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hyperopt_trvae_task_specific.py
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hyperopt_trvae_task_specific.py
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from __future__ import print_function
import argparse
import numpy as np
import scanpy as sc
from hyperas import optim
from hyperas.distributions import choice
from hyperopt import Trials, STATUS_OK, tpe
from scipy import stats
import trvae
from trvae.utils import train_test_split, remove_sparsity
def data():
DATASETS = {
"Haber": {'name': 'haber', 'need_merge': False,
'source_conditions': ['Control'],
'target_conditions': ['Hpoly.Day10', 'Hpoly.Day3', 'Salmonella'],
'transition': ('Control', 'Hpoly.Day10', 'Control_to_Hpoly.Day10'),
'condition_encoder': {'Control': 0, 'Hpoly.Day3': 1, 'Hpoly.Day10': 2, 'Salmonella': 3},
'condition_key': 'condition',
'cell_type_key': 'cell_label'},
"Kang": {'name': 'kang', 'need_merge': False,
'source_conditions': ['control'],
'target_conditions': ['stimulated'],
'transition': ('control', 'stimulated', 'control_to_stimulated'),
'condition_encoder': {'control': 0, 'stimulated': 1},
'condition_key': 'condition',
'cell_type_key': 'cell_type'},
}
data_key = "Kang"
cell_type = ["NK"]
data_dict = DATASETS[data_key]
data_name = data_dict['name']
condition_key = data_dict['condition_key']
cell_type_key = data_dict['cell_type_key']
target_conditions = data_dict['target_conditions']
condition_encoder = data_dict['condition_encoder']
adata = sc.read(f"./data/{data_name}/{data_name}_normalized.h5ad")
if adata.shape[1] > 2000:
sc.pp.highly_variable_genes(adata, n_top_genes=2000)
adata = adata[:, adata.var['highly_variable']]
train_adata, valid_adata = train_test_split(adata, 0.80)
if cell_type and target_conditions:
net_train_adata = train_adata.copy()[~((train_adata.obs[cell_type_key].isin(cell_type)) &
(train_adata.obs[condition_key].isin(target_conditions)))]
net_valid_adata = valid_adata.copy()[~((valid_adata.obs[cell_type_key].isin(cell_type)) &
(valid_adata.obs[condition_key].isin(target_conditions)))]
elif target_conditions:
net_train_adata = train_adata.copy()[~(train_adata.obs[condition_key].isin(target_conditions))]
net_valid_adata = valid_adata.copy()[~(valid_adata.obs[condition_key].isin(target_conditions))]
else:
net_train_adata = train_adata.copy()
net_valid_adata = valid_adata.copy()
source_condition, target_condition, _ = data_dict['transition']
return train_adata, net_train_adata, net_valid_adata, condition_key, cell_type_key, cell_type[0], condition_encoder, data_name, source_condition, target_condition
def create_model(train_adata,
net_train_adata, net_valid_adata,
condition_key, cell_type_key,
cell_type, condition_encoder,
data_name, source_condition, target_condition):
n_conditions = len(net_train_adata.obs[condition_key].unique().tolist())
z_dim_choices = {{choice([10, 20, 40, 50, 60, 80, 100])}}
mmd_dim_choices = {{choice([64, 128, 256])}}
alpha_choices = {{choice([0.001, 0.0001, 0.00001, 0.000001])}}
beta_choices = {{choice([1, 5, 10, 20, 40, 50, 100])}}
eta_choices = {{choice([1, 2, 5, 10, 50, 100])}}
batch_size_choices = {{choice([128, 256, 512, 1024, 1500])}}
dropout_rate_choices = {{choice([0.1, 0.2, 0.5])}}
network = trvae.archs.trVAETaskSpecific(x_dimension=net_train_adata.shape[1],
z_dimension=z_dim_choices,
n_conditions=n_conditions,
mmd_dimension=mmd_dim_choices,
alpha=alpha_choices,
beta=beta_choices,
eta=eta_choices,
kernel='multi-scale-rbf',
learning_rate=0.001,
clip_value=1e6,
loss_fn='mse',
model_path=f"./models/trVAETaskSpecific/hyperopt/{data_name}/{cell_type}/{target_condition}/",
dropout_rate=dropout_rate_choices,
output_activation="relu",
)
network.train(net_train_adata,
net_valid_adata,
condition_encoder,
condition_key,
n_epochs=10000,
batch_size=batch_size_choices,
verbose=2,
early_stop_limit=100,
lr_reducer=80,
monitor='val_loss',
shuffle=True,
save=False)
cell_type_adata = train_adata.copy()[train_adata.obs[cell_type_key] == cell_type]
sc.tl.rank_genes_groups(cell_type_adata,
key_added='up_reg_genes',
groupby=condition_key,
groups=[target_condition],
reference=source_condition,
n_genes=10)
sc.tl.rank_genes_groups(cell_type_adata,
key_added='down_reg_genes',
groupby=condition_key,
groups=[source_condition],
reference=target_condition,
n_genes=10)
up_genes = cell_type_adata.uns['up_reg_genes']['names'][target_condition].tolist()
down_genes = cell_type_adata.uns['down_reg_genes']['names'][source_condition].tolist()
top_genes = up_genes + down_genes
source_adata = cell_type_adata.copy()[cell_type_adata.obs[condition_key] == source_condition]
source_label = condition_encoder[source_condition]
target_label = condition_encoder[target_condition]
source_labels = np.zeros(source_adata.shape[0]) + source_label
target_labels = np.zeros(source_adata.shape[0]) + target_label
pred_target = network.predict(source_adata,
encoder_labels=source_labels,
decoder_labels=target_labels)
real_target = cell_type_adata.copy()[cell_type_adata.obs[condition_key] == target_condition]
real_target = remove_sparsity(real_target)
pred_target = pred_target[:, top_genes]
real_target = real_target[:, top_genes]
x_var = np.var(pred_target.X, axis=0)
y_var = np.var(real_target.X, axis=0)
m, b, r_value_var, p_value, std_err = stats.linregress(x_var, y_var)
r_value_var = r_value_var ** 2
x_mean = np.mean(pred_target.X, axis=0)
y_mean = np.mean(real_target.X, axis=0)
m, b, r_value_mean, p_value, std_err = stats.linregress(x_mean, y_mean)
r_value_mean = r_value_mean ** 2
best_mean_diff = np.abs(np.mean(x_mean - y_mean))
best_var_diff = np.abs(np.var(x_var - y_var))
objective = r_value_mean + r_value_var
print(f'Reg_mean_diff: {r_value_mean}, Reg_var_all: {r_value_var})')
print(f'Mean diff: {best_mean_diff}, Var_diff: {best_var_diff}')
print(
f'alpha = {network.alpha}, beta = {network.beta}, eta={network.eta}, z_dim = {network.z_dim}, mmd_dim = {network.mmd_dim}, batch_size = {batch_size_choices}, dropout_rate = {network.dr_rate}, lr = {network.lr}')
return {'loss': -objective, 'status': STATUS_OK}
def predict_between_conditions(network, adata, pred_adatas, source_condition, source_label, target_label, name,
condition_key='condition'):
adata_source = adata.copy()[adata.obs[condition_key] == source_condition]
if adata_source.shape[0] == 0:
adata_source = pred_adatas.copy()[pred_adatas.obs[condition_key] == source_condition]
source_labels = np.zeros(adata_source.shape[0]) + source_label
target_labels = np.zeros(adata_source.shape[0]) + target_label
pred_adata = network.predict(adata_source,
encoder_labels=source_labels,
decoder_labels=target_labels)
pred_adata.obs[condition_key] = name
pred_adata = remove_sparsity(pred_adata)
return pred_adata
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Sample a trained autoencoder.')
arguments_group = parser.add_argument_group("Parameters")
arguments_group.add_argument('-n', '--max_evals', type=int, required=True,
help='name of dataset you want to train')
args = vars(parser.parse_args())
data_key = "Kang"
cell_type = ["NK"]
best_run, best_network = optim.minimize(model=create_model,
data=data,
algo=tpe.suggest,
max_evals=args['max_evals'],
trials=Trials())
# DATASETS = {
# "Haber": {'name': 'haber', 'need_merge': False,
# 'source_conditions': ['Control'],
# 'target_conditions': ['Hpoly.Day10', 'Hpoly.Day3', 'Salmonella'],
# 'transition': [
# ('Control', 'Hpoly.Day3', 'Control_to_Hpoly.Day3'),
# ('Control', 'Hpoly.Day10', 'Control_to_Hpoly.Day10'),
# ('Control', 'Salmonella', 'Control_to_Salmonella'),
# ('Hpoly.Day3', 'Hpoly.Day10', 'Hpoly.Day3_to_Hpoly.Day10'),
# ('Control_to_Hpoly.Day3', 'Hpoly.Day10', 'Control_to_Hpoly.Day3_to_Hpoly.Day10'),
# ],
# 'condition_encoder': {'Control': 0, 'Hpoly.Day3': 1, 'Hpoly.Day10': 2, 'Salmonella': 3},
# 'conditions': ['Control', 'Hpoly.Day3', 'Hpoly.Day10', 'Salmonella'],
# 'condition': 'condition',
# 'cell_type': 'cell_label'},
# }
# data_dict = DATASETS[data_key]
# data_name = data_dict['name']
# condition_key = data_dict['condition']
# cell_type_key = data_dict['cell_type']
# source_keys = data_dict['source_conditions']
# target_keys = data_dict['target_conditions']
# label_encoder = data_dict['condition_encoder']
# conditions = data_dict.get('conditions', None)
#
# data = sc.read(f"./data/{data_name}/{data_name}.h5ad")
# if conditions:
# data = data[data.obs[condition_key].isin(conditions)]
# train_adata, valid_adata = train_test_split(data, 0.80)
#
# if cell_type:
# cell_type = cell_type[0]
#
# n_conditions = len(train_adata.obs[condition_key].unique().tolist())
#
# train_labels, _ = trvae.utils.label_encoder(train_adata, label_encoder, condition_key)
# cell_type_adata = train_adata[train_adata.obs[cell_type_key] == cell_type]
#
# perturbation_list = data_dict.get("transition", [])
# pred_adatas = None
# for source, target, name, in perturbation_list:
# print(source, target, name)
# if len(source.split("_to_")) > 1:
# source = source.split("_to_")[-1]
# source_label = label_encoder[source]
# target_label = label_encoder[target]
# pred_adata = predict_between_conditions(best_network, cell_type_adata, pred_adatas,
# source_condition=source, source_label=source_label,
# target_label=target_label, name=name,
# condition_key=condition_key)
# if pred_adatas is None:
# pred_adatas = pred_adata
# else:
# pred_adatas = pred_adatas.concatenate(pred_adata)
# path_to_save = f"./data/reconstructed/trVAEMulti/hyperopt/{data_name}/{cell_type}/"
# os.makedirs(path_to_save, exist_ok=True)
# pred_adatas.write_h5ad(filename=os.path.join(path_to_save, f"reconstructed.h5ad"))
# best_network.save_model()
print("All Done!")
print(best_run)