def test_volcano_plot():
an = ANOVA(ic50_test)
an.features.df = an.features.df[an.features.df.columns[0:10]]
an = ANOVA(ic50_test, genomic_features=an.features.df)
results = an.anova_all()
# try the constructors
v = VolcanoANOVA(results.df)
v = VolcanoANOVA(results)
# the selector metho
v.df = v.selector(v.df)
v.settings.savefig = False
# some of the plotting
v.volcano_plot_all_drugs()
v.volcano_plot_all_features()
v.volcano_plot_all()
v._get_fdr_from_pvalue_interp(1e-10)
v._get_pvalue_from_fdr(50)
v._get_pvalue_from_fdr([50,60])
""" Analyse all associations (drug/feature) ========================================= Volcano plot (all associations) """ ##################################################### # from gdsctools import ANOVA, ic50_test gdsc = ANOVA(ic50_test) results = gdsc.anova_all() results.volcano()
def test_anova_brca():
an1 = ANOVA(gdsctools_data('IC50_v17.csv.gz'))
an1.set_cancer_type('breast')
an = ANOVA(an1.ic50, gdsctools_data('GF_BRCA_v17.csv.gz'))
dfori = an.anova_all()
df = dfori.df.sum()
df = df.drop(['DRUG_TARGET', 'DRUG_NAME', 'DRUG_ID', 'FEATURE'])
df = df.fillna(0)
totest = df.to_dict()
exact = {'ANOVA_FEATURE_FDR': 1133416.7761055394,
'ANOVA_FEATURE_pval': 5824.8201538614458,
'FEATURE_IC50_T_pval': 5824.8201538614449,
'FEATURE_IC50_effect_size': 4408.511449781573,
'FEATURE_delta_MEAN_IC50': 261.11373729866705,
'FEATURE_neg_Glass_delta': 4487.7401723134735,
'FEATURE_neg_IC50_sd': 14701.868130868914,
'FEATURE_neg_logIC50_MEAN': 28701.510736736222,
'FEATURE_pos_Glass_delta': 6536.8938399490198,
'FEATURE_pos_IC50_sd': 13362.588398939894,
'FEATURE_pos_logIC50_MEAN': 28962.624474034845,
'ANOVA_MSI_pval': 0.0,
'N_FEATURE_neg': 439196.0,
'N_FEATURE_pos': 92140.0,
'ANOVA_TISSUE_pval': 0.0,
'ASSOC_ID': 68509365.0,
'index': 68497660.0}
for k, v in totest.items():
if k in ['ANOVA_MEDIA_pval']:
continue
assert_almost_equal(v, exact[k])
# test part of the report (summary section)
r = ANOVAReport(an, dfori)
totest = r.diagnostics().to_dict()
exact = {'text': {0: 'Type of analysis',
1: 'Total number of possible drug/feature associations',
2: 'Total number of ANOVA tests performed',
3: 'Percentage of tests performed',
4: '',
5: 'Total number of tested drugs',
6: 'Total number of genomic features used',
7: 'Total number of screened cell lines',
8: 'MicroSatellite instability included as factor',
9: '',
10: 'Total number of significant associations',
11: ' - sensitive',
12: ' - resistant',
13: 'p-value significance threshold',
14: 'FDR significance threshold',
15: 'Range of significant p-values',
16: 'Range of significant % FDRs'},
'value': {0: 'breast',
1: 13780,
2: 11705,
3: 84.94,
4: '',
5: 265,
6: 52,
7: 51,
8: False,
9: '',
10: 27,
11: 17,
12: 10,
13: 0.001,
14: 25,
15: '[2.098e-09, 0.0004356]',
16: '[0.002456 18.89]'}}
assert totest == exact
import shutil
shutil.rmtree('breast')
""" Analyse all associations (drug/feature) ========================================= Volcano plot (all associations) """ ##################################################### # from gdsctools import ANOVA, ic50_test gdsc = ANOVA(ic50_test) results = gdsc.anova_all() results.volcano()
def test_anova_brca():
an1 = ANOVA(gdsctools_data('IC50_v17.csv.gz'))
an1.set_cancer_type('breast')
an = ANOVA(an1.ic50, gdsctools_data('GF_BRCA_v17.csv.gz'))
dfori = an.anova_all()
df = dfori.df.sum()
df = df.drop(['DRUG_TARGET', 'DRUG_NAME', 'DRUG_ID', 'FEATURE'])
df = df.fillna(0)
totest = df.to_dict()
exact = {
'ANOVA_FEATURE_FDR': 1133416.7761055394,
'ANOVA_FEATURE_pval': 5824.8201538614458,
'FEATURE_IC50_T_pval': 5824.8201538614449,
'FEATURE_IC50_effect_size': 4408.511449781573,
'FEATURE_delta_MEAN_IC50': 261.11373729866705,
'FEATURE_neg_Glass_delta': 4487.7401723134735,
'FEATURE_neg_IC50_sd': 14701.868130868914,
'FEATURE_neg_logIC50_MEAN': 28701.510736736222,
'FEATURE_pos_Glass_delta': 6536.8938399490198,
'FEATURE_pos_IC50_sd': 13362.588398939894,
'FEATURE_pos_logIC50_MEAN': 28962.624474034845,
'ANOVA_MSI_pval': 0.0,
'N_FEATURE_neg': 439196.0,
'N_FEATURE_pos': 92140.0,
'ANOVA_TISSUE_pval': 0.0,
'ASSOC_ID': 68509365.0,
'index': 68497660.0
}
for k, v in totest.items():
if k in ['ANOVA_MEDIA_pval']:
continue
assert_almost_equal(v, exact[k])
# test part of the report (summary section)
r = ANOVAReport(an, dfori)
totest = r.diagnostics().to_dict()
exact = {
'text': {
0: 'Type of analysis',
1: 'Total number of possible drug/feature associations',
2: 'Total number of ANOVA tests performed',
3: 'Percentage of tests performed',
4: '',
5: 'Total number of tested drugs',
6: 'Total number of genomic features used',
7: 'Total number of screened cell lines',
8: 'MicroSatellite instability included as factor',
9: '',
10: 'Total number of significant associations',
11: ' - sensitive',
12: ' - resistant',
13: 'p-value significance threshold',
14: 'FDR significance threshold',
15: 'Range of significant p-values',
16: 'Range of significant % FDRs'
},
'value': {
0: 'breast',
1: 13780,
2: 11705,
3: 84.94,
4: '',
5: 265,
6: 52,
7: 51,
8: False,
9: '',
10: 27,
11: 17,
12: 10,
13: 0.001,
14: 25,
15: '[2.098e-09, 0.0004356]',
16: '[0.002456 18.89]'
}
}
assert totest == exact