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Update test_patient_specific_modeling() #28

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Apr 30, 2022
Merged

Update test_patient_specific_modeling() #28

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6608c51
Sample model from TNBC_ID
himoto Apr 27, 2022
68d8cd9
Update test_patient_model_simulations
himoto Apr 27, 2022
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47 changes: 27 additions & 20 deletions tests/test_patient_specific_modeling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
import shutil
import sys
import time
from typing import Callable, List
from typing import Callable, List, Optional

import numpy as np
from pasmopy import Model, PatientModelAnalyses, PatientModelSimulations, Text2Model
Expand Down Expand Up @@ -45,7 +45,7 @@ def test_model_construction():
from models import erbb_network
except ImportError:
print("can't import erbb_network from models.")

model = Model(erbb_network.__package__).create()
# add weighting factors
gene_expression = {
Expand Down Expand Up @@ -134,7 +134,12 @@ def test_model_construction():
assert len(model.problem.idx_params) + len(model.problem.idx_initials) == 221


def test_patient_model_simulations():
def test_patient_model_simulations(
n_patients: int = 3,
dynamical_feature: Optional[List[str]] = None,
):
if not 1<= n_patients <= 6:
raise ValueError("`n_patients` must be lie within [1, 6].")
# Initialization
for patient in TCGA_ID:
if patient in os.listdir(PATH_TO_MODELS) and patient != "TCGA_3C_AALK_01A":
Expand All @@ -160,32 +165,34 @@ def test_patient_model_simulations():
if patient != "TCGA_3C_AALK_01A":
shutil.copytree(path_to_patient("TCGA_3C_AALK_01A"), path_to_patient(f"{patient}"))
# Execute patient-specific models
simulations = PatientModelSimulations(models.breast.__package__, random.sample(TCGA_ID, 3))
simulations = PatientModelSimulations(
models.breast.__package__,
random.sample(TNBC_ID, n_patients),
)
start = time.time()
assert simulations.run() is None
elapsed = time.time() - start
print(f"Computation time for simulating 3 patients: {elapsed/60:.1f} [min]")
print(f"Computation time for simulating {n_patients} patients: {elapsed/60:.1f} [min]")
# Add new response characteristics
get_droprate: Callable[[np.ndarray], float] = (
lambda time_course: -(time_course[-1] - np.max(time_course)) / (len(time_course) - np.argmax(time_course))
)
simulations.response_characteristics["droprate"] = get_droprate
# Extract response characteristics and visualize patient classification
try:
simulations.subtyping(
"subtype_classification.pdf",
{
"Phosphorylated_Akt": {"EGF": ["AUC", "droprate"], "HRG": ["AUC", "droprate"]},
"Phosphorylated_ERK": {"EGF": ["AUC", "droprate"], "HRG": ["AUC", "droprate"]},
"Phosphorylated_c-Myc": {"EGF": ["AUC", "droprate"], "HRG": ["AUC", "droprate"]},
},
)
obs_names = ["Phosphorylated_Akt", "Phosphorylated_ERK", "Phosphorylated_c-Myc"]
for observable in obs_names:
assert os.path.isfile(os.path.join("classification", f"{observable}.csv"))
assert os.path.isfile("subtype_classification.pdf")
except ValueError:
pass
if dynamical_feature is None:
dynamical_feature = ["AUC", "droprate"]
simulations.subtyping(
"subtype_classification.pdf",
{
"Phosphorylated_Akt": {"EGF": dynamical_feature, "HRG": dynamical_feature},
"Phosphorylated_ERK": {"EGF": dynamical_feature, "HRG": dynamical_feature},
"Phosphorylated_c-Myc": {"EGF": dynamical_feature, "HRG": dynamical_feature},
},
)
obs_names = ["Phosphorylated_Akt", "Phosphorylated_ERK", "Phosphorylated_c-Myc"]
for observable in obs_names:
assert os.path.isfile(os.path.join("classification", f"{observable}.csv"))
assert os.path.isfile("subtype_classification.pdf")


def test_patient_model_analyses():
Expand Down