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test_trAE.py
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test_trAE.py
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import argparse
import os
import anndata
import numpy as np
import scanpy as sc
from scipy import sparse
import trvae
if not os.getcwd().endswith("tests"):
os.chdir("./tests")
from matplotlib import pyplot as plt
DATASETS = {
"Pancreas": {"name": 'pancreas', "source_key": "Baron", "target_key": "Segerstolpe",
'train_celltypes': ['alpha', 'beta', 'ductal', 'acinar', 'delta', 'gamma'],
'test_celltypes': ['beta'],
'cell_type': 'celltype'},
"PBMC": {"name": 'pbmc', "source_key": "control", "target_key": 'stimulated',
"cell_type": "cell_type", 'spec_cell_types': ['CD4T', "CD14+Mono", "FCGR3A+Mono"]},
"Hpoly": {"name": 'hpoly', "source_key": "Control", "target_key": 'Hpoly.Day10',
"cell_type": "cell_label", 'spec_cell_types': ['Tuft', 'Endocrine']},
"Salmonella": {"name": 'salmonella', "source_key": "Control", "target_key": 'Salmonella',
"cell_type": "cell_label", 'spec_cell_types': ['Tuft', 'Endocrine']},
}
def train_network(data_dict=None,
z_dim=100,
beta=0.001,
n_epochs=500,
batch_size=512,
dropout_rate=0.2,
):
data_name = data_dict['name']
target_key = data_dict.get('target_key', None)
cell_type_key = data_dict.get("cell_type", None)
train_data = sc.read(f"../data/{data_name}/train_{data_name}.h5ad")
valid_data = sc.read(f"../data/{data_name}/valid_{data_name}.h5ad")
cell_types = train_data.obs[cell_type_key].unique().tolist()
spec_cell_type = data_dict.get("spec_cell_types", None)
if spec_cell_type is not []:
cell_types = spec_cell_type
for cell_type in cell_types:
net_train_data = train_data.copy()[
~((train_data.obs[cell_type_key] == cell_type) & (train_data.obs['condition'] == target_key))]
net_valid_data = valid_data.copy()[
~((valid_data.obs[cell_type_key] == cell_type) & (valid_data.obs['condition'] == target_key))]
network = trvae.trAE(x_dimension=net_train_data.shape[1],
z_dimension=z_dim,
beta=beta,
model_path=f"../models/RAE/{data_name}/{cell_type}/{z_dim}/",
dropout_rate=dropout_rate)
network.train(net_train_data,
use_validation=True,
valid_adata=net_valid_data,
n_epochs=n_epochs,
batch_size=batch_size,
early_stop_limit=100,
shuffle=True)
print(f"Model for {cell_type} has been trained")
def visualize_trained_network_results(data_dict, z_dim=100):
plt.close("all")
data_name = data_dict.get('name', None)
source_key = data_dict.get('source_key', None)
target_key = data_dict.get('target_key', None)
cell_type_key = data_dict.get("cell_type", None)
data = sc.read(f"../data/{data_name}/train_{data_name}.h5ad")
cell_types = data.obs[cell_type_key].unique().tolist()
spec_cell_type = data_dict.get("spec_cell_types", None)
if spec_cell_type is not []:
cell_types = spec_cell_type
for cell_type in cell_types:
path_to_save = f"../results/RAE/{data_name}/{cell_type}/{z_dim}/{source_key} to {target_key}/Visualizations/"
os.makedirs(path_to_save, exist_ok=True)
sc.settings.figdir = os.path.abspath(path_to_save)
train_data = data.copy()[~((data.obs['condition'] == target_key) & (data.obs[cell_type_key] == cell_type))]
cell_type_adata = data[data.obs[cell_type_key] == cell_type]
network = trvae.trAE(x_dimension=data.shape[1],
z_dimension=z_dim,
model_path=f"../models/RAE/{data_name}/{cell_type}/{z_dim}/")
network.restore_model()
if sparse.issparse(data.X):
data.X = data.X.A
feed_data = data.X
latent_with_true_labels = network.to_z_latent(feed_data)
latent_with_fake_labels = network.to_z_latent(feed_data)
cell_type_ctrl = cell_type_adata.copy()[cell_type_adata.obs['condition'] == source_key]
print(cell_type_ctrl.shape, cell_type_adata.shape)
pred_celltypes = network.predict(cell_type_ctrl)
pred_adata = anndata.AnnData(X=pred_celltypes)
pred_adata.obs['condition'] = ['predicted'] * pred_adata.shape[0]
pred_adata.var = cell_type_adata.var
if data_name == "pbmc":
sc.tl.rank_genes_groups(cell_type_adata, groupby="condition", n_genes=100, method="wilcoxon")
top_100_genes = cell_type_adata.uns["rank_genes_groups"]["names"][target_key].tolist()
gene_list = top_100_genes[:10]
else:
sc.tl.rank_genes_groups(cell_type_adata, groupby="condition", n_genes=100, method="wilcoxon")
top_50_down_genes = cell_type_adata.uns["rank_genes_groups"]["names"][source_key].tolist()
top_50_up_genes = cell_type_adata.uns["rank_genes_groups"]["names"][target_key].tolist()
top_100_genes = top_50_up_genes + top_50_down_genes
gene_list = top_50_down_genes[:5] + top_50_up_genes[:5]
cell_type_adata = cell_type_adata.concatenate(pred_adata)
trvae.plotting.reg_mean_plot(cell_type_adata,
top_100_genes=top_100_genes,
gene_list=gene_list,
condition_key='condition',
axis_keys={"x": 'predicted', 'y': target_key},
labels={'x': 'pred stim', 'y': 'real stim'},
legend=False,
fontsize=20,
textsize=14,
title=cell_type,
path_to_save=os.path.join(path_to_save,
f'rcvae_reg_mean_{data_name}_{cell_type}.pdf'))
trvae.plotting.reg_var_plot(cell_type_adata,
top_100_genes=top_100_genes,
gene_list=gene_list,
condition_key='condition',
axis_keys={"x": 'predicted', 'y': target_key},
labels={'x': 'pred stim', 'y': 'real stim'},
legend=False,
fontsize=20,
textsize=14,
title=cell_type,
path_to_save=os.path.join(path_to_save,
f'rcvae_reg_var_{data_name}_{cell_type}.pdf'))
import matplotlib as mpl
mpl.rcParams.update(mpl.rcParamsDefault)
latent_with_true_labels = sc.AnnData(X=latent_with_true_labels)
latent_with_true_labels.obs['condition'] = data.obs['condition'].values
latent_with_true_labels.obs[cell_type_key] = data.obs[cell_type_key].values
latent_with_fake_labels = sc.AnnData(X=latent_with_fake_labels)
latent_with_fake_labels.obs['condition'] = data.obs['condition'].values
latent_with_fake_labels.obs[cell_type_key] = data.obs[cell_type_key].values
color = ['condition', cell_type_key]
sc.pp.neighbors(train_data)
sc.tl.umap(train_data)
sc.pl.umap(train_data, color=color,
save=f'_{data_name}_{cell_type}_train_data',
show=False)
sc.pp.neighbors(latent_with_true_labels)
sc.tl.umap(latent_with_true_labels)
sc.pl.umap(latent_with_true_labels, color=color,
save=f"_{data_name}_{cell_type}_latent_with_true_labels",
show=False)
sc.pp.neighbors(latent_with_fake_labels)
sc.tl.umap(latent_with_fake_labels)
sc.pl.umap(latent_with_fake_labels, color=color,
save=f"_{data_name}_{cell_type}_latent_with_fake_labels",
show=False)
sc.pl.violin(cell_type_adata, keys=top_100_genes[0], groupby='condition',
save=f"_{data_name}_{cell_type}_{top_100_genes[0]}",
show=False)
plt.close("all")
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Sample a trained autoencoder.')
arguments_group = parser.add_argument_group("Parameters")
arguments_group.add_argument('-d', '--data', type=str, required=True,
help='name of dataset you want to train')
arguments_group.add_argument('-z', '--z_dim', type=int, default=20, required=False,
help='latent space dimension')
arguments_group.add_argument('-b', '--beta', type=float, default=0.005, required=False,
help='Beta coeff in loss term')
arguments_group.add_argument('-n', '--n_epochs', type=int, default=5000, required=False,
help='Maximum Number of epochs for training')
arguments_group.add_argument('-c', '--batch_size', type=int, default=512, required=False,
help='Batch Size')
arguments_group.add_argument('-r', '--dropout_rate', type=float, default=0.4, required=False,
help='Dropout ratio')
args = vars(parser.parse_args())
data_dict = DATASETS[args['data']]
del args['data']
train_network(data_dict=data_dict, **args)
visualize_trained_network_results(data_dict, z_dim=args['z_dim'])
print(f"Model for {data_dict['name']} has been trained and sample results are ready!")