-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathfunctions_find_bead_params.py
173 lines (151 loc) · 9.34 KB
/
functions_find_bead_params.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
import os.path
import numpy as np
import pandas as pd
from copy import deepcopy
from pydream.core import run_dream
from pydream.convergence import Gelman_Rubin
from classes import Embryo
from functions import define_initial_protein_concentrations, setup_embryos, run_model, check_embryos_success, define_experiment_groups, set_params_from_df
from initial_params import initial_params
def set_params_from_df_2models(df, model_with_nbhd, model_no_nbhd):
if '$b_B^A$' in df.columns:
model_with_nbhd.inhibitor_scaling = 10.0 ** df.iloc[0]['$b_B^A$']
if '$b_B^B$' in df.columns:
model_no_nbhd.inhibitor_scaling = 10.0 ** df.iloc[0]['$b_B^B$']
if '$b_V^A$' in df.columns:
model_with_nbhd.inducer_scaling = 10.0 ** df.iloc[0]['$b_V^A$']
if '$b_V^B$' in df.columns:
model_no_nbhd.inducer_scaling = 10.0 ** df.iloc[0]['$b_V^B$']
if 'threshold$^A$' in df.columns:
model_with_nbhd.threshold = df.iloc[0]['threshold$^A$']
if 'threshold$^B$' in df.columns:
model_no_nbhd.threshold = df.iloc[0]['threshold$^B$']
if 'n' in df.columns:
model_with_nbhd.nbhd_size = 2*np.floor(df.iloc[0]['n']) + 1
if 'activin_conc' in df.columns:
model_with_nbhd.bead_params['activin_2_conc'] = df.iloc[0]['activin_conc']
model_no_nbhd.bead_params['activin_2_conc'] = df.iloc[0]['activin_conc']
model_with_nbhd.bead_params['activin_10_conc'] = df.iloc[0]['activin_conc']
model_no_nbhd.bead_params['activin_10_conc'] = df.iloc[0]['activin_conc']
if 'activin_2_conc' in df.columns:
model_with_nbhd.bead_params['activin_2_conc'] = df.iloc[0]['activin_2_conc']
model_no_nbhd.bead_params['activin_2_conc'] = df.iloc[0]['activin_2_conc']
if 'activin_10_conc' in df.columns:
model_with_nbhd.bead_params['activin_10_conc'] = df.iloc[0]['activin_10_conc']
model_no_nbhd.bead_params['activin_10_conc'] = df.iloc[0]['activin_10_conc']
if 'activin_spread' in df.columns:
model_with_nbhd.bead_params['heparin_2_spread'] = df.iloc[0]['activin_spread']
model_with_nbhd.bead_params['heparin_10_spread'] = df.iloc[0]['activin_spread']
model_no_nbhd.bead_params['heparin_2_spread'] = df.iloc[0]['activin_spread']
model_no_nbhd.bead_params['heparin_10_spread'] = df.iloc[0]['activin_spread']
if 'activin_2_spread' in df.columns:
model_with_nbhd.bead_params['heparin_2_spread'] = df.iloc[0]['activin_2_spread']
model_no_nbhd.bead_params['heparin_2_spread'] = df.iloc[0]['activin_2_spread']
if 'activin_10_spread' in df.columns:
model_with_nbhd.bead_params['heparin_10_spread'] = df.iloc[0]['activin_10_spread']
model_no_nbhd.bead_params['heparin_10_spread'] = df.iloc[0]['activin_10_spread']
if 'bmp4_conc' in df.columns:
model_with_nbhd.bead_params['bmp4_6_conc'] = df.iloc[0]['bmp4_conc']
model_no_nbhd.bead_params['bmp4_6_conc'] = df.iloc[0]['bmp4_conc']
model_with_nbhd.bead_params['bmp4_12_conc'] = df.iloc[0]['bmp4_conc']
model_no_nbhd.bead_params['bmp4_12_conc'] = df.iloc[0]['bmp4_conc']
model_with_nbhd.bead_params['bmp4_25_conc'] = df.iloc[0]['bmp4_conc']
model_no_nbhd.bead_params['bmp4_25_conc'] = df.iloc[0]['bmp4_conc']
model_with_nbhd.bead_params['bmp4_50_conc'] = df.iloc[0]['bmp4_conc']
model_no_nbhd.bead_params['bmp4_50_conc'] = df.iloc[0]['bmp4_conc']
if 'bmp4_50_conc' in df.columns:
model_with_nbhd.bead_params['bmp4_50_conc'] = df.iloc[0]['bmp4_50_conc']
model_no_nbhd.bead_params['bmp4_50_conc'] = df.iloc[0]['bmp4_50_conc']
if 'bmp4_25_conc' in df.columns:
model_with_nbhd.bead_params['bmp4_25_conc'] = df.iloc[0]['bmp4_25_conc']
model_no_nbhd.bead_params['bmp4_25_conc'] = df.iloc[0]['bmp4_25_conc']
if 'bmp4_12_conc' in df.columns:
model_with_nbhd.bead_params['bmp4_12_conc'] = df.iloc[0]['bmp4_12_conc']
model_no_nbhd.bead_params['bmp4_12_conc'] = df.iloc[0]['bmp4_12_conc']
if 'bmp4_6_conc' in df.columns:
model_with_nbhd.bead_params['bmp4_6_conc'] = df.iloc[0]['bmp4_6_conc']
model_no_nbhd.bead_params['bmp4_6_conc'] = df.iloc[0]['bmp4_6_conc']
if 'bmp4_spread' in df.columns:
model_with_nbhd.bead_params['afigel_50_spread'] = df.iloc[0]['bmp4_spread']
model_with_nbhd.bead_params['afigel_25_spread'] = df.iloc[0]['bmp4_spread']
model_with_nbhd.bead_params['afigel_12_spread'] = df.iloc[0]['bmp4_spread']
model_with_nbhd.bead_params['afigel_6_spread'] = df.iloc[0]['bmp4_spread']
model_no_nbhd.bead_params['afigel_50_spread'] = df.iloc[0]['bmp4_spread']
model_no_nbhd.bead_params['afigel_25_spread'] = df.iloc[0]['bmp4_spread']
model_no_nbhd.bead_params['afigel_12_spread'] = df.iloc[0]['bmp4_spread']
model_no_nbhd.bead_params['afigel_6_spread'] = df.iloc[0]['bmp4_spread']
if 'bmp4_50_spread' in df.columns:
model_with_nbhd.bead_params['afigel_50_spread'] = df.iloc[0]['bmp4_50_spread']
model_no_nbhd.bead_params['afigel_50_spread'] = df.iloc[0]['bmp4_50_spread']
if 'bmp4_25_spread' in df.columns:
model_with_nbhd.bead_params['afigel_25_spread'] = df.iloc[0]['bmp4_25_spread']
model_no_nbhd.bead_params['afigel_25_spread'] = df.iloc[0]['bmp4_25_spread']
if 'bmp4_12_spread' in df.columns:
model_with_nbhd.bead_params['afigel_12_spread'] = df.iloc[0]['bmp4_12_spread']
model_no_nbhd.bead_params['afigel_12_spread'] = df.iloc[0]['bmp4_12_spread']
if 'bmp4_6_spread' in df.columns:
model_with_nbhd.bead_params['afigel_6_spread'] = df.iloc[0]['bmp4_6_spread']
model_no_nbhd.bead_params['afigel_6_spread'] = df.iloc[0]['bmp4_6_spread']
if 'DM_conc' in df.columns:
model_with_nbhd.bead_params['DM_conc'] = df.iloc[0]['DM_conc']
model_no_nbhd.bead_params['DM_conc'] = df.iloc[0]['DM_conc']
if 'AG1X2_spread' in df.columns:
model_with_nbhd.bead_params['AG1X2_spread'] = df.iloc[0]['AG1X2_spread']
model_no_nbhd.bead_params['AG1X2_spread'] = df.iloc[0]['AG1X2_spread']
if 'cell_pellet_spread' in df.columns:
model_with_nbhd.bead_params['cell_pellet_spread'] = df.iloc[0]['cell_pellet_spread']
model_no_nbhd.bead_params['cell_pellet_spread'] = df.iloc[0]['cell_pellet_spread']
if 'vg1_cell_conc' in df.columns:
model_with_nbhd.bead_params['vg1_cell_conc'] = df.iloc[0]['vg1_cell_conc']
model_no_nbhd.bead_params['vg1_cell_conc'] = df.iloc[0]['vg1_cell_conc']
if 'bmp4_cell_conc' in df.columns:
model_with_nbhd.bead_params['bmp4_cell_conc'] = df.iloc[0]['bmp4_cell_conc']
model_no_nbhd.bead_params['bmp4_cell_conc'] = df.iloc[0]['bmp4_cell_conc']
return model_with_nbhd, model_no_nbhd
def check_success_rate_2models(select_embryos, model_with_nbhd, model_no_nbhd, save_directory):
df = pd.read_csv(save_directory + 'dream_out.tsv', sep='\t', index_col=0, header=0)
df = df.drop(columns=['chainID'])
df = df.drop_duplicates()
df = df.sort_values(by='logp', ascending=False)
top_params_N = int(np.ceil(len(df) * 1))
top_params = df.iloc[:top_params_N,:]
colN = len(top_params.columns)
for idx, emb_idx in enumerate(select_embryos):
colN = len(top_params.columns)
top_params.insert(colN, 'A_' + str(emb_idx) , False)
for idx, emb_idx in enumerate(select_embryos):
colN = len(top_params.columns)
top_params.insert(colN, 'B_' + str(emb_idx) , False)
colN = len(top_params.columns)
top_params.insert(colN, 'A_success_proportion' , np.nan)
top_params.insert(colN + 1, 'B_success_proportion' , np.nan)
for index, row in top_params.iterrows():
embryoN = 15
row_df = row.to_frame().T
model_with_nbhd, model_no_nbhd = set_params_from_df_2models(row_df, model_with_nbhd, model_no_nbhd)
# with nbhd
embryos = [Embryo('title', initial_params['number_of_cells']) for i in range(embryoN)]
initial_concentrations = define_initial_protein_concentrations(initial_params)
embryos = setup_embryos(embryos, model_with_nbhd, initial_concentrations)
for idx, emb_idx in enumerate(select_embryos):
embryo = embryos[emb_idx]
run_model(embryo, model_with_nbhd)
embryo.find_streaks()
successN, failureN = check_embryos_success(embryos)
for idx, emb_idx in enumerate(select_embryos):
top_params.at[index, 'A_' + str(emb_idx)] = embryos[emb_idx].success
top_params.at[index,'A_success_proportion'] = successN / len(select_embryos)
# no nbhd
embryos = [Embryo('title', initial_params['number_of_cells']) for i in range(embryoN)]
initial_concentrations = define_initial_protein_concentrations(initial_params)
embryos = setup_embryos(embryos, model_no_nbhd, initial_concentrations)
for idx, emb_idx in enumerate(select_embryos):
embryo = embryos[emb_idx]
run_model(embryo, model_no_nbhd)
embryo.find_streaks()
successN, failureN = check_embryos_success(embryos)
for idx, emb_idx in enumerate(select_embryos):
top_params.at[index, 'B_' + str(emb_idx)] = embryos[emb_idx].success
top_params.at[index,'B_success_proportion'] = successN / len(select_embryos)
top_params.to_csv(save_directory + 'top_params.tsv', sep='\t')
return top_params