def prediccion():
result = "Entre"
print(request.get_data()
) #--> este es el que trae la imagen codificada en Base64.
# Parseamos request y nos quedamos solo con los bytes de la imagen.
bloque = request.get_data()[92:-47]
# Resolvemos problemas de incompatibilidad.
missing_padding = len(bloque) % 4
if missing_padding != 0:
bloque += b'=' * (4 - missing_padding)
# Decoficamos la imagen.
imgdata = base64.b64decode(bloque)
print(imgdata)
# Guardamos la imagen.
filename = 'pruebab64.jpg'
with open(filename, 'wb') as f:
f.write(imgdata)
# Levantamos la imagen con cv2
path = "./pruebab64.jpg"
img = cv2.imread(path)
# Realizamos preprocesamiento a la imagen.
resize_img = cv2.resize(img, (28, 28))
resize_img = cv2.cvtColor(resize_img, cv2.COLOR_BGR2GRAY)
resize_img = resize_img / 255
array_img = resize_img.ravel()
# Guardamos la imagen preprocesada.
cv2.imwrite(path, resize_img)
# Envio la imagen al predictor.
Xnew = []
Xnew.append(array_img)
predictor = Predictor()
result = predictor.predecir(Xnew)
return jsonify(result)
def __init__(self):
model = load_model()
self.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
self.autoencoder = Autoencoder(model, self.device)
self.predictor = Predictor(model, self.device)
self.buffer_autoencoder_f = []
self.buffer_autoencoder_n = []
self.lim_autoencoder = 4000
self.threshold_autoencoder = 1
self.buffer_predictor_f = []
self.buffer_predictor_n = []
self.lim_predictor = 4000
self.threshold_predictor = 1
self.alpha = 0.95
def main():
device = 'cuda' if torch.cuda.is_available() else 'cpu'
#MODIFY THESE PARAMETERS
predictorFile = "checkpoints/LogP Predictor/model-200000.pt"
generatorFile = "checkpoints/SMILE Generator/model-400000.pt"
vocabulary_file = "../vocab_mapping.pkl"
NUM_SMILES_TO_GENERATE = 1000
#Load vocabulary
vocabulary = pickle.load(open(vocabulary_file, 'rb'))
vocabulary = {v: k for k, v in vocabulary.items()}
V = len(vocabulary) #Vocab size
print("Vocab loaded")
#Load predictor
p = Predictor(vocab_size=V, embedding_dim=32).load_state_dict(
torch.load(predictorFile)).to(device)
p.eval()
print("Predictor loaded")
#Load generator
g = Generator(vocab_size=V + 2, embedding_dim=32).load_state_dict(
torch.load(generatorFile)).to(device)
g.eval()
print("Generator loaded")
#Evaluate results
smiles = generateSmiles(g, NUM_SMILES_TO_GENERATE, vocabulary, 77, device)
validSmiles = checkValidity(smiles)
logP = evaluateLogP(p, validSmiles, vocabulary, device=device)
#Save results
results = {}
results['smiles'] = smiles
results['validSmiles'] = validSmiles
results['logP'] = logP
f = open("evaluation_results.pkl", "wb")
pickle.dump(results, f)
f.close()
def test_predictor(self):
tensor = torch.randn(32, 256)
model = Predictor(input_dim=256,
hidden_dim=256,
n_layers=2,
bidirection=True,
p=0.5,
layers=[
torch.nn.Linear(256, 256),
torch.nn.ReLU(),
torch.nn.Linear(256, 1)
])
out = model(tensor)
self.assertEqual(out.shape, (32, 1))
def get_auxiliary_task_predictor(opt):
supported_auxiliary_tasks = [
item[10:] for item in dir(Predictor) if 'Predictor_' in item
]
layers = []
for crit_name in opt['crit']:
if crit_name in supported_auxiliary_tasks:
predictor_name = 'Predictor_%s' % crit_name
_func = getattr(Predictor, predictor_name, None)
if _func is None:
raise ValueError(
'We can not find {} in models/Predictor.py'.format(
predictor_name))
layers.append(_func(opt, key_name=Constants.mapping[crit_name][0]))
return None if not len(layers) else Predictor.Auxiliary_Task_Predictor(
layers)
class CLeaR:
def __init__(self):
model = load_model()
self.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
self.autoencoder = Autoencoder(model, self.device)
self.predictor = Predictor(model, self.device)
self.buffer_autoencoder_f = []
self.buffer_autoencoder_n = []
self.lim_autoencoder = 4000
self.threshold_autoencoder = 1
self.buffer_predictor_f = []
self.buffer_predictor_n = []
self.lim_predictor = 4000
self.threshold_predictor = 1
self.alpha = 0.95
def train_on_task(self, dataloader, warmup=False):
for x, y in tqdm(dataloader, desc="Reading data stream", leave=None):
x = x.to(self.device)
y = [yi.to(self.device) for yi in y]
with torch.no_grad():
z, x_ = self.autoencoder(x, True)
error_autoencoder = mse(x, x_)
if error_autoencoder > self.threshold_autoencoder or warmup:
self.buffer_autoencoder_n.append(x)
else:
self.buffer_autoencoder_f.append(x)
with torch.no_grad():
y_pred = self.predictor(z)
error_predictor = minMSELoss(y_pred, y)
if error_predictor > self.threshold_predictor or warmup:
self.buffer_predictor_n.append((z, y))
else:
self.buffer_predictor_f.append((z, y))
if len(self.buffer_autoencoder_n
) >= self.lim_autoencoder and not warmup:
self.update_autoencoder()
if len(self.buffer_predictor_n
) >= self.lim_predictor and not warmup:
self.update_predictor()
self.update_autoencoder()
self.update_predictor()
def update_autoencoder(self):
self.autoencoder.update(self.buffer_autoencoder_n,
self.buffer_autoencoder_f)
with torch.no_grad():
x = torch.cat(self.buffer_autoencoder_n)
x_ = self.autoencoder(x)
error = mse(x, x_)
self.threshold_autoencoder = self.alpha * error
self.buffer_autoencoder_f = []
self.buffer_autoencoder_n = []
def update_predictor(self):
self.predictor.update(self.buffer_predictor_n, self.buffer_predictor_f)
with torch.no_grad():
z = torch.cat([zy[0] for zy in self.buffer_predictor_n])
y = [zy[1][0] for zy in self.buffer_predictor_n]
y_pred = self.predictor(z)
error = minMSELoss(y_pred, y)
self.threshold_predictor = self.alpha * error
self.buffer_predictor_f = []
self.buffer_predictor_n = []
def save_alexnet(self, path):
d1 = self.autoencoder.encoder.state_dict()
d1 = {'features.' + key: d1[key] for key in d1}
d2 = self.predictor.model.state_dict()
d = {**d1, **d2}
torch.save(d, path)
print("Saved model to {}".format(path))
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5), (0.5, 0.5)),
]),
)
train_dataloader = tutils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_data_workers,
)
# Builds the models.
num_embed_dims = 128
encoder = Encoder(num_embed_dims).apply(weights_init)
predictor = Predictor(num_embed_dims, 1).apply(weights_init)
adaptor = Predictor(num_embed_dims, 10).apply(weights_init)
# Defines the loss functions.
adapt_loss_func = nn.CrossEntropyLoss()
predict_loss_func = nn.BCELoss()
# Swaps the models, if the user specified as such.
if args.swap_predictors:
predictor, adaptor = adaptor, predictor
predict_loss_func, adapt_loss_func = adapt_loss_func, predict_loss_func
# Creates model optimizers.
encoder_optim = optim.Adam(encoder.parameters())
adaptor_optim = optim.Adam(adaptor.parameters())
predictor_optim = optim.Adam(predictor.parameters())
def run_main(args):
################################################# START SECTION OF LOADING PARAMETERS #################################################
# Read parameters
epochs = args.epochs
dim_au_out = args.bottleneck #8, 16, 32, 64, 128, 256,512
freeze = args.freeze_pretrain
source_model_path = args.source_model_path
target_model_path = args.target_model_path
log_path = args.logging_file
encoder_hdims = args.source_h_dims.split(",")
encoder_hdims = list(map(int, encoder_hdims))
pretrain = args.pretrain
prediction = args.predition
reduce_model = args.dimreduce
predict_hdims = args.p_h_dims.split(",")
predict_hdims = list(map(int, predict_hdims))
load_model = True
# Misc
now=time.strftime("%Y-%m-%d-%H-%M-%S")
# Initialize logging and std out
out_path = log_path+now+".err"
log_path = log_path+now+".log"
out=open(out_path,"w")
sys.stderr=out
#Logging infomaion
logging.basicConfig(level=logging.INFO,
filename=log_path,
filemode='a',
format=
'%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s'
)
logging.getLogger('matplotlib.font_manager').disabled = True
logging.info(args)
# Save arguments
args_df = ut.save_arguments(args,now)
################################################# END SECTION OF LOADING PARAMETERS #################################################
# Select the device of gpu
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Assuming that we are on a CUDA machine, this should print a CUDA device:
logging.info(device)
torch.cuda.set_device(device)
################################################# END SECTION OF LOADING BULK DATA #################################################
################################################# START SECTION OF MODEL CUNSTRUCTION #################################################
# Construct target encoder
if reduce_model == "AE":
encoder = AEBase(input_dim=data.shape[1],latent_dim=dim_au_out,h_dims=encoder_hdims)
loss_function_e = nn.MSELoss()
elif reduce_model == "VAE":
encoder = VAEBase(input_dim=data.shape[1],latent_dim=dim_au_out,h_dims=encoder_hdims)
# elif reduce_model == "CVAE":
# # Number of condition is equal to the number of clusters
# encoder = CVAEBase(input_dim=data.shape[1],n_conditions=len(set(data_c)),latent_dim=dim_au_out,h_dims=encoder_hdims)
if torch.cuda.is_available():
encoder.cuda()
logging.info("Target encoder structure is: ")
logging.info(encoder)
encoder.to(device)
optimizer_e = optim.Adam(encoder.parameters(), lr=1e-2)
loss_function_e = nn.MSELoss()
exp_lr_scheduler_e = lr_scheduler.ReduceLROnPlateau(optimizer_e)
# Load source model before transfer
if prediction == "regression":
dim_model_out = 1
else:
dim_model_out = 2
# Load AE model
if reduce_model == "AE":
source_model = PretrainedPredictor(input_dim=Xsource_train.shape[1],latent_dim=dim_au_out,h_dims=encoder_hdims,
hidden_dims_predictor=predict_hdims,output_dim=dim_model_out,
pretrained_weights=None,freezed=freeze)
source_model.load_state_dict(torch.load(source_model_path))
source_encoder = source_model
# Load VAE model
elif reduce_model in ["VAE","CVAE"]:
source_model = PretrainedVAEPredictor(input_dim=Xsource_train.shape[1],latent_dim=dim_au_out,h_dims=encoder_hdims,
hidden_dims_predictor=predict_hdims,output_dim=dim_model_out,
pretrained_weights=None,freezed=freeze,z_reparam=bool(args.VAErepram))
source_model.load_state_dict(torch.load(source_model_path))
source_encoder = source_model
logging.info("Load pretrained source model from: "+source_model_path)
source_encoder.to(device)
################################################# END SECTION OF MODEL CUNSTRUCTION #################################################
################################################# START SECTION OF SC MODEL PRETRAININIG #################################################
# Pretrain target encoder
# Pretain using autoencoder is pretrain is not False
if(str(pretrain)!='0'):
# Pretrained target encoder if there are not stored files in the harddisk
train_flag = True
pretrain = str(pretrain)
try:
encoder.load_state_dict(torch.load(pretrain))
logging.info("Load pretrained target encoder from "+pretrain)
train_flag = False
except:
logging.warning("Loading failed, procceed to re-train model")
logging.info("Pretrained finished")
################################################# END SECTION OF SC MODEL PRETRAININIG #################################################
################################################# START SECTION OF TRANSFER LEARNING TRAINING #################################################
# Using ADDA transfer learning
if args.transfer =='ADDA':
# Set discriminator model
discriminator = Predictor(input_dim=dim_au_out,output_dim=2)
discriminator.to(device)
loss_d = nn.CrossEntropyLoss()
optimizer_d = optim.Adam(encoder.parameters(), lr=1e-2)
exp_lr_scheduler_d = lr_scheduler.ReduceLROnPlateau(optimizer_d)
# Adversairal trainning
discriminator,encoder, report_, report2_ = t.train_ADDA_model(source_encoder,encoder,discriminator,
dataloaders_source,dataloaders_pretrain,
loss_d,loss_d,
# Should here be all optimizer d?
optimizer_d,optimizer_d,
exp_lr_scheduler_d,exp_lr_scheduler_d,
epochs,device,
target_model_path)
logging.info("Transfer ADDA finished")
# DaNN model
elif args.transfer == 'DaNN':
# Set predictor loss
loss_d = nn.CrossEntropyLoss()
optimizer_d = optim.Adam(encoder.parameters(), lr=1e-2)
exp_lr_scheduler_d = lr_scheduler.ReduceLROnPlateau(optimizer_d)
# Set DaNN model
DaNN_model = DaNN(source_model=source_encoder,target_model=encoder)
DaNN_model.to(device)
def loss(x,y,GAMMA=args.GAMMA_mmd):
result = mmd.mmd_loss(x,y,GAMMA)
return result
loss_disrtibution = loss
# Tran DaNN model
DaNN_model, report_ = t.train_DaNN_model(DaNN_model,
dataloaders_source,dataloaders_pretrain,
# Should here be all optimizer d?
optimizer_d, loss_d,
epochs,exp_lr_scheduler_d,
dist_loss=loss_disrtibution,
load=load_model,
weight = args.mmd_weight,
save_path=target_model_path+"_DaNN.pkl")
encoder = DaNN_model.target_model
source_model = DaNN_model.source_model
logging.info("Transfer DaNN finished")
def run_main(args):
################################################# START SECTION OF LOADING PARAMETERS #################################################
# Read parameters
epochs = args.epochs
dim_au_out = args.bottleneck #8, 16, 32, 64, 128, 256,512
na = args.missing_value
data_path = DATA_MAP[args.target_data]
test_size = args.test_size
select_drug = args.drug
freeze = args.freeze_pretrain
valid_size = args.valid_size
g_disperson = args.var_genes_disp
min_n_genes = args.min_n_genes
max_n_genes = args.max_n_genes
source_model_path = args.source_model_path
target_model_path = args.target_model_path
log_path = args.logging_file
batch_size = args.batch_size
encoder_hdims = args.source_h_dims.split(",")
encoder_hdims = list(map(int, encoder_hdims))
source_data_path = args.source_data
pretrain = args.pretrain
prediction = args.predition
data_name = args.target_data
label_path = args.label_path
reduce_model = args.dimreduce
predict_hdims = args.p_h_dims.split(",")
predict_hdims = list(map(int, predict_hdims))
leiden_res = args.cluster_res
load_model = bool(args.load_target_model)
# Misc
now = time.strftime("%Y-%m-%d-%H-%M-%S")
# Initialize logging and std out
out_path = log_path + now + ".err"
log_path = log_path + now + ".log"
out = open(out_path, "w")
sys.stderr = out
#Logging infomaion
logging.basicConfig(
level=logging.INFO,
filename=log_path,
filemode='a',
format=
'%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s'
)
logging.getLogger('matplotlib.font_manager').disabled = True
logging.info(args)
# Save arguments
args_df = ut.save_arguments(args, now)
################################################# END SECTION OF LOADING PARAMETERS #################################################
################################################# START SECTION OF SINGLE CELL DATA REPROCESSING #################################################
# Load data and preprocessing
adata = pp.read_sc_file(data_path)
if data_name == 'GSE117872':
adata = ut.specific_process(adata,
dataname=data_name,
select_origin=args.batch_id)
elif data_name == 'GSE122843':
adata = ut.specific_process(adata, dataname=data_name)
elif data_name == 'GSE110894':
adata = ut.specific_process(adata, dataname=data_name)
elif data_name == 'GSE112274':
adata = ut.specific_process(adata, dataname=data_name)
elif data_name == 'GSE116237':
adata = ut.specific_process(adata, dataname=data_name)
elif data_name == 'GSE108383':
adata = ut.specific_process(adata, dataname=data_name)
elif data_name == 'GSE140440':
adata = ut.specific_process(adata, dataname=data_name)
elif data_name == 'GSE129730':
adata = ut.specific_process(adata, dataname=data_name)
elif data_name == 'GSE149383':
adata = ut.specific_process(adata, dataname=data_name)
else:
adata = adata
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)
adata = pp.cal_ncount_ngenes(adata)
# Show statisctic after QX
sc.pl.violin(adata,
['n_genes_by_counts', 'total_counts', 'pct_counts_mt-'],
jitter=0.4,
multi_panel=True,
save=data_name,
show=False)
sc.pl.scatter(adata, x='total_counts', y='pct_counts_mt-', show=False)
sc.pl.scatter(adata, x='total_counts', y='n_genes_by_counts', show=False)
if args.remove_genes == 0:
r_genes = []
else:
r_genes = REMOVE_GENES
#Preprocess data by filtering
if data_name not in ['GSE112274', 'GSE140440']:
adata = pp.receipe_my(adata,
l_n_genes=min_n_genes,
r_n_genes=max_n_genes,
filter_mincells=args.min_c,
filter_mingenes=args.min_g,
normalize=True,
log=True,
remove_genes=r_genes)
else:
adata = pp.receipe_my(adata,
l_n_genes=min_n_genes,
r_n_genes=max_n_genes,
filter_mincells=args.min_c,
percent_mito=100,
filter_mingenes=args.min_g,
normalize=True,
log=True,
remove_genes=r_genes)
# Select highly variable genes
sc.pp.highly_variable_genes(adata,
min_disp=g_disperson,
max_disp=np.inf,
max_mean=6)
sc.pl.highly_variable_genes(adata, save=data_name, show=False)
adata.raw = adata
adata = adata[:, adata.var.highly_variable]
# Preprocess data if spcific process is required
data = adata.X
# PCA
# Generate neighbor graph
sc.tl.pca(adata, svd_solver='arpack')
sc.pp.neighbors(adata, n_neighbors=10)
# Generate cluster labels
sc.tl.leiden(adata, resolution=leiden_res)
sc.tl.umap(adata)
sc.pl.umap(adata,
color=['leiden'],
save=data_name + 'umap' + now,
show=False)
adata.obs['leiden_origin'] = adata.obs['leiden']
adata.obsm['X_umap_origin'] = adata.obsm['X_umap']
data_c = adata.obs['leiden'].astype("long").to_list()
################################################# END SECTION OF SINGLE CELL DATA REPROCESSING #################################################
################################################# START SECTION OF LOADING SC DATA TO THE TENSORS #################################################
#Prepare to normailize and split target data
mmscaler = preprocessing.MinMaxScaler()
try:
data = mmscaler.fit_transform(data)
except:
logging.warning("Only one class, no ROC")
# Process sparse data
data = data.todense()
data = mmscaler.fit_transform(data)
# Split data to train and valid set
# Along with the leiden conditions for CVAE propose
Xtarget_train, Xtarget_valid, Ctarget_train, Ctarget_valid = train_test_split(
data, data_c, test_size=valid_size, random_state=42)
# Select the device of gpu
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Assuming that we are on a CUDA machine, this should print a CUDA device:
logging.info(device)
try:
torch.cuda.set_device(device)
except:
logging.warning("No GPU detected, will apply cpu to process")
# Construct datasets and data loaders
Xtarget_trainTensor = torch.FloatTensor(Xtarget_train).to(device)
Xtarget_validTensor = torch.FloatTensor(Xtarget_valid).to(device)
# Use leiden label if CVAE is applied
Ctarget_trainTensor = torch.LongTensor(Ctarget_train).to(device)
Ctarget_validTensor = torch.LongTensor(Ctarget_valid).to(device)
X_allTensor = torch.FloatTensor(data).to(device)
C_allTensor = torch.LongTensor(data_c).to(device)
train_dataset = TensorDataset(Xtarget_trainTensor, Ctarget_trainTensor)
valid_dataset = TensorDataset(Xtarget_validTensor, Ctarget_validTensor)
Xtarget_trainDataLoader = DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
Xtarget_validDataLoader = DataLoader(dataset=valid_dataset,
batch_size=batch_size,
shuffle=True)
dataloaders_pretrain = {
'train': Xtarget_trainDataLoader,
'val': Xtarget_validDataLoader
}
################################################# START SECTION OF LOADING SC DATA TO THE TENSORS #################################################
################################################# START SECTION OF LOADING BULK DATA #################################################
# Read source data
data_r = pd.read_csv(source_data_path, index_col=0)
label_r = pd.read_csv(label_path, index_col=0)
label_r = label_r.fillna(na)
# Extract labels
selected_idx = label_r.loc[:, select_drug] != na
label = label_r.loc[selected_idx, select_drug]
label = label.values.reshape(-1, 1)
if prediction == "regression":
lbscaler = preprocessing.MinMaxScaler()
label = lbscaler.fit_transform(label)
dim_model_out = 1
else:
le = preprocessing.LabelEncoder()
label = le.fit_transform(label)
dim_model_out = 2
# Process source data
mmscaler = preprocessing.MinMaxScaler()
source_data = mmscaler.fit_transform(data_r)
# Split source data
Xsource_train_all, Xsource_test, Ysource_train_all, Ysource_test = train_test_split(
source_data, label, test_size=test_size, random_state=42)
Xsource_train, Xsource_valid, Ysource_train, Ysource_valid = train_test_split(
Xsource_train_all,
Ysource_train_all,
test_size=valid_size,
random_state=42)
# Transform source data
# Construct datasets and data loaders
Xsource_trainTensor = torch.FloatTensor(Xsource_train).to(device)
Xsource_validTensor = torch.FloatTensor(Xsource_valid).to(device)
if prediction == "regression":
Ysource_trainTensor = torch.FloatTensor(Ysource_train).to(device)
Ysource_validTensor = torch.FloatTensor(Ysource_valid).to(device)
else:
Ysource_trainTensor = torch.LongTensor(Ysource_train).to(device)
Ysource_validTensor = torch.LongTensor(Ysource_valid).to(device)
sourcetrain_dataset = TensorDataset(Xsource_trainTensor,
Ysource_trainTensor)
sourcevalid_dataset = TensorDataset(Xsource_validTensor,
Ysource_validTensor)
Xsource_trainDataLoader = DataLoader(dataset=sourcetrain_dataset,
batch_size=batch_size,
shuffle=True)
Xsource_validDataLoader = DataLoader(dataset=sourcevalid_dataset,
batch_size=batch_size,
shuffle=True)
dataloaders_source = {
'train': Xsource_trainDataLoader,
'val': Xsource_validDataLoader
}
################################################# END SECTION OF LOADING BULK DATA #################################################
################################################# START SECTION OF MODEL CUNSTRUCTION #################################################
# Construct target encoder
if reduce_model == "AE":
encoder = AEBase(input_dim=data.shape[1],
latent_dim=dim_au_out,
h_dims=encoder_hdims)
loss_function_e = nn.MSELoss()
elif reduce_model == "VAE":
encoder = VAEBase(input_dim=data.shape[1],
latent_dim=dim_au_out,
h_dims=encoder_hdims)
elif reduce_model == "CVAE":
# Number of condition is equal to the number of clusters
encoder = CVAEBase(input_dim=data.shape[1],
n_conditions=len(set(data_c)),
latent_dim=dim_au_out,
h_dims=encoder_hdims)
if torch.cuda.is_available():
encoder.cuda()
logging.info("Target encoder structure is: ")
logging.info(encoder)
encoder.to(device)
optimizer_e = optim.Adam(encoder.parameters(), lr=1e-2)
loss_function_e = nn.MSELoss()
exp_lr_scheduler_e = lr_scheduler.ReduceLROnPlateau(optimizer_e)
# Load source model before transfer
if prediction == "regression":
dim_model_out = 1
else:
dim_model_out = 2
# Load AE model
if reduce_model == "AE":
source_model = PretrainedPredictor(input_dim=Xsource_train.shape[1],
latent_dim=dim_au_out,
h_dims=encoder_hdims,
hidden_dims_predictor=predict_hdims,
output_dim=dim_model_out,
pretrained_weights=None,
freezed=freeze)
source_model.load_state_dict(torch.load(source_model_path))
source_encoder = source_model
# Load VAE model
elif reduce_model in ["VAE", "CVAE"]:
source_model = PretrainedVAEPredictor(
input_dim=Xsource_train.shape[1],
latent_dim=dim_au_out,
h_dims=encoder_hdims,
hidden_dims_predictor=predict_hdims,
output_dim=dim_model_out,
pretrained_weights=None,
freezed=freeze,
z_reparam=bool(args.VAErepram))
source_model.load_state_dict(torch.load(source_model_path))
source_encoder = source_model
logging.info("Load pretrained source model from: " + source_model_path)
source_encoder.to(device)
################################################# END SECTION OF MODEL CUNSTRUCTION #################################################
################################################# START SECTION OF SC MODEL PRETRAININIG #################################################
# Pretrain target encoder
# Pretain using autoencoder is pretrain is not False
if (str(pretrain) != '0'):
# Pretrained target encoder if there are not stored files in the harddisk
train_flag = True
pretrain = str(pretrain)
if (os.path.exists(pretrain) == True):
try:
encoder.load_state_dict(torch.load(pretrain))
logging.info("Load pretrained target encoder from " + pretrain)
train_flag = False
except:
logging.warning("Loading failed, procceed to re-train model")
if train_flag == True:
if reduce_model == "AE":
encoder, loss_report_en = t.train_AE_model(
net=encoder,
data_loaders=dataloaders_pretrain,
optimizer=optimizer_e,
loss_function=loss_function_e,
n_epochs=epochs,
scheduler=exp_lr_scheduler_e,
save_path=pretrain)
elif reduce_model == "VAE":
encoder, loss_report_en = t.train_VAE_model(
net=encoder,
data_loaders=dataloaders_pretrain,
optimizer=optimizer_e,
n_epochs=epochs,
scheduler=exp_lr_scheduler_e,
save_path=pretrain)
elif reduce_model == "CVAE":
encoder, loss_report_en = t.train_CVAE_model(
net=encoder,
data_loaders=dataloaders_pretrain,
optimizer=optimizer_e,
n_epochs=epochs,
scheduler=exp_lr_scheduler_e,
save_path=pretrain)
logging.info("Pretrained finished")
# Before Transfer learning, we test the performance of using no transfer performance:
# Use vae result to predict
if (args.dimreduce != "CVAE"):
embeddings_pretrain = encoder.encode(X_allTensor)
else:
embeddings_pretrain = encoder.encode(X_allTensor, C_allTensor)
pretrain_prob_prediction = source_model.predict(
embeddings_pretrain).detach().cpu().numpy()
adata.obs["sens_preds_pret"] = pretrain_prob_prediction[:, 1]
adata.obs["sens_label_pret"] = pretrain_prob_prediction.argmax(axis=1)
# # Use umap result to predict
## This section is removed because the dim problem and the performance problem
# sc.tl.pca(adata, n_comps=max(50,2*dim_au_out),svd_solver='arpack')
# sc.tl.umap(adata, n_components=dim_au_out)
# embeddings_umap = torch.FloatTensor(adata.obsm["X_umap"]).to(device)
# umap_prob_prediction = source_model.predict(embeddings_umap).detach().cpu().numpy()
# adata.obs["sens_preds_umap"] = umap_prob_prediction[:,1]
# adata.obs["sens_label_umap"] = umap_prob_prediction.argmax(axis=1)
# # Use tsne result to predict
# #sc.tl.tsne(adata, n_pcs=dim_au_out)
# X_pca = adata.obsm["X_pca"]
# # Replace tsne by pac beacause TSNE is very slow
# X_tsne = adata.obsm["X_umap"]
# #X_tsne = TSNE(n_components=dim_au_out,method='exact').fit_transform(X_pca)
# embeddings_tsne = torch.FloatTensor(X_tsne).to(device)
# tsne_prob_prediction = source_model.predict(embeddings_tsne).detach().cpu().numpy()
# adata.obs["sens_preds_tsne"] = tsne_prob_prediction[:,1]
# adata.obs["sens_label_tsne"] = tsne_prob_prediction.argmax(axis=1)
# adata.obsm["X_tsne_pret"] = X_tsne
# Add embeddings to the adata object
embeddings_pretrain = embeddings_pretrain.detach().cpu().numpy()
adata.obsm["X_pre"] = embeddings_pretrain
################################################# END SECTION OF SC MODEL PRETRAININIG #################################################
################################################# START SECTION OF TRANSFER LEARNING TRAINING #################################################
# Using ADDA transfer learning
if args.transfer == 'ADDA':
# Set discriminator model
discriminator = Predictor(input_dim=dim_au_out, output_dim=2)
discriminator.to(device)
loss_d = nn.CrossEntropyLoss()
optimizer_d = optim.Adam(encoder.parameters(), lr=1e-2)
exp_lr_scheduler_d = lr_scheduler.ReduceLROnPlateau(optimizer_d)
# Adversairal trainning
discriminator, encoder, report_, report2_ = t.train_ADDA_model(
source_encoder,
encoder,
discriminator,
dataloaders_source,
dataloaders_pretrain,
loss_d,
loss_d,
# Should here be all optimizer d?
optimizer_d,
optimizer_d,
exp_lr_scheduler_d,
exp_lr_scheduler_d,
epochs,
device,
target_model_path)
logging.info("Transfer ADDA finished")
# DaNN model
elif args.transfer == 'DaNN':
# Set predictor loss
loss_d = nn.CrossEntropyLoss()
optimizer_d = optim.Adam(encoder.parameters(), lr=1e-2)
exp_lr_scheduler_d = lr_scheduler.ReduceLROnPlateau(optimizer_d)
# Set DaNN model
DaNN_model = DaNN(source_model=source_encoder, target_model=encoder)
DaNN_model.to(device)
def loss(x, y, GAMMA=args.GAMMA_mmd):
result = mmd.mmd_loss(x, y, GAMMA)
return result
loss_disrtibution = loss
# Tran DaNN model
DaNN_model, report_ = t.train_DaNN_model(
DaNN_model,
dataloaders_source,
dataloaders_pretrain,
# Should here be all optimizer d?
optimizer_d,
loss_d,
epochs,
exp_lr_scheduler_d,
dist_loss=loss_disrtibution,
load=load_model,
weight=args.mmd_weight,
save_path=target_model_path + "_DaNN.pkl")
encoder = DaNN_model.target_model
source_model = DaNN_model.source_model
logging.info("Transfer DaNN finished")
if (load_model == False):
ut.plot_loss(report_[0], path="figures/train_loss_" + now + ".pdf")
ut.plot_loss(report_[1],
path="figures/mmd_loss_" + now + ".pdf",
set_ylim=False)
if (args.dimreduce != 'CVAE'):
# Attribute test using integrated gradient
# Generate a target model including encoder and predictor
target_model = TargetModel(source_model, encoder)
# Allow require gradients and process label
X_allTensor.requires_grad_()
# Run integrated gradient check
# Return adata and feature integrated gradient
ytarget_allPred = target_model(X_allTensor).detach().cpu().numpy()
ytarget_allPred = ytarget_allPred.argmax(axis=1)
adata, attrp1, senNeu_c0_genes, senNeu_c1_genes = ut.integrated_gradient_differential(
net=target_model,
input=X_allTensor,
clip="positive",
target=ytarget_allPred,
adata=adata,
ig_fc=1,
save_name=reduce_model + args.predictor + prediction +
select_drug + "sensNeuron" + now)
adata, attrn1, resNeu_c0_genes, resNeu_c1_genes = ut.integrated_gradient_differential(
net=target_model,
input=X_allTensor,
clip="negative",
target=ytarget_allPred,
adata=adata,
ig_fc=1,
save_name=reduce_model + args.predictor + prediction +
select_drug + "restNeuron" + now)
sc.pl.heatmap(attrp1,
senNeu_c0_genes,
groupby='sensitive',
cmap='RdBu_r',
save=data_name + args.transfer + args.dimreduce +
"_seNc0_" + now,
show=False)
sc.pl.heatmap(attrp1,
senNeu_c1_genes,
groupby='sensitive',
cmap='RdBu_r',
save=data_name + args.transfer + args.dimreduce +
"_seNc1_" + now,
show=False)
sc.pl.heatmap(attrn1,
resNeu_c0_genes,
groupby='sensitive',
cmap='RdBu_r',
save=data_name + args.transfer + args.dimreduce +
"_reNc0_" + now,
show=False)
sc.pl.heatmap(attrn1,
resNeu_c1_genes,
groupby='sensitive',
cmap='RdBu_r',
save=data_name + args.transfer + args.dimreduce +
"_reNc1_" + now,
show=False)
# CHI2 Test on predictive features
SFD = SelectFdr(chi2)
SFD.fit(adata.raw.X, ytarget_allPred)
adata.raw.var['chi2_pval'] = SFD.pvalues_
adata.raw.var['chi2_score'] = SFD.scores_
df_chi2_genes = adata.raw.var[
(SFD.pvalues_ < 0.05) & (adata.raw.var.highly_variable == True)
& (adata.raw.var.n_cells > args.min_c)]
df_chi2_genes.sort_values(by="chi2_pval",
ascending=True,
inplace=True)
df_chi2_genes.to_csv("saved/results/chi2_pval_genes" +
args.predictor + prediction + select_drug +
now + '.csv')
else:
print()
################################################# END SECTION OF TRANSER LEARNING TRAINING #################################################
################################################# START SECTION OF PREPROCESSING FEATURES #################################################
# Extract feature embeddings
# Extract prediction probabilities
if (args.dimreduce != "CVAE"):
embedding_tensors = encoder.encode(X_allTensor)
else:
embedding_tensors = encoder.encode(X_allTensor, C_allTensor)
prediction_tensors = source_model.predictor(embedding_tensors)
embeddings = embedding_tensors.detach().cpu().numpy()
predictions = prediction_tensors.detach().cpu().numpy()
# Transform predict8ion probabilities to 0-1 labels
if (prediction == "regression"):
adata.obs["sens_preds"] = predictions
else:
adata.obs["sens_preds"] = predictions[:, 1]
adata.obs["sens_label"] = predictions.argmax(axis=1)
adata.obs["sens_label"] = adata.obs["sens_label"].astype('category')
adata.obs["rest_preds"] = predictions[:, 0]
adata.write("saved/adata/before_ann" + data_name + now + ".h5ad")
################################################# END SECTION OF PREPROCESSING FEATURES #################################################
################################################# START SECTION OF ANALYSIS AND POST PROCESSING #################################################
# Pipeline of scanpy
# Add embeddings to the adata package
adata.obsm["X_Trans"] = embeddings
#sc.tl.umap(adata)
sc.pp.neighbors(adata, n_neighbors=10, use_rep="X_Trans")
# Use t-sne on transfer learning features
sc.tl.tsne(adata, use_rep="X_Trans")
# Leiden on the data
# sc.tl.leiden(adata)
# Plot tsne
sc.pl.tsne(adata, save=data_name + now, color=["leiden"], show=False)
# Differenrial expression genes
sc.tl.rank_genes_groups(adata, 'leiden', method='wilcoxon')
sc.pl.rank_genes_groups(adata,
n_genes=args.n_DE_genes,
sharey=False,
save=data_name + now,
show=False)
# Differenrial expression genes across 0-1 classes
sc.tl.rank_genes_groups(adata, 'sens_label', method='wilcoxon')
adata = ut.de_score(adata, clustername='sens_label')
# save DE genes between 0-1 class
for label in [0, 1]:
try:
df_degs = get_de_dataframe(adata, label)
df_degs.to_csv("saved/results/DEGs_class_" + str(label) +
args.predictor + prediction + select_drug + now +
'.csv')
except:
logging.warning("Only one class, no two calsses critical genes")
# Generate reports of scores
report_df = args_df
# Data specific benchmarking
sens_pb_pret = adata.obs['sens_preds_pret']
lb_pret = adata.obs['sens_label_pret']
# sens_pb_umap = adata.obs['sens_preds_umap']
# lb_umap = adata.obs['sens_label_umap']
# sens_pb_tsne = adata.obs['sens_preds_tsne']
# lb_tsne = adata.obs['sens_label_tsne']
if ('sensitive' in adata.obs.keys()):
report_df = report_df.T
Y_test = adata.obs['sensitive']
sens_pb_results = adata.obs['sens_preds']
lb_results = adata.obs['sens_label']
le_sc = LabelEncoder()
le_sc.fit(['Resistant', 'Sensitive'])
label_descrbie = le_sc.inverse_transform(Y_test)
adata.obs['sens_truth'] = label_descrbie
color_list = ["sens_truth", "sens_label", 'sens_preds']
color_score_list = [
"Sensitive_score", "Resistant_score", "1_score", "0_score"
]
sens_score = pearsonr(adata.obs["sens_preds"],
adata.obs["Sensitive_score"])[0]
resistant_score = pearsonr(adata.obs["rest_preds"],
adata.obs["Resistant_score"])[0]
report_df['prob_sens_pearson'] = sens_score
report_df['prob_rest_pearson'] = resistant_score
try:
cluster_score_sens = pearsonr(adata.obs["1_score"],
adata.obs["Sensitive_score"])[0]
report_df['sens_pearson'] = cluster_score_sens
except:
logging.warning(
"Prediction score 1 not exist, fill adata with 0 values")
adata.obs["1_score"] = np.zeros(len(adata))
try:
cluster_score_resist = pearsonr(adata.obs["0_score"],
adata.obs["Resistant_score"])[0]
report_df['rest_pearson'] = cluster_score_resist
except:
logging.warning(
"Prediction score 0 not exist, fill adata with 0 values")
adata.obs["0_score"] = np.zeros(len(adata))
#if (data_name in ['GSE110894','GSE117872']):
ap_score = average_precision_score(Y_test, sens_pb_results)
ap_pret = average_precision_score(Y_test, sens_pb_pret)
# ap_umap = average_precision_score(Y_test, sens_pb_umap)
# ap_tsne = average_precision_score(Y_test, sens_pb_tsne)
report_dict = classification_report(Y_test,
lb_results,
output_dict=True)
f1score = report_dict['weighted avg']['f1-score']
report_df['f1_score'] = f1score
classification_report_df = pd.DataFrame(report_dict).T
classification_report_df.to_csv("saved/results/clf_report_" +
reduce_model + args.predictor +
prediction + select_drug + now +
'.csv')
# report_dict_umap = classification_report(Y_test, lb_umap, output_dict=True)
# classification_report_umap_df = pd.DataFrame(report_dict_umap).T
# classification_report_umap_df.to_csv("saved/results/clf_umap_report_" + reduce_model + args.predictor+ prediction + select_drug+now + '.csv')
report_dict_pret = classification_report(Y_test,
lb_pret,
output_dict=True)
classification_report_pret_df = pd.DataFrame(report_dict_pret).T
classification_report_pret_df.to_csv("saved/results/clf_pret_report_" +
reduce_model + args.predictor +
prediction + select_drug + now +
'.csv')
# report_dict_tsne = classification_report(Y_test, lb_tsne, output_dict=True)
# classification_report_tsne_df = pd.DataFrame(report_dict_tsne).T
# classification_report_tsne_df.to_csv("saved/results/clf_tsne_report_" + reduce_model + args.predictor+ prediction + select_drug+now + '.csv')
try:
auroc_score = roc_auc_score(Y_test, sens_pb_results)
auroc_pret = average_precision_score(Y_test, sens_pb_pret)
# auroc_umap = average_precision_score(Y_test, sens_pb_umap)
# auroc_tsne = average_precision_score(Y_test, sens_pb_tsne)
except:
logging.warning("Only one class, no ROC")
auroc_pret = auroc_umap = auroc_tsne = auroc_score = 0
report_df['auroc_score'] = auroc_score
report_df['ap_score'] = ap_score
report_df['auroc_pret'] = auroc_pret
report_df['ap_pret'] = ap_pret
# report_df['auroc_umap'] = auroc_umap
# report_df['ap_umap'] = ap_umap
# report_df['auroc_tsne'] = auroc_tsne
# report_df['ap_tsne'] = ap_tsne
ap_title = "ap: " + str(Decimal(ap_score).quantize(Decimal('0.0000')))
auroc_title = "roc: " + str(
Decimal(auroc_score).quantize(Decimal('0.0000')))
title_list = ["Ground truth", "Prediction", "Probability"]
else:
color_list = ["leiden", "sens_label", 'sens_preds']
title_list = ['Cluster', "Prediction", "Probability"]
color_score_list = color_list
# Simple analysis do neighbors in adata using PCA embeddings
#sc.pp.neighbors(adata)
# Run UMAP dimension reduction
sc.pp.neighbors(adata)
sc.tl.umap(adata)
# Run leiden clustering
# sc.tl.leiden(adata,resolution=leiden_res)
# Plot uamp
# sc.pl.umap(adata,color=[color_list[0],'sens_label_umap','sens_preds_umap'],save=data_name+args.transfer+args.dimreduce+now,show=False,title=title_list)
# Plot transfer learning on umap
sc.pl.umap(adata,
color=color_list + color_score_list,
save=data_name + args.transfer + args.dimreduce + "umap_all" +
now,
show=False)
sc.settings.set_figure_params(dpi=100,
frameon=False,
figsize=(4, 3),
facecolor='white')
sc.pl.umap(adata,
color=['sensitivity', 'leiden', 'sens_label', 'sens_preds'],
title=[
'Cell sensitivity', 'Cell clusters',
'Transfer learning prediction', 'Prediction probability'
],
save=data_name + args.transfer + args.dimreduce + "umap_pred" +
now,
show=False,
ncols=4)
sc.pl.umap(adata,
color=color_score_list,
title=[
'Sensitive gene score', 'Resistant gene score',
'Sensitive gene score (prediction)',
'Resistant gene score (prediction)'
],
save=data_name + args.transfer + args.dimreduce +
"umap_scores" + now,
show=False,
ncols=2)
# sc.pl.umap(adata,color=['Sample name'],
# save=data_name+args.transfer+args.dimreduce+"umap_sm"+now,show=False,ncols=4)
try:
sc.pl.umap(adata,
color=adata.var.sort_values(
"integrated_gradient_sens_class0").head().index,
save=data_name + args.transfer + args.dimreduce +
"_cgenes0_" + now,
show=False)
sc.pl.umap(adata,
color=adata.var.sort_values(
"integrated_gradient_sens_class1").head().index,
save=data_name + args.transfer + args.dimreduce +
"_cgenes1_" + now,
show=False)
# c0_genes = df_11_genes.loc[df_11_genes.pval<0.05].head().index
# c1_genes = df_00_genes.loc[df_00_genes.pval<0.05].head().index
# sc.pl.umap(adata,color=c0_genes,neighbors_key="Trans",save=data_name+args.transfer+args.dimreduce+"_cgenes0_TL"+now,show=False)
# sc.pl.umap(adata,color=c1_genes,neighbors_key="Trans",save=data_name+args.transfer+args.dimreduce+"_cgenes1_TL"+now,show=False)
except:
logging.warning("IG results not avaliable")
# Run embeddings using transfered embeddings
sc.pp.neighbors(adata, use_rep='X_Trans', key_added="Trans")
sc.tl.umap(adata, neighbors_key="Trans")
sc.tl.leiden(adata,
neighbors_key="Trans",
key_added="leiden_trans",
resolution=leiden_res)
sc.pl.umap(adata,
color=color_list,
neighbors_key="Trans",
save=data_name + args.transfer + args.dimreduce + "_TL" + now,
show=False,
title=title_list)
# Plot cell score on umap
sc.pl.umap(adata,
color=color_score_list,
neighbors_key="Trans",
save=data_name + args.transfer + args.dimreduce + "_score_TL" +
now,
show=False,
title=color_score_list)
# This tsne is based on transfer learning feature
sc.pl.tsne(adata,
color=color_list,
neighbors_key="Trans",
save=data_name + args.transfer + args.dimreduce + "_TL" + now,
show=False,
title=title_list)
# Use tsne origianl version to visualize
sc.tl.tsne(adata)
# This tsne is based on transfer learning feature
# sc.pl.tsne(adata,color=[color_list[0],'sens_label_tsne','sens_preds_tsne'],save=data_name+args.transfer+args.dimreduce+"_original_tsne"+now,show=False,title=title_list)
# Plot tsne of the pretrained (autoencoder) embeddings
sc.pp.neighbors(adata, use_rep='X_pre', key_added="Pret")
sc.tl.umap(adata, neighbors_key="Pret")
sc.tl.leiden(adata,
neighbors_key="Pret",
key_added="leiden_Pret",
resolution=leiden_res)
sc.pl.umap(adata,
color=[color_list[0], 'sens_label_pret', 'sens_preds_pret'],
neighbors_key="Pret",
save=data_name + args.transfer + args.dimreduce +
"_umap_Pretrain_" + now,
show=False)
# Ari between two transfer learning embedding and sensitivity label
ari_score_trans = adjusted_rand_score(adata.obs['leiden_trans'],
adata.obs['sens_label'])
ari_score = adjusted_rand_score(adata.obs['leiden'],
adata.obs['sens_label'])
pret_ari_score = adjusted_rand_score(adata.obs['leiden_origin'],
adata.obs['leiden_Pret'])
transfer_ari_score = adjusted_rand_score(adata.obs['leiden_origin'],
adata.obs['leiden_trans'])
sc.pl.umap(adata,
color=['leiden_origin', 'leiden_trans', 'leiden_Pret'],
save=data_name + args.transfer + args.dimreduce +
"_comp_Pretrain_" + now,
show=False)
#report_df = args_df
report_df['ari_score'] = ari_score
report_df['ari_trans_score'] = ari_score_trans
report_df['ari_pre_umap'] = pret_ari_score
report_df['ari_trans_umap'] = transfer_ari_score
# Trajectory of adata
adata, corelations = trajectory(adata,
root_key='sensitive',
genes_vis=senNeu_c0_genes[:5],
root=1,
now=now,
plot=True)
gene_cor = {}
# Trajectory
for g in np.array(senNeu_c0_genes):
gene = g
express_vec = adata[:, gene].X
corr = pearsonr(
np.array(express_vec).ravel(),
np.array(adata.obs["dpt_pseudotime"]))[0]
gene_cor[gene] = corr
try:
for k in corelations.keys():
report_df['cor_dpt_' + k] = corelations[k][0]
report_df['cor_pvl_' + k] = corelations[k][1]
except:
logging.warning(
"Some of the coorelation cannot be reterived from the dictional")
################################################# END SECTION OF ANALYSIS AND POST PROCESSING #################################################
################################################# START SECTION OF ANALYSIS FOR BULK DATA #################################################
# bdata = sc.AnnData(data_r)
# bdata.obs = label_r
# bulk_degs={}
# sc.tl.rank_genes_groups(bdata, select_drug, method='wilcoxon')
# bdata = ut.de_score(bdata,select_drug)
# for label in set(label_r.loc[:,select_drug]):
# try:
# df_degs = get_de_dataframe(bdata,label)
# bulk_degs[label] = df_degs.iloc[:50,:].names
# df_degs.to_csv("saved/results/DEGs_bulk_" +str(label)+ args.predictor+ prediction + select_drug+now + '.csv')
# except:
# logging.warning("Only one class, no two calsses critical genes")
# Xsource_allTensor = torch.FloatTensor(data_r.values).to(device)
# Ysource_preTensor = source_model(Xsource_allTensor)
# Ysource_prediction = Ysource_preTensor.detach().cpu().numpy()
# bdata.obs["sens_preds"] = Ysource_prediction[:,1]
# bdata.obs["sens_label"] = Ysource_prediction.argmax(axis=1)
# bdata.obs["sens_label"] = bdata.obs["sens_label"].astype('category')
# bdata.obs["rest_preds"] = Ysource_prediction[:,0]
# sc.tl.score_genes(adata, bulk_degs['sensitive'],score_name="bulk_sens_score" )
# sc.tl.score_genes(adata, bulk_degs['resistant'],score_name="bulk_rest_score" )
# sc.pl.umap(adata,color=['bulk_sens_score','bulk_rest_score'],save=data_name+args.transfer+args.dimreduce+"umap_bg_all"+now,show=False)
# try:
# bulk_score_sens = pearsonr(adata.obs["1_score"],adata.obs["bulk_sens_score"])[0]
# report_df['bulk_sens_pearson'] = bulk_score_sens
# cluster_score_resist = pearsonr(adata.obs["0_score"],adata.obs["bulk_rest_score"])[0]
# report_df['bulk_rest_pearson'] = cluster_score_resist
# except:
# logging.warning("Bulk level gene score not exist")
# Save adata
adata.write("saved/adata/" + data_name + now + ".h5ad")
# Save report
report_df = report_df.T
report_df.to_csv("saved/results/report" + reduce_model + args.predictor +
prediction + select_drug + now + '.csv')
# In[29]: # Load source model source_encoder = AEBase(input_dim=data_r.shape[1],latent_dim=dim_au_out,h_dims=encoder_hdims) source_encoder.load_state_dict(torch.load(source_model_path)) source_encoder.to(device) # In[30]: # Set discriminator model discriminator = Predictor(input_dim=dim_au_out,output_dim=2) discriminator.to(device) loss_d = nn.CrossEntropyLoss() optimizer_d = optim.Adam(encoder.parameters(), lr=1e-2) exp_lr_scheduler_d = lr_scheduler.ReduceLROnPlateau(optimizer_d) # In[31]: device # In[32]:
def run_main(args):
################################################# START SECTION OF LOADING PARAMETERS #################################################
# Read parameters
epochs = args.epochs
dim_au_out = args.bottleneck #8, 16, 32, 64, 128, 256,512
na = args.missing_value
data_path = DATA_MAP[args.target_data]
test_size = args.test_size
select_drug = args.drug
freeze = args.freeze_pretrain
valid_size = args.valid_size
g_disperson = args.var_genes_disp
min_n_genes = args.min_n_genes
max_n_genes = args.max_n_genes
source_model_path = args.source_model_path
target_model_path = args.target_model_path
log_path = args.logging_file
batch_size = args.batch_size
encoder_hdims = args.source_h_dims.split(",")
encoder_hdims = list(map(int, encoder_hdims))
source_data_path = args.source_data
pretrain = args.pretrain
prediction = args.predition
data_name = args.target_data
label_path = args.label_path
reduce_model = args.dimreduce
predict_hdims = args.p_h_dims.split(",")
predict_hdims = list(map(int, predict_hdims))
leiden_res = args.cluster_res
load_model = bool(args.load_target_model)
# Misc
now = time.strftime("%Y-%m-%d-%H-%M-%S")
# Initialize logging and std out
out_path = log_path + now + ".err"
log_path = log_path + now + ".log"
out = open(out_path, "w")
sys.stderr = out
#Logging infomaion
logging.basicConfig(
level=logging.INFO,
filename=log_path,
filemode='a',
format=
'%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s'
)
logging.getLogger('matplotlib.font_manager').disabled = True
logging.info(args)
# Save arguments
args_df = ut.save_arguments(args, now)
AUROC = list()
AP = list()
Fone = list()
################################################# END SECTION OF LOADING PARAMETERS #################################################
cycletime = 50
while cycletime < 101:
adata = pp.read_sc_file(data_path)
adata = resample(adata,
n_samples=350,
random_state=cycletime,
replace=False)
#replace=True: 可以从a 中反复选取同一个元素。
#replace=False: a 中同一个元素只能被选取一次。
################################################# START SECTION OF SINGLE CELL DATA REPROCESSING #################################################
# Load data and preprocessing
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)
adata = pp.cal_ncount_ngenes(adata)
# Show statisctic after QX
sc.pl.violin(adata,
['n_genes_by_counts', 'total_counts', 'pct_counts_mt'],
jitter=0.4,
multi_panel=True,
save=data_name,
show=False)
sc.pl.scatter(adata, x='total_counts', y='pct_counts_mt', show=False)
sc.pl.scatter(adata,
x='total_counts',
y='n_genes_by_counts',
show=False)
if args.remove_genes == 0:
r_genes = []
else:
r_genes = REMOVE_GENES
#Preprocess data by filtering
adata = pp.receipe_my(adata,
l_n_genes=min_n_genes,
r_n_genes=max_n_genes,
filter_mincells=args.min_c,
filter_mingenes=args.min_g,
normalize=True,
log=True,
remove_genes=r_genes)
# Select highly variable genes
sc.pp.highly_variable_genes(adata,
min_disp=g_disperson,
max_disp=np.inf,
max_mean=6)
sc.pl.highly_variable_genes(adata, save=data_name, show=False)
adata.raw = adata
adata = adata[:, adata.var.highly_variable]
# Preprocess data if spcific process is required
if data_name == 'GSE117872':
adata = ut.specific_process(adata,
dataname=data_name,
select_origin=args.batch_id)
data = adata.X
elif data_name == 'GSE122843':
adata = ut.specific_process(adata, dataname=data_name)
data = adata.X
elif data_name == 'GSE110894':
adata = ut.specific_process(adata, dataname=data_name)
data = adata.X
elif data_name == 'GSE112274':
adata = ut.specific_process(adata, dataname=data_name)
data = adata.X
elif data_name == 'GSE116237':
adata = ut.specific_process(adata, dataname=data_name)
data = adata.X
elif data_name == 'GSE108383':
adata = ut.specific_process(adata, dataname=data_name)
data = adata.X
else:
data = adata.X
# PCA
# Generate neighbor graph
sc.tl.pca(adata, svd_solver='arpack')
sc.pp.neighbors(adata, n_neighbors=10)
# Generate cluster labels
sc.tl.leiden(adata, resolution=leiden_res)
sc.tl.umap(adata)
# sc.pl.umap(adata,color=['leiden'],save=data_name+'umap'+now,show=False)
adata.obs['leiden_origin'] = adata.obs['leiden']
adata.obsm['X_umap_origin'] = adata.obsm['X_umap']
data_c = adata.obs['leiden'].astype("long").to_list()
################################################# END SECTION OF SINGLE CELL DATA REPROCESSING #################################################
################################################# START SECTION OF LOADING SC DATA TO THE TENSORS #################################################
#Prepare to normailize and split target data
mmscaler = preprocessing.MinMaxScaler()
try:
data = mmscaler.fit_transform(data)
except:
logging.warning("Only one class, no ROC")
# Process sparse data
data = data.todense()
data = mmscaler.fit_transform(data)
# Split data to train and valid set
# Along with the leiden conditions for CVAE propose
Xtarget_train, Xtarget_valid, Ctarget_train, Ctarget_valid = train_test_split(
data, data_c, test_size=valid_size, random_state=42)
# Select the device of gpu
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Assuming that we are on a CUDA machine, this should print a CUDA device:
logging.info(device)
torch.cuda.set_device(device)
# Construct datasets and data loaders
Xtarget_trainTensor = torch.FloatTensor(Xtarget_train).to(device)
Xtarget_validTensor = torch.FloatTensor(Xtarget_valid).to(device)
# Use leiden label if CVAE is applied
Ctarget_trainTensor = torch.LongTensor(Ctarget_train).to(device)
Ctarget_validTensor = torch.LongTensor(Ctarget_valid).to(device)
X_allTensor = torch.FloatTensor(data).to(device)
C_allTensor = torch.LongTensor(data_c).to(device)
train_dataset = TensorDataset(Xtarget_trainTensor, Ctarget_trainTensor)
valid_dataset = TensorDataset(Xtarget_validTensor, Ctarget_validTensor)
Xtarget_trainDataLoader = DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
Xtarget_validDataLoader = DataLoader(dataset=valid_dataset,
batch_size=batch_size,
shuffle=True)
dataloaders_pretrain = {
'train': Xtarget_trainDataLoader,
'val': Xtarget_validDataLoader
}
################################################# START SECTION OF LOADING SC DATA TO THE TENSORS #################################################
################################################# START SECTION OF LOADING BULK DATA #################################################
# Read source data
data_r = pd.read_csv(source_data_path, index_col=0)
label_r = pd.read_csv(label_path, index_col=0)
label_r = label_r.fillna(na)
# Extract labels
selected_idx = label_r.loc[:, select_drug] != na
label = label_r.loc[selected_idx, select_drug]
label = label.values.reshape(-1, 1)
if prediction == "regression":
lbscaler = preprocessing.MinMaxScaler()
label = lbscaler.fit_transform(label)
dim_model_out = 1
else:
le = preprocessing.LabelEncoder()
label = le.fit_transform(label)
dim_model_out = 2
# Process source data
mmscaler = preprocessing.MinMaxScaler()
source_data = mmscaler.fit_transform(data_r)
# Split source data
Xsource_train_all, Xsource_test, Ysource_train_all, Ysource_test = train_test_split(
source_data, label, test_size=test_size, random_state=42)
Xsource_train, Xsource_valid, Ysource_train, Ysource_valid = train_test_split(
Xsource_train_all,
Ysource_train_all,
test_size=valid_size,
random_state=42)
# Transform source data
# Construct datasets and data loaders
Xsource_trainTensor = torch.FloatTensor(Xsource_train).to(device)
Xsource_validTensor = torch.FloatTensor(Xsource_valid).to(device)
if prediction == "regression":
Ysource_trainTensor = torch.FloatTensor(Ysource_train).to(device)
Ysource_validTensor = torch.FloatTensor(Ysource_valid).to(device)
else:
Ysource_trainTensor = torch.LongTensor(Ysource_train).to(device)
Ysource_validTensor = torch.LongTensor(Ysource_valid).to(device)
sourcetrain_dataset = TensorDataset(Xsource_trainTensor,
Ysource_trainTensor)
sourcevalid_dataset = TensorDataset(Xsource_validTensor,
Ysource_validTensor)
Xsource_trainDataLoader = DataLoader(dataset=sourcetrain_dataset,
batch_size=batch_size,
shuffle=True)
Xsource_validDataLoader = DataLoader(dataset=sourcevalid_dataset,
batch_size=batch_size,
shuffle=True)
dataloaders_source = {
'train': Xsource_trainDataLoader,
'val': Xsource_validDataLoader
}
################################################# END SECTION OF LOADING BULK DATA #################################################
################################################# START SECTION OF MODEL CUNSTRUCTION #################################################
# Construct target encoder
if reduce_model == "AE":
encoder = AEBase(input_dim=data.shape[1],
latent_dim=dim_au_out,
h_dims=encoder_hdims)
loss_function_e = nn.MSELoss()
elif reduce_model == "VAE":
encoder = VAEBase(input_dim=data.shape[1],
latent_dim=dim_au_out,
h_dims=encoder_hdims)
elif reduce_model == "CVAE":
# Number of condition is equal to the number of clusters
encoder = CVAEBase(input_dim=data.shape[1],
n_conditions=len(set(data_c)),
latent_dim=dim_au_out,
h_dims=encoder_hdims)
if torch.cuda.is_available():
encoder.cuda()
logging.info("Target encoder structure is: ")
logging.info(encoder)
encoder.to(device)
optimizer_e = optim.Adam(encoder.parameters(), lr=1e-2)
loss_function_e = nn.MSELoss()
exp_lr_scheduler_e = lr_scheduler.ReduceLROnPlateau(optimizer_e)
# Load source model before transfer
if prediction == "regression":
dim_model_out = 1
else:
dim_model_out = 2
# Load AE model
if reduce_model == "AE":
source_model = PretrainedPredictor(
input_dim=Xsource_train.shape[1],
latent_dim=dim_au_out,
h_dims=encoder_hdims,
hidden_dims_predictor=predict_hdims,
output_dim=dim_model_out,
pretrained_weights=None,
freezed=freeze)
source_model.load_state_dict(torch.load(source_model_path))
source_encoder = source_model
# Load VAE model
elif reduce_model in ["VAE", "CVAE"]:
source_model = PretrainedVAEPredictor(
input_dim=Xsource_train.shape[1],
latent_dim=dim_au_out,
h_dims=encoder_hdims,
hidden_dims_predictor=predict_hdims,
output_dim=dim_model_out,
pretrained_weights=None,
freezed=freeze,
z_reparam=bool(args.VAErepram))
source_model.load_state_dict(torch.load(source_model_path))
source_encoder = source_model
logging.info("Load pretrained source model from: " + source_model_path)
source_encoder.to(device)
################################################# END SECTION OF MODEL CUNSTRUCTION #################################################
################################################# START SECTION OF SC MODEL PRETRAININIG #################################################
# Pretrain target encoder
# Pretain using autoencoder is pretrain is not False
if (str(pretrain) != '0'):
# Pretrained target encoder if there are not stored files in the harddisk
train_flag = True
pretrain = str(pretrain)
if (os.path.exists(pretrain) == True):
try:
encoder.load_state_dict(torch.load(pretrain))
logging.info("Load pretrained target encoder from " +
pretrain)
train_flag = False
except:
logging.warning(
"Loading failed, procceed to re-train model")
if train_flag == True:
if reduce_model == "AE":
encoder, loss_report_en = t.train_AE_model(
net=encoder,
data_loaders=dataloaders_pretrain,
optimizer=optimizer_e,
loss_function=loss_function_e,
n_epochs=epochs,
scheduler=exp_lr_scheduler_e,
save_path=pretrain)
elif reduce_model == "VAE":
encoder, loss_report_en = t.train_VAE_model(
net=encoder,
data_loaders=dataloaders_pretrain,
optimizer=optimizer_e,
n_epochs=epochs,
scheduler=exp_lr_scheduler_e,
save_path=pretrain)
elif reduce_model == "CVAE":
encoder, loss_report_en = t.train_CVAE_model(
net=encoder,
data_loaders=dataloaders_pretrain,
optimizer=optimizer_e,
n_epochs=epochs,
scheduler=exp_lr_scheduler_e,
save_path=pretrain)
logging.info("Pretrained finished")
# Before Transfer learning, we test the performance of using no transfer performance:
# Use vae result to predict
if (args.dimreduce != "CVAE"):
embeddings_pretrain = encoder.encode(X_allTensor)
else:
embeddings_pretrain = encoder.encode(X_allTensor, C_allTensor)
pretrain_prob_prediction = source_model.predict(
embeddings_pretrain).detach().cpu().numpy()
adata.obs["sens_preds_pret"] = pretrain_prob_prediction[:, 1]
adata.obs["sens_label_pret"] = pretrain_prob_prediction.argmax(
axis=1)
# Add embeddings to the adata object
embeddings_pretrain = embeddings_pretrain.detach().cpu().numpy()
adata.obsm["X_pre"] = embeddings_pretrain
################################################# END SECTION OF SC MODEL PRETRAININIG #################################################
################################################# START SECTION OF TRANSFER LEARNING TRAINING #################################################
# Using ADDA transfer learning
if args.transfer == 'ADDA':
# Set discriminator model
discriminator = Predictor(input_dim=dim_au_out, output_dim=2)
discriminator.to(device)
loss_d = nn.CrossEntropyLoss()
optimizer_d = optim.Adam(encoder.parameters(), lr=1e-2)
exp_lr_scheduler_d = lr_scheduler.ReduceLROnPlateau(optimizer_d)
# Adversairal trainning
discriminator, encoder, report_, report2_ = t.train_ADDA_model(
source_encoder,
encoder,
discriminator,
dataloaders_source,
dataloaders_pretrain,
loss_d,
loss_d,
# Should here be all optimizer d?
optimizer_d,
optimizer_d,
exp_lr_scheduler_d,
exp_lr_scheduler_d,
epochs,
device,
target_model_path)
logging.info("Transfer ADDA finished")
# DaNN model
elif args.transfer == 'DaNN':
# Set predictor loss
loss_d = nn.CrossEntropyLoss()
optimizer_d = optim.Adam(encoder.parameters(), lr=1e-2)
exp_lr_scheduler_d = lr_scheduler.ReduceLROnPlateau(optimizer_d)
# Set DaNN model
DaNN_model = DaNN(source_model=source_encoder,
target_model=encoder)
DaNN_model.to(device)
def loss(x, y, GAMMA=args.GAMMA_mmd):
result = mmd.mmd_loss(x, y, GAMMA)
return result
loss_disrtibution = loss
# Tran DaNN model
DaNN_model, report_ = t.train_DaNN_model(
DaNN_model,
dataloaders_source,
dataloaders_pretrain,
# Should here be all optimizer d?
optimizer_d,
loss_d,
epochs,
exp_lr_scheduler_d,
dist_loss=loss_disrtibution,
load=load_model,
weight=args.mmd_weight,
save_path=target_model_path + "_DaNN.pkl")
encoder = DaNN_model.target_model
source_model = DaNN_model.source_model
logging.info("Transfer DaNN finished")
if (load_model == False):
ut.plot_loss(report_[0],
path="figures/train_loss_" + now + ".pdf")
ut.plot_loss(report_[1],
path="figures/mmd_loss_" + now + ".pdf")
if (args.dimreduce != 'CVAE'):
# Attribute test using integrated gradient
# Generate a target model including encoder and predictor
target_model = TargetModel(source_model, encoder)
# Allow require gradients and process label
Xtarget_validTensor.requires_grad_()
# Run integrated gradient check
# Return adata and feature integrated gradient
ytarget_validPred = target_model(
Xtarget_validTensor).detach().cpu().numpy()
ytarget_validPred = ytarget_validPred.argmax(axis=1)
adata, attr = ut.integrated_gradient_check(
net=target_model,
input=Xtarget_validTensor,
target=ytarget_validPred,
adata=adata,
n_genes=args.n_DL_genes,
save_name=reduce_model + args.predictor + prediction +
select_drug + now)
adata, attr0 = ut.integrated_gradient_check(
net=target_model,
input=Xtarget_validTensor,
target=ytarget_validPred,
target_class=0,
adata=adata,
n_genes=args.n_DL_genes,
save_name=reduce_model + args.predictor + prediction +
select_drug + now)
else:
print()
################################################# END SECTION OF TRANSER LEARNING TRAINING #################################################
################################################# START SECTION OF PREPROCESSING FEATURES #################################################
# Extract feature embeddings
# Extract prediction probabilities
if (args.dimreduce != "CVAE"):
embedding_tensors = encoder.encode(X_allTensor)
else:
embedding_tensors = encoder.encode(X_allTensor, C_allTensor)
prediction_tensors = source_model.predictor(embedding_tensors)
embeddings = embedding_tensors.detach().cpu().numpy()
predictions = prediction_tensors.detach().cpu().numpy()
# Transform predict8ion probabilities to 0-1 labels
if (prediction == "regression"):
adata.obs["sens_preds"] = predictions
else:
adata.obs["sens_preds"] = predictions[:, 1]
adata.obs["sens_label"] = predictions.argmax(axis=1)
adata.obs["sens_label"] = adata.obs["sens_label"].astype(
'category')
adata.obs["rest_preds"] = predictions[:, 0]
################################################# END SECTION OF PREPROCESSING FEATURES #################################################
################################################# START SECTION OF ANALYSIS AND POST PROCESSING #################################################
# Pipeline of scanpy
# Add embeddings to the adata package
adata.obsm["X_Trans"] = embeddings
#sc.tl.umap(adata)
sc.pp.neighbors(adata, n_neighbors=10, use_rep="X_Trans")
# Use t-sne on transfer learning features
sc.tl.tsne(adata, use_rep="X_Trans")
# Leiden on the data
# sc.tl.leiden(adata)
# Plot tsne
# sc.pl.tsne(adata,save=data_name+now,color=["leiden"],show=False)
# Differenrial expression genes
sc.tl.rank_genes_groups(adata, 'leiden', method='wilcoxon')
# sc.pl.rank_genes_groups(adata, n_genes=args.n_DE_genes, sharey=False,save=data_name+now,show=False)
# Differenrial expression genes across 0-1 classes
sc.tl.rank_genes_groups(adata, 'sens_label', method='wilcoxon')
adata = ut.de_score(adata, clustername='sens_label')
# save DE genes between 0-1 class
# for label in [0,1]:
# try:
# df_degs = get_de_dataframe(adata,label)
# #df_degs.to_csv("saved/results/DEGs_class_" +str(label)+ args.predictor+ prediction + select_drug+now + '.csv')
# except:
# logging.warning("Only one class, no two calsses critical genes")
# Generate reports of scores
report_df = args_df
# Data specific benchmarking
# sens_pb_pret = adata.obs['sens_preds_pret']
# lb_pret = adata.obs['sens_label_pret']
# sens_pb_umap = adata.obs['sens_preds_umap']
# lb_umap = adata.obs['sens_label_umap']
# sens_pb_tsne = adata.obs['sens_preds_tsne']
# lb_tsne = adata.obs['sens_label_tsne']
if (data_name == 'GSE117872'):
label = adata.obs['cluster']
label_no_ho = label
if len(label[label != "Sensitive"]) > 0:
# label[label != "Sensitive"] = 'Resistant'
label_no_ho[label_no_ho != "Resistant"] = 'Sensitive'
adata.obs['sens_truth'] = label_no_ho
le_sc = LabelEncoder()
le_sc.fit(['Resistant', 'Sensitive'])
sens_pb_results = adata.obs['sens_preds']
Y_test = le_sc.transform(label_no_ho)
lb_results = adata.obs['sens_label']
color_list = ["sens_truth", "sens_label", 'sens_preds']
color_score_list = [
"Sensitive_score", "Resistant_score", "1_score", "0_score"
]
sens_score = pearsonr(adata.obs["sens_preds"],
adata.obs["Sensitive_score"])[0]
resistant_score = pearsonr(adata.obs["sens_preds"],
adata.obs["Resistant_score"])[0]
cluster_score_sens = pearsonr(adata.obs["1_score"],
adata.obs["Sensitive_score"])[0]
cluster_score_resist = pearsonr(adata.obs["0_score"],
adata.obs["Resistant_score"])[0]
report_df['sens_pearson'] = sens_score
report_df['resist_pearson'] = resistant_score
report_df['1_pearson'] = cluster_score_sens
report_df['0_pearson'] = cluster_score_resist
elif (data_name == 'GSE110894'):
report_df = report_df.T
Y_test = adata.obs['sensitive']
sens_pb_results = adata.obs['sens_preds']
lb_results = adata.obs['sens_label']
le_sc = LabelEncoder()
le_sc.fit(['Resistant', 'Sensitive'])
label_descrbie = le_sc.inverse_transform(Y_test)
adata.obs['sens_truth'] = label_descrbie
color_list = ["sens_truth", "sens_label", 'sens_preds']
color_score_list = [
"sensitive_score", "resistant_score", "1_score", "0_score"
]
sens_score = pearsonr(adata.obs["sens_preds"],
adata.obs["sensitive_score"])[0]
resistant_score = pearsonr(adata.obs["sens_preds"],
adata.obs["resistant_score"])[0]
report_df['sens_pearson'] = sens_score
report_df['resist_pearson'] = resistant_score
cluster_score_sens = pearsonr(adata.obs["1_score"],
adata.obs["sensitive_score"])[0]
cluster_score_resist = pearsonr(adata.obs["0_score"],
adata.obs["resistant_score"])[0]
report_df['1_pearson'] = cluster_score_sens
report_df['0_pearson'] = cluster_score_resist
elif (data_name == 'GSE108383'):
report_df = report_df.T
Y_test = adata.obs['sensitive']
sens_pb_results = adata.obs['sens_preds']
lb_results = adata.obs['sens_label']
le_sc = LabelEncoder()
le_sc.fit(['Resistant', 'Sensitive'])
label_descrbie = le_sc.inverse_transform(Y_test)
adata.obs['sens_truth'] = label_descrbie
color_list = ["sens_truth", "sens_label", 'sens_preds']
color_score_list = [
"sensitive_score", "resistant_score", "1_score", "0_score"
]
sens_score = pearsonr(adata.obs["sens_preds"],
adata.obs["sensitive_score"])[0]
resistant_score = pearsonr(adata.obs["sens_preds"],
adata.obs["resistant_score"])[0]
report_df['sens_pearson'] = sens_score
report_df['resist_pearson'] = resistant_score
cluster_score_sens = pearsonr(adata.obs["1_score"],
adata.obs["sensitive_score"])[0]
cluster_score_resist = pearsonr(adata.obs["0_score"],
adata.obs["resistant_score"])[0]
report_df['1_pearson'] = cluster_score_sens
report_df['0_pearson'] = cluster_score_resist
if (data_name in ['GSE110894', 'GSE117872', 'GSE108383']):
ap_score = average_precision_score(Y_test, sens_pb_results)
report_dict = classification_report(Y_test,
lb_results,
output_dict=True)
classification_report_df = pd.DataFrame(report_dict).T
#classification_report_df.to_csv("saved/results/clf_report_" + reduce_model + args.predictor+ prediction + select_drug+now + '.csv')
sum_classification_report_df = pd.DataFrame()
sum_classification_report_df = sum_classification_report_df.append(
classification_report_df, ignore_index=False)
try:
auroc_score = roc_auc_score(Y_test, sens_pb_results)
except:
logging.warning("Only one class, no ROC")
auroc_pret = auroc_umap = auroc_tsne = auroc_score = 0
report_df['auroc_score'] = auroc_score
report_df['ap_score'] = ap_score
ap_title = "ap: " + str(
Decimal(ap_score).quantize(Decimal('0.0000')))
auroc_title = "roc: " + str(
Decimal(auroc_score).quantize(Decimal('0.0000')))
title_list = ["Ground truth", "Prediction", "Probability"]
else:
color_list = ["leiden", "sens_label", 'sens_preds']
title_list = ['Cluster', "Prediction", "Probability"]
color_score_list = color_list
# Simple analysis do neighbors in adata using PCA embeddings
#sc.pp.neighbors(adata)
# Run UMAP dimension reduction
sc.pp.neighbors(adata)
sc.tl.umap(adata)
# Run leiden clustering
# sc.tl.leiden(adata,resolution=leiden_res)
# # Plot uamp
# sc.pl.umap(adata,color=[color_list[0],'sens_label_umap','sens_preds_umap'],save=data_name+args.transfer+args.dimreduce+now,show=False,title=title_list)
# Run embeddings using transfered embeddings
sc.pp.neighbors(adata, use_rep='X_Trans', key_added="Trans")
sc.tl.umap(adata, neighbors_key="Trans")
sc.tl.leiden(adata,
neighbors_key="Trans",
key_added="leiden_trans",
resolution=leiden_res)
# sc.pl.umap(adata,color=color_list,neighbors_key="Trans",save=data_name+args.transfer+args.dimreduce+"_TL"+now,show=False,title=title_list)
# Plot tsne
# sc.pl.umap(adata,color=color_score_list,neighbors_key="Trans",save=data_name+args.transfer+args.dimreduce+"_score_TL"+now,show=False,title=color_score_list)
# This tsne is based on transfer learning feature
# sc.pl.tsne(adata,color=color_list,neighbors_key="Trans",save=data_name+args.transfer+args.dimreduce+"_TL"+now,show=False,title=title_list)
# Use tsne origianl version to visualize
sc.tl.tsne(adata)
# This tsne is based on transfer learning feature
# sc.pl.tsne(adata,color=[color_list[0],'sens_label_tsne','sens_preds_tsne'],save=data_name+args.transfer+args.dimreduce+"_original_tsne"+now,show=False,title=title_list)
# Plot tsne of the pretrained (autoencoder) embeddings
sc.pp.neighbors(adata, use_rep='X_pre', key_added="Pret")
sc.tl.umap(adata, neighbors_key="Pret")
sc.tl.leiden(adata,
neighbors_key="Pret",
key_added="leiden_Pret",
resolution=leiden_res)
# sc.pl.umap(adata,color=[color_list[0],'sens_label_pret','sens_preds_pret'],neighbors_key="Pret",save=data_name+args.transfer+args.dimreduce+"_umap_Pretrain_"+now,show=False)
# Ari between two transfer learning embedding and sensitivity label
ari_score_trans = adjusted_rand_score(adata.obs['leiden_trans'],
adata.obs['sens_label'])
ari_score = adjusted_rand_score(adata.obs['leiden'],
adata.obs['sens_label'])
pret_ari_score = adjusted_rand_score(adata.obs['leiden_origin'],
adata.obs['leiden_Pret'])
transfer_ari_score = adjusted_rand_score(adata.obs['leiden_origin'],
adata.obs['leiden_trans'])
# sc.pl.umap(adata,color=['leiden_origin','leiden_trans','leiden_Pret'],save=data_name+args.transfer+args.dimreduce+"_comp_Pretrain_"+now,show=False)
#report_df = args_df
report_df['ari_score'] = ari_score
report_df['ari_trans_score'] = ari_score_trans
report_df['ari_pre_umap'] = pret_ari_score
report_df['ari_trans_umap'] = transfer_ari_score
cluster_ids = set(adata.obs['leiden'])
# Two class: sens and resistant between clustering label
# Trajectory of adata
#adata = trajectory(adata,now=now)
# Draw PDF
# sc.pl.draw_graph(adata, color=['leiden', 'dpt_pseudotime'],save=data_name+args.dimreduce+"leiden+trajectory")
# sc.pl.draw_graph(adata, color=['sens_preds', 'dpt_pseudotime_leiden_trans','leiden_trans'],save=data_name+args.dimreduce+"sens_preds+trajectory")
# Save adata
#adata.write("saved/adata/"+data_name+now+".h5ad")
# Save report
report_df = report_df.T
AP.append(ap_score)
AUROC.append(auroc_score)
Fone.append(report_dict['weighted avg']['f1-score'])
cycletime = cycletime + 1
AUROC2 = pd.DataFrame(AUROC)
AP2 = pd.DataFrame(AP)
Fonefinal = pd.DataFrame(Fone)
AUROC2.to_csv("saved/results/AUROC2report" + reduce_model +
args.predictor + prediction + select_drug + now + '.csv')
################################################# END SECTION OF ANALYSIS AND POST PROCESSING #################################################
Fonefinal.to_csv("saved/results/clf_report_Fonefinal" + reduce_model +
args.predictor + prediction + select_drug + now + '.csv')
AP2.to_csv("saved/results/clf_umap_report_AP2" + reduce_model +
args.predictor + prediction + select_drug + now + '.csv')