def visualize_trained_network_results(network,
train,
cell_type,
conditions={
"ctrl": "control",
"stim": "stimulated"
},
condition_key="condition",
cell_type_key="cell_type",
path_to_save="./figures/",
plot_umap=True,
plot_reg=True):
plt.close("all")
os.makedirs(path_to_save, exist_ok=True)
sc.settings.figdir = os.path.abspath(path_to_save)
if isinstance(network, scgen.VAEArithKeras):
if sparse.issparse(train.X):
latent = network.to_latent(train.X.A)
else:
latent = network.to_latent(train.X)
latent = sc.AnnData(X=latent,
obs={
condition_key:
train.obs[condition_key].tolist(),
cell_type_key:
train.obs[cell_type_key].tolist()
})
if plot_umap:
sc.pp.neighbors(latent)
sc.tl.umap(latent)
sc.pl.umap(latent,
color=[condition_key, cell_type_key],
save=f"_latent",
show=False)
cell_type_data = train[train.obs[cell_type_key] == cell_type]
pred, delta = network.predict(adata=cell_type_data,
conditions=conditions,
cell_type_key=cell_type_key,
condition_key=condition_key,
celltype_to_predict=cell_type)
pred_adata = anndata.AnnData(
pred,
obs={condition_key: ["pred"] * len(pred)},
var={"var_names": cell_type_data.var_names})
all_adata = cell_type_data.concatenate(pred_adata)
sc.tl.rank_genes_groups(cell_type_data,
groupby=condition_key,
n_genes=100)
diff_genes = cell_type_data.uns["rank_genes_groups"]["names"][
conditions["stim"]]
if plot_reg:
scgen.plotting.reg_mean_plot(all_adata,
condition_key=condition_key,
axis_keys={
"x": "pred",
"y": conditions["stim"]
},
gene_list=diff_genes[:5],
path_to_save=os.path.join(
path_to_save,
f"reg_mean_all_genes.pdf"))
scgen.plotting.reg_var_plot(all_adata,
condition_key=condition_key,
axis_keys={
"x": "pred",
"y": conditions["stim"]
},
gene_list=diff_genes[:5],
path_to_save=os.path.join(
path_to_save,
f"reg_var_all_genes.pdf"))
all_adata_top_100_genes = all_adata.copy()[:, diff_genes.tolist()]
scgen.plotting.reg_mean_plot(all_adata_top_100_genes,
condition_key=condition_key,
axis_keys={
"x": "pred",
"y": conditions["stim"]
},
gene_list=diff_genes[:5],
path_to_save=os.path.join(
path_to_save,
f"reg_mean_top_100_genes.pdf"))
scgen.plotting.reg_var_plot(all_adata_top_100_genes,
condition_key=condition_key,
axis_keys={
"x": "pred",
"y": conditions["stim"]
},
gene_list=diff_genes[:5],
path_to_save=os.path.join(
path_to_save,
f"reg_var_top_100_genes.pdf"))
all_adata_top_50_genes = all_adata.copy()[:,
diff_genes.tolist()[:50]]
scgen.plotting.reg_mean_plot(all_adata_top_50_genes,
condition_key=condition_key,
axis_keys={
"x": "pred",
"y": conditions["stim"]
},
gene_list=diff_genes[:5],
path_to_save=os.path.join(
path_to_save,
f"reg_mean_top_50_genes.pdf"))
scgen.plotting.reg_var_plot(all_adata_top_50_genes,
condition_key=condition_key,
axis_keys={
"x": "pred",
"y": conditions["stim"]
},
gene_list=diff_genes[:5],
path_to_save=os.path.join(
path_to_save,
f"reg_var_top_50_genes.pdf"))
if plot_umap:
sc.pp.neighbors(all_adata)
sc.tl.umap(all_adata)
sc.pl.umap(all_adata,
color=condition_key,
save="pred_all_genes",
show=False)
sc.pp.neighbors(all_adata_top_100_genes)
sc.tl.umap(all_adata_top_100_genes)
sc.pl.umap(all_adata_top_100_genes,
color=condition_key,
save="pred_top_100_genes",
show=False)
sc.pp.neighbors(all_adata_top_50_genes)
sc.tl.umap(all_adata_top_50_genes)
sc.pl.umap(all_adata_top_50_genes,
color=condition_key,
save="pred_top_50_genes",
show=False)
sc.pl.violin(all_adata,
keys=diff_genes.tolist()[0],
groupby=condition_key,
save=f"_{diff_genes.tolist()[0]}",
show=False)
plt.close("all")
elif isinstance(network, scgen.VAEArith):
if sparse.issparse(train.X):
latent = network.to_latent(train.X.A)
else:
latent = network.to_latent(train.X)
latent = sc.AnnData(X=latent,
obs={
condition_key:
train.obs[condition_key].tolist(),
cell_type_key:
train.obs[cell_type_key].tolist()
})
if plot_umap:
sc.pp.neighbors(latent)
sc.tl.umap(latent)
sc.pl.umap(latent,
color=[condition_key, cell_type_key],
save=f"_latent",
show=False)
cell_type_data = train[train.obs[cell_type_key] == cell_type]
pred, delta = network.predict(adata=cell_type_data,
conditions=conditions,
cell_type_key=cell_type_key,
condition_key=condition_key,
celltype_to_predict=cell_type)
pred_adata = anndata.AnnData(
pred,
obs={condition_key: ["pred"] * len(pred)},
var={"var_names": cell_type_data.var_names})
all_adata = cell_type_data.concatenate(pred_adata)
sc.tl.rank_genes_groups(cell_type_data,
groupby=condition_key,
n_genes=100)
diff_genes = cell_type_data.uns["rank_genes_groups"]["names"][
conditions["stim"]]
if plot_reg:
scgen.plotting.reg_mean_plot(all_adata,
condition_key=condition_key,
axis_keys={
"x": "pred",
"y": conditions["stim"]
},
gene_list=diff_genes[:5],
path_to_save=os.path.join(
path_to_save,
f"reg_mean_all_genes.pdf"))
scgen.plotting.reg_var_plot(all_adata,
condition_key=condition_key,
axis_keys={
"x": "pred",
"y": conditions["stim"]
},
gene_list=diff_genes[:5],
path_to_save=os.path.join(
path_to_save,
f"reg_var_all_genes.pdf"))
all_adata_top_100_genes = all_adata.copy()[:, diff_genes.tolist()]
scgen.plotting.reg_mean_plot(all_adata_top_100_genes,
condition_key=condition_key,
axis_keys={
"x": "pred",
"y": conditions["stim"]
},
gene_list=diff_genes[:5],
path_to_save=os.path.join(
path_to_save,
f"reg_mean_top_100_genes.pdf"))
scgen.plotting.reg_var_plot(all_adata_top_100_genes,
condition_key=condition_key,
axis_keys={
"x": "pred",
"y": conditions["stim"]
},
gene_list=diff_genes[:5],
path_to_save=os.path.join(
path_to_save,
f"reg_var_top_100_genes.pdf"))
all_adata_top_50_genes = all_adata.copy()[:,
diff_genes.tolist()[:50]]
scgen.plotting.reg_mean_plot(all_adata_top_50_genes,
condition_key=condition_key,
axis_keys={
"x": "pred",
"y": conditions["stim"]
},
gene_list=diff_genes[:5],
path_to_save=os.path.join(
path_to_save,
f"reg_mean_top_50_genes.pdf"))
scgen.plotting.reg_var_plot(all_adata_top_50_genes,
condition_key=condition_key,
axis_keys={
"x": "pred",
"y": conditions["stim"]
},
gene_list=diff_genes[:5],
path_to_save=os.path.join(
path_to_save,
f"reg_var_top_50_genes.pdf"))
if plot_umap:
sc.pp.neighbors(all_adata)
sc.tl.umap(all_adata)
sc.pl.umap(all_adata,
color=condition_key,
save="pred_all_genes",
show=False)
sc.pp.neighbors(all_adata_top_100_genes)
sc.tl.umap(all_adata_top_100_genes)
sc.pl.umap(all_adata_top_100_genes,
color=condition_key,
save="pred_top_100_genes",
show=False)
sc.pp.neighbors(all_adata_top_50_genes)
sc.tl.umap(all_adata_top_50_genes)
sc.pl.umap(all_adata_top_50_genes,
color=condition_key,
save="pred_top_50_genes",
show=False)
sc.pl.violin(all_adata,
keys=diff_genes.tolist()[0],
groupby=condition_key,
save=f"_{diff_genes.tolist()[0]}",
show=False)
plt.close("all")
elif isinstance(network, scgen.CVAE):
true_labels, _ = scgen.label_encoder(train)
if sparse.issparse(train.X):
latent = network.to_latent(train.X.A, labels=true_labels)
else:
latent = network.to_latent(train.X, labels=true_labels)
latent = sc.AnnData(X=latent,
obs={
condition_key:
train.obs[condition_key].tolist(),
cell_type_key:
train.obs[cell_type_key].tolist()
})
if plot_umap:
sc.pp.neighbors(latent)
sc.tl.umap(latent)
sc.pl.umap(latent,
color=[condition_key, cell_type_key],
save=f"_latent",
show=False)
cell_type_data = train[train.obs[cell_type_key] == cell_type]
fake_labels = np.ones(shape=(cell_type_data.shape[0], 1))
pred = network.predict(data=cell_type_data, labels=fake_labels)
pred_adata = anndata.AnnData(
pred,
obs={condition_key: ["pred"] * len(pred)},
var={"var_names": cell_type_data.var_names})
all_adata = cell_type_data.concatenate(pred_adata)
sc.tl.rank_genes_groups(cell_type_data,
groupby=condition_key,
n_genes=100)
diff_genes = cell_type_data.uns["rank_genes_groups"]["names"][
conditions["stim"]]
if plot_reg:
scgen.plotting.reg_mean_plot(all_adata,
condition_key=condition_key,
axis_keys={
"x": "pred",
"y": conditions["stim"]
},
gene_list=diff_genes[:5],
path_to_save=os.path.join(
path_to_save,
f"reg_mean_all_genes.pdf"))
scgen.plotting.reg_var_plot(all_adata,
condition_key=condition_key,
axis_keys={
"x": "pred",
"y": conditions["stim"]
},
gene_list=diff_genes[:5],
path_to_save=os.path.join(
path_to_save,
f"reg_var_all_genes.pdf"))
all_adata_top_100_genes = all_adata.copy()[:, diff_genes.tolist()]
scgen.plotting.reg_mean_plot(all_adata_top_100_genes,
condition_key=condition_key,
axis_keys={
"x": "pred",
"y": conditions["stim"]
},
gene_list=diff_genes[:5],
path_to_save=os.path.join(
path_to_save,
f"reg_mean_top_100_genes.pdf"))
scgen.plotting.reg_var_plot(all_adata_top_100_genes,
condition_key=condition_key,
axis_keys={
"x": "pred",
"y": conditions["stim"]
},
gene_list=diff_genes[:5],
path_to_save=os.path.join(
path_to_save,
f"reg_var_top_100_genes.pdf"))
all_adata_top_50_genes = all_adata.copy()[:,
diff_genes.tolist()[:50]]
scgen.plotting.reg_mean_plot(all_adata_top_50_genes,
condition_key=condition_key,
axis_keys={
"x": "pred",
"y": conditions["stim"]
},
gene_list=diff_genes[:5],
path_to_save=os.path.join(
path_to_save,
f"reg_mean_top_50_genes.pdf"))
scgen.plotting.reg_var_plot(all_adata_top_50_genes,
condition_key=condition_key,
axis_keys={
"x": "pred",
"y": conditions["stim"]
},
gene_list=diff_genes[:5],
path_to_save=os.path.join(
path_to_save,
f"reg_var_top_50_genes.pdf"))
if plot_umap:
sc.pp.neighbors(all_adata)
sc.tl.umap(all_adata)
sc.pl.umap(all_adata,
color=condition_key,
save="pred_all_genes",
show=False)
sc.pp.neighbors(all_adata_top_100_genes)
sc.tl.umap(all_adata_top_100_genes)
sc.pl.umap(all_adata_top_100_genes,
color=condition_key,
save="pred_top_100_genes",
show=False)
sc.pp.neighbors(all_adata_top_50_genes)
sc.tl.umap(all_adata_top_50_genes)
sc.pl.umap(all_adata_top_50_genes,
color=condition_key,
save="pred_top_50_genes",
show=False)
sc.pl.violin(all_adata,
keys=diff_genes.tolist()[0],
groupby=condition_key,
save=f"_{diff_genes.tolist()[0]}",
show=False)
plt.close("all")
import scgen
import scanpy as sc
import numpy as np
train = sc.read("./data/train.h5ad")
# train = train[train.obs["cell_type"] == "CD4T"]
train = train[~((train.obs["cell_type"] == "CD4T") &
(train.obs["condition"] == "stimulated"))]
z_dim = 20
network = scgen.CVAE(x_dimension=train.X.shape[1],
z_dimension=z_dim,
alpha=0.1)
network.restore_model()
# network.train(train, n_epochs=100)
labels, _ = scgen.label_encoder(train)
latent = network.to_latent(train.X.A, labels=labels)
adata = sc.AnnData(X=latent,
obs={
"condition": train.obs["condition"].tolist(),
"cell_type": train.obs["cell_type"].tolist()
})
sc.pp.neighbors(adata)
sc.tl.umap(adata)
sc.pl.umap(adata, color=["condition", "cell_type"], save=f"train_{z_dim}")
mmd = network.to_mmd_layer(train.X.A, labels=labels)
adata_mmd = sc.AnnData(X=mmd,
obs={
"condition": train.obs["condition"].tolist(),
"cell_type": train.obs["cell_type"].tolist()
})
