def test_split_metagene(pretrained_toy_model, toydata):
r"""Test that metagenes are split correctly"""
st_experiment = pretrained_toy_model.get_experiment("ST")
metagene = next(iter(st_experiment.metagenes.keys()))
metagene_new = st_experiment.split_metagene(metagene)
with Session(
model=pretrained_toy_model,
dataloader=toydata,
covariates=extract_covariates(toydata.dataset.data.design),
):
x = to_device(next(iter(toydata)))
with pyro.poutine.trace() as guide_tr:
get("model").guide(x)
with pyro.poutine.trace() as model_tr:
with pyro.poutine.replay(trace=guide_tr.trace):
get("model").model(x)
rim_mean = model_tr.trace.nodes["rim"]["fn"].mean
assert (rim_mean[0, 0] == rim_mean[-1][0, -1]).all()
rate_mg = guide_tr.trace.nodes[_encode_metagene_name(metagene)]["fn"].mean
rate_mg_new = guide_tr.trace.nodes[_encode_metagene_name(
metagene_new)]["fn"].mean
assert (rate_mg == rate_mg_new).all()
def test_compute_differential_expression(
pretrained_toy_model, toydata, tmp_path
):
with Session(
model=pretrained_toy_model,
genes=toydata.dataset.genes,
dataloader=toydata,
covariates=extract_covariates(toydata.dataset.data.design),
save_path=tmp_path,
eval=True,
):
compute_differential_expression("annotation1", "annotation2")
assert os.path.exists(tmp_path / "differential_expression" / "data.csv.gz")
def test_metagenes(pretrained_toy_model, toydata, tmp_path):
with Session(
model=pretrained_toy_model,
genes=toydata.dataset.genes,
dataloader=toydata,
covariates=extract_covariates(toydata.dataset.data.design),
save_path=tmp_path,
eval=True,
):
compute_metagene_summary()
for section in map(os.path.basename, toydata.dataset.data.design):
assert os.path.exists(tmp_path / "metagenes" / section /
f"summary.png")
def test_gene_maps(pretrained_toy_model, toydata, tmp_path):
with Session(
model=pretrained_toy_model,
genes=toydata.dataset.genes,
dataloader=toydata,
covariates=extract_covariates(toydata.dataset.data.design),
save_path=tmp_path,
eval=True,
):
compute_gene_maps()
for section in toydata.dataset.data.design:
for gene in toydata.dataset.genes:
assert os.path.exists(
tmp_path / "gene_maps" / section / f"{gene}.png"
)
def pretrained_toy_model(toydata):
r"""Pretrained toy model"""
# pylint: disable=redefined-outer-name
st_experiment = ST(
depth=2,
num_channels=4,
metagenes=[MetageneDefault(0.0, None) for _ in range(1)],
)
xfuse = XFuse(experiments=[st_experiment])
with Session(
model=xfuse,
optimizer=pyro.optim.Adam({"lr": 0.001}),
dataloader=toydata,
covariates=extract_covariates(toydata.dataset.data.design),
):
train(100 + get("training_data").epoch)
return xfuse
def test_toydata(mocker, toydata):
r"""Integration test on toy dataset"""
st_experiment = ST(
depth=2,
num_channels=4,
metagenes=[MetageneDefault(0.0, None) for _ in range(3)],
)
xfuse = XFuse(experiments=[st_experiment])
rmse = RMSE()
mock_log_scalar = mocker.patch("xfuse.messengers.stats.rmse.log_scalar")
with Session(
model=xfuse,
optimizer=pyro.optim.Adam({"lr": 0.0001}),
dataloader=toydata,
covariates=extract_covariates(toydata.dataset.data.design),
messengers=[rmse],
):
train(100 + get("training_data").epoch)
rmses = [x[1][1] for x in mock_log_scalar.mock_calls]
assert rmses[-1] < 6.0
def test_metagene_expansion(
# pylint: disable=redefined-outer-name
toydata,
pretrained_toy_model,
expansion_strategies,
compute_expected_metagenes,
):
r"""Test metagene expansion dynamics"""
st_experiment = pretrained_toy_model.get_experiment("ST")
num_start_metagenes = len(st_experiment.metagenes)
for expansion_strategy, expected_metagenes in zip(
expansion_strategies, compute_expected_metagenes(num_start_metagenes)
):
with Session(
covariates=extract_covariates(toydata.dataset.data.design),
dataloader=toydata,
metagene_expansion_strategy=expansion_strategy,
model=pretrained_toy_model,
):
purge_metagenes(num_samples=10)
assert len(st_experiment.metagenes) == expected_metagenes
def test_compute_imputation(pretrained_toy_model, toydata, tmp_path):
with Session(
model=pretrained_toy_model,
genes=toydata.dataset.genes,
dataloader=toydata,
covariates=extract_covariates(toydata.dataset.data.design),
save_path=tmp_path,
eval=True,
):
compute_imputation("annotation1")
for name, slide in toydata.dataset.data.slides.items():
name = os.path.basename(name)
output_file = (tmp_path / "imputation-annotation1" / name /
"imputed_counts.csv.gz")
assert os.path.exists(output_file)
output_data = pd.read_csv(output_file)
output_data_labels = np.unique(output_data.label)
output_data_labels = np.sort(output_data_labels)
annotation_labels = np.unique(slide.data.annotation("annotation1"))
annotation_labels = np.sort(annotation_labels[annotation_labels > 0])
assert (annotation_labels == output_data_labels).all()