESO':
compute_correlation_PESO(args.path_to_model, args.path_to_tiles, args.path_to_masks, args.corr)
else:
print("unrecognized experiment")
if __name__ == '__main__':
main()