-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathrun_cc.py
127 lines (67 loc) · 2.82 KB
/
run_cc.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
import anndata as ad
import squidpy as sq
import cellcharter as cc
import pandas as pd
import scanpy as sc
import scvi
import numpy as np
import matplotlib.pyplot as plt
from lightning.pytorch import seed_everything
import time
from Taichi.model import Taichi
import time
def cellcharter(adata_list):
adata = ad.concat(adata_list, label='slice_id')
seed_everything(12345)
scvi.settings.seed = 12345
adata.layers["counts"] = adata.X.copy()
sc.pp.normalize_total(adata, target_sum=1e6)
sc.pp.log1p(adata)
scvi.model.SCVI.setup_anndata(
adata,
layer="counts",
batch_key='slice_id',
)
model = scvi.model.SCVI(adata)
model.train(early_stopping=True, enable_progress_bar=True)
adata.obsm['X_scVI'] = model.get_latent_representation(adata).astype(np.float32)
sq.gr.spatial_neighbors(adata, library_key='slice_id', coord_type='generic', delaunay=True, spatial_key='spatial', percentile=99)\
cc.gr.aggregate_neighbors(adata, n_layers=3, use_rep='X_scVI', out_key='X_cellcharter', sample_key='slice_id')
return adata
ctrl_adata = sc.read_h5ad('merfish_control.h5ad')
full_adata = sc.read_h5ad('/home/cuiyan/mms/mms.h5ad')
res_list = []
for i in full_adata.obs['slice_id'].unique():
cond_adata = full_adata[full_adata.obs['slice_id'] == i].copy()
ctrl_adata.obs['condition'] = 0
cond_adata.obs['condition'] = 1
run_adata = cellcharter([ctrl_adata, cond_adata])
run_adata.obs['condition'] = run_adata.obs['condition'].astype('category')
start_time = time.time()
model = Taichi(run_adata, ct_obs='cell_type', slice_id='slice_id')
model.label_refinement(use_rep='X_cellcharter')
res = model.graph_diffusion()
res = res[res.obs['condition'] == 1]
end_time = time.time()
print(f'Total Running Time {end_time - start_time}')
res_list.append(res)
ad.concat(res_list, label='slice_id').write_h5ad('taichi_cc_merfish.h5ad')
adata = sc.read_h5ad('/home/cuiyan/mms/gt_starmap.h5ad')
res_list = []
for i in adata.obs['slice_id'].unique():
cond_adata = adata[adata.obs['slice_id'] == i].copy()
ctrl_adata = cond_adata[cond_adata.obs['Region'].isin([2, 3])].copy()
ctrl_adata.obs['condition'] = 0
cond_adata.obs['condition'] = 1
start_time = time.time()
run_adata = cellcharter([ctrl_adata, cond_adata])
run_adata.obs['condition'] = run_adata.obs['condition'].astype('category')
model = Taichi(run_adata, ct_obs='ct', slice_id='slice_id')
model.label_refinement(use_rep='X_cellcharter')
res = model.adata
res = model.graph_diffusion()
res = res[res.obs['condition'] == 1].copy()
end_time = time.time()
res_list.append(res)
print(f'Total Running Time {end_time - start_time}')
ad.concat(res_list, label='slice_id').write_h5ad('taichi_cc_starmap.h5ad')