def test_anova_summary():
an = ANOVA(ic50_test)
# by default regression includes + msi + feature
drug_id = 999
df = an.anova_one_drug_one_feature(drug_id, 'ASH1L_mut')
x = an.anova_pvalues
x = [x["tissue"], x["msi"], x["feature"]]
y = [3.210453608523738e-06, 0.14579091345305398, 0.5430736275249095]
assert_list_almost_equal(x, y, deltas=1e-10)
an.settings.analysis_type = 'COREAD' # something different from PANCAN
df = an.anova_one_drug_one_feature(drug_id, 'ASH1L_mut')
x = an.anova_pvalues
x = [x["msi"], x["feature"]]
y = [0.262294448831941, 0.30599483315087317]
assert_list_almost_equal(x, y, deltas=1e-10)
# now remove also the MSI factor, in which case the tissue must also be
# removed !
an.settings.include_MSI_factor = False
an.settings.analysis_type = "COREAD"
df = an.anova_one_drug_one_feature(drug_id, 'ASH1L_mut')
x = [an.anova_pvalues["feature"]]
y = [0.21266050833611852]
assert_list_almost_equal(x, y, deltas=1e-10)
assert (df.N_FEATURE_neg == 365).all()
def test_anova_summary():
an = ANOVA(ic50_test)
# by default regression includes + msi + feature
drug_id = 999
df = an.anova_one_drug_one_feature(drug_id, "ASH1L_mut")
x = an.anova_pvalues
x = [x["tissue"], x["msi"], x["feature"]]
y = [3.210453608523738e-06, 0.14579091345305398, 0.5430736275249095]
assert_list_almost_equal(x, y, deltas=1e-10)
an.settings.analysis_type = "COREAD" # something different from PANCAN
df = an.anova_one_drug_one_feature(drug_id, "ASH1L_mut")
x = an.anova_pvalues
x = [x["msi"], x["feature"]]
y = [0.262294448831941, 0.30599483315087317]
assert_list_almost_equal(x, y, deltas=1e-10)
# now remove also the MSI factor, in which case the tissue must also be
# removed !
an.settings.include_MSI_factor = False
an.settings.analysis_type = "COREAD"
df = an.anova_one_drug_one_feature(drug_id, "ASH1L_mut")
x = [an.anova_pvalues["feature"]]
y = [0.21266050833611852]
assert_list_almost_equal(x, y, deltas=1e-10)
assert (df.N_FEATURE_neg == 365).all()
def test_anova_summary():
an = ANOVA(ic50_test)
# by default regression includes + msi + feature
drug_id = 'Drug_999_IC50'
df = an.anova_one_drug_one_feature(drug_id, 'ASH1L_mut')
x = an.anova_pvalues
y = [3.210453608523738e-06, 0.14579091345305398, 0.5430736275249095, None]
assert_list_almost_equal(x, y)
an.settings.analysis_type = 'COREAD' # something different from PANCAN
df = an.anova_one_drug_one_feature(drug_id, 'ASH1L_mut')
x = an.anova_pvalues
y = [0.262294448831941, 0.30599483315087317, None]
assert_list_almost_equal(x, y)
# now remove also the MSI factor
an.settings.include_MSI_factor = False
df = an.anova_one_drug_one_feature(drug_id, 'ASH1L_mut')
x = an.anova_pvalues
y = [0.21266050833611852, None]
assert_list_almost_equal(x, y)
assert (df.N_FEATURE_neg == 365).all()
def test_anova_summary():
an = ANOVA(ic50_test)
# by default regression includes + msi + feature
drug_id = 'Drug_999_IC50'
df = an.anova_one_drug_one_feature(drug_id, 'ASH1L_mut')
x = an.anova_pvalues
y = [3.210453608523738e-06, 0.14579091345305398, 0.5430736275249095, None]
assert_list_almost_equal(x, y)
an.settings.analysis_type = 'COREAD' # something different from PANCAN
df = an.anova_one_drug_one_feature(drug_id, 'ASH1L_mut')
x = an.anova_pvalues
y = [0.262294448831941, 0.30599483315087317, None]
assert_list_almost_equal(x, y)
# now remove also the MSI factor
an.settings.include_MSI_factor = False
df = an.anova_one_drug_one_feature(drug_id, 'ASH1L_mut')
x = an.anova_pvalues
y = [0.21266050833611852, None]
assert_list_almost_equal(x, y)
assert (df.N_FEATURE_neg == 365).all()
def test_odof_with_without_media():
gdsc = ANOVA(ic50_test)
_res = gdsc.anova_one_drug_one_feature('Drug_1047_IC50', 'TP53_mut')
dd1 = gdsc._get_anova_summary(gdsc.data_lm, output='dict')
assert_list_almost_equal([dd1['feature'], dd1['msi'], dd1['tissue']],
[1.5750735472022118e-58, 0.025902887791637515,
5.541879283763767e-44])
gdsc = ANOVA(ic50_test, set_media_factor=True)
_res = gdsc.anova_one_drug_one_feature('Drug_1047_IC50', 'TP53_mut')
dd2 = gdsc._get_anova_summary(gdsc.data_lm, output='dict')
assert_list_almost_equal([dd2['feature'], dd2['media'], dd2['msi'],
dd2['tissue']], [ 2.9236500715529455e-58, 0.7762487502315283,
0.023777744527686766, 1.5729157319290974e-44])
def test_anova_one_drug_one_feature():
an = ANOVA(ic50_test)
# test 1 drug
drug_id = 'Drug_999_IC50'
df = an.anova_one_drug_one_feature(
drug_id=drug_id,
feature_name='ABCB1_mut', show=True)
control = {'DRUG_ID': {1: drug_id},
'DRUG_NAME': {1: drug_id},
'DRUG_TARGET': {1: drug_id},
'FEATURE': {1: 'ABCB1_mut'},
'ANOVA_FEATURE_pval': {1: 0.86842684367357359},
'FEATURE_IC50_T_pval': {1: 0.48586107208790896},
'FEATURE_IC50_effect_size': {1: 0.31407773405409201},
'FEATURE_delta_MEAN_IC50': {1: -0.36105662590553411},
'FEATUREneg_Glass_delta': {1: 0.31296252976074801},
'FEATUREneg_IC50_sd': {1: 1.1536736560173895},
'FEATUREneg_logIC50_MEAN': {1: 2.8007757068403043},
'FEATUREpos_Glass_delta': {1: 0.53754504818376181},
'FEATUREpos_IC50_sd': {1: 0.67167696386648634},
'FEATUREpos_logIC50_MEAN': {1: 2.4397190809347702},
'ANOVA_MSI_pval': {1: 0.14598946672374763},
'N_FEATURE_neg': {1: 370},
'N_FEATURE_pos': {1: 5},
'ANOVA_TISSUE_pval': {1: 3.2808255732569986e-06}}
control = pd.DataFrame(control)
assert_list_almost_equal(df,control)
def test_compare_formula_vs_gdsc():
# TISSUE + MSI + feature
an = ANOVA(ic50_test)
drug_id = 999
df1 = an.anova_one_drug_one_feature(drug_id=drug_id, feature_name="ABCB1_mut")
an.settings.regression_formula = "Y ~ C(tissue) + C(msi) + feature"
df2 = an.anova_one_drug_one_feature(drug_id=drug_id, feature_name="ABCB1_mut")
assert all(df1.fillna(1).values[0] == df2.fillna(1).values[0])
# MSI + feature only
an = ANOVA(ic50_test)
an.settings.analysis_type = "breast"
df1 = an.anova_one_drug_one_feature(drug_id=drug_id, feature_name="ABCB1_mut")
an.settings.regression_formula = "Y ~ C(msi) + feature"
df2 = an.anova_one_drug_one_feature(an.drugIds[2], an.feature_names[0])
assert all(df1.fillna(1) == df2.fillna(1))
def test_odof_with_without_media():
gdsc = ANOVA(ic50_test)
_res = gdsc.anova_one_drug_one_feature('Drug_1047_IC50', 'TP53_mut')
dd1 = gdsc._get_anova_summary(gdsc.data_lm, output='dict')
assert_list_almost_equal(
[dd1['feature'], dd1['msi'], dd1['tissue']],
[1.5750735472022118e-58, 0.025902887791637515, 5.541879283763767e-44])
gdsc = ANOVA(ic50_test, set_media_factor=True)
_res = gdsc.anova_one_drug_one_feature('Drug_1047_IC50', 'TP53_mut')
dd2 = gdsc._get_anova_summary(gdsc.data_lm, output='dict')
assert_list_almost_equal(
[dd2['feature'], dd2['media'], dd2['msi'], dd2['tissue']], [
2.9236500715529455e-58, 0.7762487502315283, 0.023777744527686766,
1.5729157319290974e-44
])
def test_compare_formula_vs_gdsc():
# TISSUE + MSI + feature
an = ANOVA(ic50_test)
drug_id = 999
df1 = an.anova_one_drug_one_feature(drug_id=drug_id,
feature_name='ABCB1_mut')
an.settings.regression_formula = "Y ~ C(tissue) + C(msi) + feature"
df2 = an.anova_one_drug_one_feature(drug_id=drug_id,
feature_name='ABCB1_mut')
assert all(df1.fillna(1).values[0] == df2.fillna(1).values[0])
# MSI + feature only
an = ANOVA(ic50_test)
an.settings.analysis_type = "breast"
df1 = an.anova_one_drug_one_feature(drug_id=drug_id,
feature_name='ABCB1_mut')
an.settings.regression_formula = "Y ~ C(msi) + feature"
df2 = an.anova_one_drug_one_feature(an.drugIds[2], an.feature_names[0])
assert all(df1.fillna(1) == df2.fillna(1))
def test_odof_with_without_media():
gdsc = ANOVA(ic50_test)
_res = gdsc.anova_one_drug_one_feature(1047, 'TP53_mut')
odof = gdsc._get_one_drug_one_feature_data(1047, 'TP53_mut')
dd1 = gdsc._get_anova_summary(gdsc.data_lm, output='dict', odof=odof)
assert_almost_equal(dd1['feature'] - 1.5750735472022118e-58, delta=1e-64)
assert_almost_equal(dd1['msi'] - 0.025902887791637515, delta=1e-10)
# Change in Nov 2016
#assert_almost_equal(dd1['tissue'], 5.541879283763767e-44, delta=1e-50)
assert_almost_equal(dd1['tissue'] - 1.0258702741509e-44, delta=1e-50)
gdsc = ANOVA(ic50_test, set_media_factor=True)
_res = gdsc.anova_one_drug_one_feature(1047, 'TP53_mut')
odof = gdsc._get_one_drug_one_feature_data(1047, 'TP53_mut')
dd2 = gdsc._get_anova_summary(gdsc.data_lm, output='dict', odof=odof)
assert_almost_equal(dd2['feature'] - 2.9236500715529455e-58, delta=1e-64)
assert_almost_equal(dd2['media'] - 0.7762487502315283, delta=1e-10)
assert_almost_equal(dd2['msi'] - 0.023777744527686766, delta=1e-10)
assert_almost_equal(dd2['tissue'] - 1.5729157319290974e-44, delta=1e-50)
def test_odof_with_without_media():
gdsc = ANOVA(ic50_test)
_res = gdsc.anova_one_drug_one_feature(1047, "TP53_mut")
odof = gdsc._get_one_drug_one_feature_data(1047, "TP53_mut")
dd1 = gdsc._get_anova_summary(gdsc.data_lm, output="dict", odof=odof)
assert_almost_equal(dd1["feature"], 1.5750735472022118e-58, delta=1e-64)
assert_almost_equal(dd1["msi"], 0.025902887791637515, delta=1e-10)
# Change in Nov 2016
# assert_almost_equal(dd1['tissue'], 5.541879283763767e-44, delta=1e-50)
assert_almost_equal(dd1["tissue"], 1.0258702741509e-44, delta=1e-50)
gdsc = ANOVA(ic50_test, set_media_factor=True)
_res = gdsc.anova_one_drug_one_feature(1047, "TP53_mut")
odof = gdsc._get_one_drug_one_feature_data(1047, "TP53_mut")
dd2 = gdsc._get_anova_summary(gdsc.data_lm, output="dict", odof=odof)
assert_almost_equal(dd2["feature"], 2.9236500715529455e-58, delta=1e-64)
assert_almost_equal(dd2["media"], 0.7762487502315283, delta=1e-10)
assert_almost_equal(dd2["msi"], 0.023777744527686766, delta=1e-10)
assert_almost_equal(dd2["tissue"], 1.5729157319290974e-44, delta=1e-50)
def test_anova_one_drug_one_feature():
an = ANOVA(ic50_test)
# test 1 drug
drug_id = 999
df = an.anova_one_drug_one_feature(drug_id=drug_id, feature_name="ABCB1_mut", show=True)
df["DRUG_ID"] = drug_id
df["DRUG_NAME"] = drug_id
df["DRUG_TARGET"] = drug_id
df["ANOVA_MEDIA_pval"] = -1
control = {
"DRUG_ID": {1: drug_id},
"DRUG_NAME": {1: drug_id},
"DRUG_TARGET": {1: drug_id},
"FEATURE": {1: "ABCB1_mut"},
"ANOVA_FEATURE_pval": {1: 0.86842684367357359},
"FEATURE_IC50_T_pval": {1: 0.48586107208790896},
"FEATURE_IC50_effect_size": {1: 0.31407773405409201},
"FEATURE_delta_MEAN_IC50": {1: -0.36105662590553411},
# 'FEATUREneg_Glass_delta': {1: 0.31296252976074801},
# 'FEATUREneg_IC50_sd': {1: 1.1536736560173895},
"FEATUREneg_logIC50_MEAN": {1: 2.8007757068403043},
# 'FEATUREpos_Glass_delta': {1: 0.53754504818376181},
# 'FEATUREpos_IC50_sd': {1: 0.67167696386648634},
"FEATUREpos_logIC50_MEAN": {1: 2.4397190809347702},
"ANOVA_MSI_pval": {1: 0.14598946672374763},
"N_FEATURE_neg": {1: 370},
"N_FEATURE_pos": {1: 5},
"ANOVA_MEDIA_pval": {1: -1},
"ANOVA_TISSUE_pval": {1: 3.2808255732569986e-06},
}
control = pd.DataFrame(control)
for this in control.columns:
if this in ["FEATURE_delta_MEAN_IC50", "FEATUREneg_logIC50_MEAN", "FEATUREpos_logIC50_MEAN"]:
pass
else:
assert_almost_equal(df[this].values[0], control[this].values[0])
def test_anova_one_drug_one_feature():
an = ANOVA(ic50_test)
# test 1 drug
drug_id = 999
df = an.anova_one_drug_one_feature(drug_id=drug_id,
feature_name='ABCB1_mut',
show=True)
df['DRUG_ID'] = drug_id
df['DRUG_NAME'] = drug_id
df['DRUG_TARGET'] = drug_id
df['ANOVA_MEDIA_pval'] = -1
control = {
'DRUG_ID': {
1: drug_id
},
'DRUG_NAME': {
1: drug_id
},
'DRUG_TARGET': {
1: drug_id
},
'FEATURE': {
1: 'ABCB1_mut'
},
'ANOVA_FEATURE_pval': {
1: 0.86842684367357359
},
'FEATURE_IC50_T_pval': {
1: 0.48586107208790896
},
'FEATURE_IC50_effect_size': {
1: 0.31407773405409201
},
'FEATURE_delta_MEAN_IC50': {
1: -0.36105662590553411
},
# 'FEATUREneg_Glass_delta': {1: 0.31296252976074801},
# 'FEATUREneg_IC50_sd': {1: 1.1536736560173895},
'FEATUREneg_logIC50_MEAN': {
1: 2.8007757068403043
},
# 'FEATUREpos_Glass_delta': {1: 0.53754504818376181},
# 'FEATUREpos_IC50_sd': {1: 0.67167696386648634},
'FEATUREpos_logIC50_MEAN': {
1: 2.4397190809347702
},
'ANOVA_MSI_pval': {
1: 0.14598946672374763
},
'N_FEATURE_neg': {
1: 370
},
'N_FEATURE_pos': {
1: 5
},
'ANOVA_MEDIA_pval': {
1: -1
},
'ANOVA_TISSUE_pval': {
1: 3.2808255732569986e-06
}
}
control = pd.DataFrame(control)
for this in control.columns:
if this in [
"FEATURE_delta_MEAN_IC50", "FEATUREneg_logIC50_MEAN",
"FEATUREpos_logIC50_MEAN"
]:
pass
else:
assert_almost_equal(df[this].values[0], control[this].values[0])
