def anova_one_drug(anova, options):
"""Analyse one specific drug"""
from gdsctools import ANOVAReport
anova.set_cancer_type(options.tissue)
if options.feature:
anova.feature_names = options.features
results = anova.anova_one_drug(options.drug)
print("\nFound %s associations" % len(results))
if len(results)==0:
print(red("\nPlease try with another drug or no --drug option"))
return
# ?? is this required ?
N = len(results)
results.df.insert(0, 'ASSOC_ID', range(1, N+1))
if options.no_html is True:
return
r = ANOVAReport(anova, results=results)
print(darkgreen("\nCreating all figure and html documents in %s" %
r.settings.directory))
r.create_html_pages(onweb=options.onweb)
def anova_one_drug(anova, options):
"""Analyse one specific drug"""
from gdsctools import ANOVAReport
anova.set_cancer_type(options.tissue)
if options.feature:
anova.feature_names = options.features
results = anova.anova_one_drug(options.drug)
print("\nFound %s associations" % len(results))
if len(results) == 0:
print(red("\nPlease try with another drug or no --drug option"))
return
# ?? is this required ?
N = len(results)
results.df.insert(0, 'ASSOC_ID', range(1, N + 1))
if options.no_html is True:
return
r = ANOVAReport(anova, results=results)
print(
darkgreen("\nCreating all figure and html documents in %s" %
r.settings.directory))
r.create_html_pages(onweb=options.onweb)
def anova_all(anova, options):
"""Analyse the entire data set. May be restricted to one feature"""
from gdsctools import ANOVAReport
if options.feature:
anova.feature_names = [options.feature]
# The analysis
print(darkgreen("Starting the analysis"))
df = anova.anova_all()
if len(df) == 0:
print("Found no valid association ? Check your input files")
return
# HTML report
if options.no_html is True:
return
r = ANOVAReport(anova, results=df)
print(darkgreen("Creating all figure and html documents in %s" %
r.settings.directory))
r.create_html_pages(onweb=options.onweb)
def anova_all(anova, options):
"""Analyse the entire data set. May be restricted to one feature"""
from gdsctools import ANOVAReport
if options.feature:
anova.feature_names = [options.feature]
# The analysis
print(darkgreen("Starting the analysis"))
df = anova.anova_all()
if len(df) == 0:
print("Found no valid association ? Check your input files")
return
# HTML report
if options.no_html is True:
return
r = ANOVAReport(anova, results=df)
print(darkgreen("Creating all figure and html documents in %s" %
r.settings.directory))
r.create_html_pages(onweb=options.onweb)
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')
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