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fixed pytests for new handling of gene lists
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@JudithBernett
JudithBernett committed Oct 7, 2024
1 parent 2138142 commit 55d3915
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4 changes: 3 additions & 1 deletion drevalpy/models/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,9 @@
"MultiOmicsNeuralNetwork",
"MultiOmicsRandomForest",
"SingleDrugRandomForest",
"SRMF" "MULTI_DRUG_MODEL_FACTORY",
"SRMF",
"GradientBoosting",
"MULTI_DRUG_MODEL_FACTORY",
"SINGLE_DRUG_MODEL_FACTORY",
"MODEL_FACTORY",
]
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10 changes: 6 additions & 4 deletions tests/individual_models/test_baselines.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
import pytest
import numpy as np
import math
from sklearn.linear_model import Ridge, ElasticNet

from drevalpy.evaluation import evaluate, pearson
Expand Down Expand Up @@ -204,12 +205,13 @@ def call_other_baselines(
)
if model == "ElasticNet" and hpam_combi["l1_ratio"] == 1.0:
# TODO: Why is this happening? Investigate
assert metrics["Pearson"] == 0.0
assert math.isclose(metrics["Pearson"], 0.0, abs_tol=1e-2)
elif model == "ElasticNet" and hpam_combi["l1_ratio"] == 0.5:
# TODO: Why so bad? E.g., LPO+l1_ratio=0.5 -> 0.06, LCO+l1_ratio=0.5 -> 0.1, LDO+l1_ratio=0.5 -> 0.23
assert metrics["Pearson"] > 0.0
# TODO: Why so bad? E.g., LPO+l1_ratio=0.5 -> 0.06/-0.07, LCO+l1_ratio=0.5 -> 0.1,
# LDO+l1_ratio=0.5 -> 0.23
assert metrics["Pearson"] > -0.1
elif test_mode == "LDO":
assert metrics["Pearson"] > 0.0
else:
assert metrics["Pearson"] > 0.5
assert metrics["Pearson"] > 0.4
call_save_and_load(model_instance)
2 changes: 1 addition & 1 deletion tests/test_dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -354,7 +354,7 @@ def graph_dataset():


def test_feature_dataset_get_ids(sample_dataset):
assert sample_dataset.get_ids() == ["drug1", "drug2", "drug3", "drug4", "drug5"]
assert np.all(sample_dataset.get_ids() == ["drug1", "drug2", "drug3", "drug4", "drug5"])


def test_feature_dataset_get_view_names(sample_dataset):
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190 changes: 107 additions & 83 deletions tests/test_drp_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,17 +17,19 @@


def test_factory():
assert "SimpleNeuralNetwork" in MODEL_FACTORY
assert "MultiOmicsNeuralNetwork" in MODEL_FACTORY
assert "ElasticNet" in MODEL_FACTORY
assert "RandomForest" in MODEL_FACTORY
assert "MultiOmicsRandomForest" in MODEL_FACTORY
assert "SVR" in MODEL_FACTORY
assert "NaivePredictor" in MODEL_FACTORY
assert "NaiveDrugMeanPredictor" in MODEL_FACTORY
assert "NaiveCellLineMeanPredictor" in MODEL_FACTORY
assert "ElasticNet" in MODEL_FACTORY
assert "RandomForest" in MODEL_FACTORY
assert "SVR" in MODEL_FACTORY
assert "SimpleNeuralNetwork" in MODEL_FACTORY
assert "MultiOmicsNeuralNetwork" in MODEL_FACTORY
assert "MultiOmicsRandomForest" in MODEL_FACTORY
assert "SingleDrugRandomForest" in MODEL_FACTORY
assert len(MODEL_FACTORY) == 10
assert "SRMF" in MODEL_FACTORY
assert "GradientBoosting" in MODEL_FACTORY
assert len(MODEL_FACTORY) == 12


def test_load_cl_ids_from_csv():
Expand All @@ -50,33 +52,30 @@ def write_gene_list(temp_dir, gene_list):
if gene_list == "landmark_genes":
with open(temp_file, "w") as f:
f.write(
"Entrez ID\tSymbol\tName\tGene Family\tType\tRNA-Seq Correlation\tRNA-Seq Correlation Self-Rank\n"
"3638\tINSIG1\tinsulin induced gene 1\t\tlandmark\t\t\n"
"2309\tFOXO3\tforkhead box O3\tForkhead boxes\tlandmark\t\t\n"
"7105\tTSPAN6\ttetraspanin 6\tTetraspanins\tlandmark\t\t\n"
"57147\tSCYL3\tSCY1 like pseudokinase 3\tSCY1 like pseudokinases\tlandmark\t\t"
'Entrez ID,Symbol,Name,Gene Family,Type,RNA-Seq Correlation,RNA-Seq Correlation Self-Rank\n'
'3638,INSIG1,insulin induced gene 1,,landmark,,\n'
'2309,FOXO3,forkhead box O3,Forkhead boxes,landmark,,\n'
'672,BRCA1,"BRCA1, DNA repair associated","Ring finger proteins, Fanconi anemia complementation groups, Protein phosphatase 1 regulatory subunits, BRCA1 A complex, BRCA1 B complex, BRCA1 C complex",landmark,,\n'
'57147,SCYL3,SCY1 like pseudokinase 3,SCY1 like pseudokinases,landmark,,'
)
elif gene_list == "drug_target_genes_all_drugs":
with open(temp_file, "w") as f:
f.write(
",Symbol\n"
"0,EGFR\n"
"1,MTOR\n"
"2,KIT\n"
"3,FLT3\n"
"4,RET\n"
"5,BRCA1\n"
"Symbol\n"
"TSPAN6\n"
"SCYL3\n"
"BRCA1\n"
)
elif gene_list == "gene_list_paccmann_network_prop":
with open(temp_file, "w") as f:
f.write(
",Symbol\n"
"0,HDAC1\n"
"1,ALS2CR12\n"
"2,BFAR\n"
"3,ZCWPW1\n"
"4,ZP1\n"
"5,PDZD7"
"Symbol\n"
"HDAC1\n"
"ALS2CR12\n"
"BFAR\n"
"ZCWPW1\n"
"ZP1\n"
"PDZD7"
)


Expand All @@ -95,40 +94,52 @@ def test_load_and_reduce_gene_features(gene_list):
temp_file = os.path.join(temp.name, "GDSC1_small", "gene_expression.csv")
with open(temp_file, "w") as f:
f.write(
"CELL_LINE_NAME,TSPAN6,TNMD,BRCA1,SCYL3,HDAC1\n"
"CAL-120,7.632023171463389,2.9645851205892404,10.3795526353077,3.61479404843988,3.38068143582194\n"
"DMS 114,7.54867116637172,2.77771614989839,11.807341248845802,4.066886747621,3.73248465377029\n"
"CAL-51,8.71233752103624,2.6435077554121,9.88073281995499,3.95622995046262,3.23662007804984\n"
"NCI-H2869,7.79714221650204,2.8179230218265,9.88347076381233,4.0637013909818505,3.55841402145301\n"
"22Rv1,4.8044868436701,2.84812776692645,10.3319941550002,5.14538669275316,3.54519297942073\n"
"CELL_LINE_NAME,TSPAN6,TNMD,BRCA1,SCYL3,HDAC1,INSIG1,FOXO3\n"
"CAL-120,7.632023171463389,2.9645851205892404,10.3795526353077,3.61479404843988,"
"3.38068143582194,7.09344749430946,3.0222634357817597\n"
"DMS 114,7.54867116637172,2.77771614989839,11.807341248845802,4.066886747621,"
"3.73248465377029,2.8016127581695,6.07851099764176\n"
"CAL-51,8.71233752103624,2.6435077554121,9.88073281995499,3.95622995046262,"
"3.23662007804984,11.394340478134598,4.22471584953505\n"
"NCI-H2869,7.79714221650204,2.8179230218265,9.88347076381233,4.0637013909818505,"
"3.55841402145301,8.76055372116888,4.33420904819493\n"
"22Rv1,4.8044868436701,2.84812776692645,10.3319941550002,5.14538669275316,"
"3.54519297942073,3.9337949618623704,2.8629939819029904\n"
)
if gene_list is not None:
write_gene_list(temp, gene_list)

gene_features_gdsc1 = load_and_reduce_gene_features(
"gene_expression", gene_list, temp.name, "GDSC1_small"
)
if gene_list == "gene_list_paccmann_network_prop":
with pytest.raises(ValueError) as valerr:
gene_features_gdsc1 = load_and_reduce_gene_features(
"gene_expression", gene_list, temp.name, "GDSC1_small"
)
else:
gene_features_gdsc1 = load_and_reduce_gene_features(
"gene_expression", gene_list, temp.name, "GDSC1_small"
)
if gene_list is None:
assert len(gene_features_gdsc1.features) == 5
assert len(gene_features_gdsc1.meta_info["gene_expression"]) == 5
assert len(gene_features_gdsc1.meta_info["gene_expression"]) == 7
assert np.all(
gene_features_gdsc1.meta_info["gene_expression"]
== ["TSPAN6", "TNMD", "BRCA1", "SCYL3", "HDAC1"]
== ["TSPAN6", "TNMD", "BRCA1", "SCYL3", "HDAC1", "INSIG1", "FOXO3"]
)
elif gene_list == "landmark_genes":
assert len(gene_features_gdsc1.features) == 5
assert len(gene_features_gdsc1.meta_info["gene_expression"]) == 2
assert len(gene_features_gdsc1.meta_info["gene_expression"]) == 4
colnames = gene_features_gdsc1.meta_info["gene_expression"]
colnames.sort()
assert np.all(colnames == ["SCYL3", "TSPAN6"])
assert np.all(colnames == ["BRCA1", "FOXO3", "INSIG1", "SCYL3"])
elif gene_list == "drug_target_genes_all_drugs":
assert len(gene_features_gdsc1.features) == 5
assert len(gene_features_gdsc1.meta_info["gene_expression"]) == 1
assert np.all(gene_features_gdsc1.meta_info["gene_expression"] == ["BRCA1"])
assert len(gene_features_gdsc1.meta_info["gene_expression"]) == 3
colnames = gene_features_gdsc1.meta_info["gene_expression"]
colnames.sort()
assert np.all(colnames == ["BRCA1", "SCYL3", "TSPAN6"])
elif gene_list == "gene_list_paccmann_network_prop":
assert len(gene_features_gdsc1.features) == 5
assert len(gene_features_gdsc1.meta_info["gene_expression"]) == 1
assert np.all(gene_features_gdsc1.meta_info["gene_expression"] == ["HDAC1"])
assert ("The following genes are missing from the dataset GDSC1_small"
in str(valerr.value))


def test_iterate_features():
Expand Down Expand Up @@ -202,19 +213,26 @@ def test_get_multiomics_feature_dataset(gene_list):
temp_file = os.path.join(temp.name, "GDSC1_small", "gene_expression.csv")
with open(temp_file, "w") as f:
f.write(
"CELL_LINE_NAME,TSPAN6,BRCA1,DPM1,SCYL3,HDAC1\n"
"CAL-120,7.632023171463389,2.9645851205892404,10.3795526353077,3.61479404843988,3.38068143582194\n"
"DMS 114,7.54867116637172,2.77771614989839,11.807341248845802,4.066886747621,3.73248465377029\n"
"CAL-51,8.71233752103624,2.6435077554121,9.88073281995499,3.95622995046262,3.23662007804984\n"
"PFSK-1,7.79714221650204,2.8179230218265,9.88347076381233,4.0637013909818505,3.55841402145301\n"
"22Rv1,4.8044868436701,2.84812776692645,10.3319941550002,5.14538669275316,3.54519297942073\n"
"CELL_LINE_NAME,TSPAN6,TNMD,BRCA1,SCYL3,HDAC1,INSIG1,FOXO3\n"
"CAL-120,7.632023171463389,2.9645851205892404,10.3795526353077,3.61479404843988,"
"3.38068143582194,7.09344749430946,3.0222634357817597\n"
"DMS 114,7.54867116637172,2.77771614989839,11.807341248845802,4.066886747621,"
"3.73248465377029,2.8016127581695,6.07851099764176\n"
"CAL-51,8.71233752103624,2.6435077554121,9.88073281995499,3.95622995046262,"
"3.23662007804984,11.394340478134598,4.22471584953505\n"
"NCI-H2869,7.79714221650204,2.8179230218265,9.88347076381233,4.0637013909818505,"
"3.55841402145301,8.76055372116888,4.33420904819493\n"
"22Rv1,4.8044868436701,2.84812776692645,10.3319941550002,5.14538669275316,"
"3.54519297942073,3.9337949618623704,2.8629939819029904\n"
)

# methylation
temp_file = os.path.join(temp.name, "GDSC1_small", "methylation.csv")
with open(temp_file, "w") as f:
f.write(
"CELL_LINE_NAME,chr1:10003165-10003585,chr1:100315420-100316009,chr1:100435297-100436070,chr1:100503482-100504404,chr1:10057121-10058108\n"
"CELL_LINE_NAME,chr1:10003165-10003585,chr1:100315420-100316009,"
"chr1:100435297-100436070,chr1:100503482-100504404,chr1:10057121-10058108,"
"chr11:107728949-107729586,chr11:107798958-107799980\n"
"22Rv1,0.192212286,0.20381998,0.277913619,0.1909300789999999,0.544058696\n"
"PFSK-1,0.1876026089999999,0.2076517789999999,0.400145531,0.195871473,0.76489757\n"
"CAL-120,0.2101851619999999,0.222116189,0.264730199,0.243298011,0.415484752\n"
Expand All @@ -224,11 +242,11 @@ def test_get_multiomics_feature_dataset(gene_list):
temp_file = os.path.join(temp.name, "GDSC1_small", "mutations.csv")
with open(temp_file, "w") as f:
f.write(
"CELL_LINE_NAME,TSPAN6,BRCA1,DPM1,SCYL3,HDAC1\n"
"201T,False,False,False,False,False\n"
"22Rv1,False,True,False,True,False\n"
"23132/87,False,False,True,True,False\n"
"CAL-120,False,False,False,False,False\n"
"CELL_LINE_NAME,TSPAN6,TNMD,BRCA1,SCYL3,HDAC1,INSIG1,FOXO3\n"
"201T,False,False,False,False,False,True,True\n"
"22Rv1,False,True,False,True,False,False,True\n"
"23132/87,False,False,True,True,False,False,False\n"
"CAL-120,False,False,False,False,False,True,False\n"
)

# copy number variation
Expand All @@ -237,55 +255,61 @@ def test_get_multiomics_feature_dataset(gene_list):
)
with open(temp_file, "w") as f:
f.write(
"CELL_LINE_NAME,TSPAN6,BRCA1,DPM1,SCYL3,HDAC1\n"
"201T,0.0,0.0,-1.0,0.0,0.0\n"
"TE-12,-1.0,-1.0,0.0,1.0,1.0\n"
"CAL-120,0.0,0.0,0.0,-1.0,-1.0\n"
"STS-0421,0.0,0.0,1.0,0.0,0.0\n"
"22Rv1,1.0,1.0,-1.0,1.0,1.0\n"
"CELL_LINE_NAME,TSPAN6,TNMD,BRCA1,SCYL3,HDAC1,INSIG1,FOXO3\n"
"201T,0.0,0.0,-1.0,0.0,0.0,1.0,-1.0\n"
"TE-12,-1.0,-1.0,0.0,1.0,1.0,0.0,0.0\n"
"CAL-120,0.0,0.0,0.0,-1.0,-1.0,1.0,0.0\n"
"STS-0421,0.0,0.0,1.0,0.0,0.0,-1.0,0.0\n"
"22Rv1,1.0,1.0,-1.0,1.0,1.0,1.0,1.0\n"
)
if gene_list is not None:
write_gene_list(temp, gene_list)
dataset = get_multiomics_feature_dataset(
data_path=temp.name, dataset_name="GDSC1_small", gene_list=gene_list
)
assert len(dataset.features) == 2
common_cls = dataset.get_ids()
common_cls.sort()
assert common_cls == ["22Rv1", "CAL-120"]
assert len(dataset.meta_info) == 4
if gene_list == "gene_list_paccmann_network_prop":
with pytest.raises(ValueError) as valerr:
dataset = get_multiomics_feature_dataset(
data_path=temp.name, dataset_name="GDSC1_small", gene_list=gene_list
)
else:
dataset = get_multiomics_feature_dataset(
data_path=temp.name, dataset_name="GDSC1_small", gene_list=gene_list
)
assert len(dataset.features) == 2
common_cls = dataset.get_ids()
common_cls.sort()
assert np.all(common_cls == ["22Rv1", "CAL-120"])
assert len(dataset.meta_info) == 4
if gene_list is None:
assert np.all(
dataset.meta_info["gene_expression"]
== ["TSPAN6", "BRCA1", "DPM1", "SCYL3", "HDAC1"]
== ["TSPAN6", "TNMD", "BRCA1", "SCYL3", "HDAC1", "INSIG1", "FOXO3"]
)
for key in dataset.meta_info:
assert len(dataset.meta_info[key]) == 5
assert len(dataset.meta_info[key]) == 7
elif gene_list == "landmark_genes":
feature_names = []
for key in dataset.meta_info:
if key == "methylation":
assert len(dataset.meta_info[key]) == 5
assert len(dataset.meta_info[key]) == 7
else:
assert len(dataset.meta_info[key]) == 2
assert len(dataset.meta_info[key]) == 4
if len(feature_names) == 0:
feature_names = dataset.meta_info[key]
else:
assert np.all(dataset.meta_info[key] == feature_names)
elif gene_list == "drug_target_genes_all_drugs":
feature_names = []
for key in dataset.meta_info:
if key == "methylation":
assert len(dataset.meta_info[key]) == 5
assert len(dataset.meta_info[key]) == 7
else:
assert len(dataset.meta_info[key]) == 1
assert np.all(dataset.meta_info[key] == ["BRCA1"])
assert len(dataset.meta_info[key]) == 3
if len(feature_names) == 0:
feature_names = dataset.meta_info[key]
else:
assert np.all(dataset.meta_info[key] == feature_names)
elif gene_list == "gene_list_paccmann_network_prop":
for key in dataset.meta_info:
if key == "methylation":
assert len(dataset.meta_info[key]) == 5
else:
assert len(dataset.meta_info[key]) == 1
assert np.all(dataset.meta_info[key] == ["HDAC1"])
assert ("The following genes are missing from the dataset GDSC1_small"
in str(valerr.value))


def test_unique():
Expand Down

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