def getManhattanData(type, value):
# We'll need to get which phenotypes are selected.
significantPhenotypes = getPhenotypes.getSignigicantPhenotypes(type, value)
if significantPhenotypes.any()['nom']: # Check if there are significant phenotypes
phenotype = getPhenotypes.getSignigicantPhenotypes(type, value).iat[0,0] #This gets the name of the significant phenotype
# phenotype = "All cause death"
# First we define the query
sqlQuery="select distinct a.rs_id_assoc, a.chromosome,a.pos,a.info_assoc,a.pvalue_assoc,a.allele_A,a.allele_B,a.cohort_AA,a.cohort_BB,a.beta_assoc,a.maf, a.all_OR,xp.covariates,m.gene,m.gene_before,m.gene_after from assoc a join experiment xp on a.experiment=xp.idexperiment join phenotypes p on xp.phenotype=p.idphenotypes join marqueurs m on a.rs_id_assoc=m.nom where p.nom='"+phenotype+"' and a.pvalue_assoc<=0.001"
# Then we fetch the data and store it in a dataframe
sorted_data = connect.fetchData(sqlQuery)
UserLogs.add(
'Victor Dupuy',
'255.255.255.255',
'accessed the module : Interactive Manhattan',
'MySQL Database',
['assoc', 'phenotypes', 'marqueurs']
)
sorted_data.drop_duplicates(subset='rs_id_assoc', inplace=True,keep='last')
sorted_data["log10"] = -numpy.log10(sorted_data.pvalue_assoc) #ADD COLUMN LOG10
sorted_data = sorted_data.sort(['chromosome', 'pos'])
sorted_data['even']=numpy.where(sorted_data['chromosome'] %2==0,sorted_data['log10'] , 'NaN')
sorted_data["odd"]=numpy.where(sorted_data['chromosome'] %2!=0,sorted_data['log10'] , 'NaN')
col=['rs_id_assoc', 'chromosome', 'pos', 'pvalue_assoc', 'allele_A', 'allele_B', 'covariates', 'cohort_BB', 'cohort_AA', 'beta_assoc', 'maf']
return sorted_data, phenotype
else:
print "There is no phenotype !"
return "No Phenotype !"
def getManhattanData(type, value):
# We'll need to get which phenotypes are selected.
significantPhenotypes = getPhenotypes.getSignigicantPhenotypes(type, value)
if significantPhenotypes.any()['nom']: # Check if there are significant phenotypes
phenotype = getPhenotypes.getSignigicantPhenotypes(type, value).iat[0,0] #This gets the name of the significant phenotype
# phenotype = "All cause death"
# First we define the query
sqlQuery="select distinct a.rs_id_assoc, a.chromosome,a.pos,a.info_assoc,a.pvalue_assoc,a.allele_A,a.allele_B,a.cohort_AA,a.cohort_BB,a.beta_assoc,a.maf, a.all_OR,xp.covariates,m.gene,m.gene_before,m.gene_after from assoc a join experiment xp on a.experiment=xp.idexperiment join phenotypes p on xp.phenotype=p.idphenotypes join marqueurs m on a.rs_id_assoc=m.nom where p.nom='"+phenotype+"' and a.pvalue_assoc<=0.001 limit 1000"
# Then we fetch the data and store it in a dataframe
sorted_data = connect.fetchData(sqlQuery)
UserLogs.add(
'Victor Dupuy',
'255.255.255.255',
'accessed the module : Interactive Manhattan',
'MySQL Database',
['assoc', 'phenotypes', 'marqueurs']
)
sorted_data.drop_duplicates(subset='rs_id_assoc', inplace=True,keep='last')
sorted_data["log10"] = -numpy.log10(sorted_data.pvalue_assoc) #ADD COLUMN LOG10
sorted_data = sorted_data.sort(['chromosome', 'pos'])
sorted_data['even']=numpy.where(sorted_data['chromosome'] %2==0,sorted_data['log10'] , 'NaN')
sorted_data["odd"]=numpy.where(sorted_data['chromosome'] %2!=0,sorted_data['log10'] , 'NaN')
col=['rs_id_assoc', 'chromosome', 'pos', 'pvalue_assoc', 'allele_A', 'allele_B', 'covariates', 'cohort_BB', 'cohort_AA', 'beta_assoc', 'maf']
return sorted_data, phenotype
else:
print "There is no phenotype !"
return "No Phenotype !"
def table(request, type, value):
print request, type, value
global data
UserLogs.add(
'Victor Dupuy',
'255.255.255.255',
'accessed the module : Web',
'MySQL Database',
['assoc', 'phenotypes', 'marqueurs']
)
# We'll need to get which phenotypes are selected.
significantPhenotypes = getPhenotypes.getSignigicantPhenotypes(type, value)
if significantPhenotypes.any()['nom']: # Check if there are significant phenotypes
phenotype_selected = getPhenotypes.getSignigicantPhenotypes(type, value).iat[0,0] #This gets the name of the significant phenotype
print significantPhenotypes
# First we define the query
sqlQuery = "select distinct a.rs_id_assoc,a.chromosome,a.pos,a.pvalue_assoc,m.gene_before,m.gene,m.gene_after,a.experiment,a.info_assoc,a.allele_A,a.allele_B,a.cohort_AA,a.cohort_BB,a.beta_assoc,a.maf,a.all_OR,xp.covariates, p.nom, case when a.beta_assoc>0 and p.Risk_on_rise is true then a.allele_B when a.beta_assoc<0 and p.Risk_on_rise is true then a.allele_A when a.beta_assoc>0 and p.Risk_on_rise is false then a.allele_A when a.beta_assoc<0 and p.Risk_on_rise is false then a.allele_B end as risk_allele, case when a.beta_assoc>0 and p.Risk_on_rise is true then ( (2*a.cohort_BB) + a.cohort_AB ) / ((2*a.cohort_AA) + (2*a.cohort_AB) +(2*a.cohort_BB) ) when a.beta_assoc<0 and p.Risk_on_rise is true then ( (2*a.cohort_AA) + a.cohort_AB ) / ((2*a.cohort_AA) + (2*a.cohort_AB) +(2*a.cohort_BB) ) when a.beta_assoc>0 and p.Risk_on_rise is false then ( (2*a.cohort_AA) + a.cohort_AB ) / ((2*a.cohort_AA) + (2*a.cohort_AB) +(2*a.cohort_BB) ) when a.beta_assoc<0 and p.Risk_on_rise is false then ( (2*a.cohort_BB) + a.cohort_AB ) / ((2*a.cohort_AA) + (2*a.cohort_AB) +(2*a.cohort_BB) ) end as risk_af, case when a.beta_assoc>0 and p.Risk_on_rise is true then a.beta_assoc when a.beta_assoc<0 and p.Risk_on_rise is true then a.beta_assoc*(-1) when a.beta_assoc>0 and p.Risk_on_rise is false then a.beta_assoc*(-1) when a.beta_assoc<0 and p.Risk_on_rise is false then a.beta_assoc end as risk_allele_beta from assoc a join experiment xp on a.experiment=xp.idexperiment join phenotypes p on xp.phenotype=p.idphenotypes join marqueurs m on a.rs_id_assoc=m.nom where p.nom='"+phenotype_selected+"' and a.pvalue_assoc<0.001"
# Then we fetch the data and store it in a dataframe
dataframe = connect.fetchData(sqlQuery)
# dataframe.rename(columns={'rs_id_assoc':'rs id assoc'}) #Self-explanatory
data = dataframe
dataframe.rename(columns = {
'rs_id_assoc': 'rs_ID',
'pos': 'Pos',
'chromosome': 'Chr',
'gene_before': 'GeneBefore',
'pvalue_assoc': 'P-value',
'allele_A': 'Allele A',
'allele_B': 'Allele B',
'cohort_AA' : 'cohort AA',
'cohort_BB' : 'cohort BB',
'beta_assoc' : 'Beta Assoc',
'covariates' : 'Covariates',
'risk_allele' : 'Risk Allele',
'risk_af' : 'Risk Af',
'risk_allele_beta' : 'Risk Allele Beta',
'gene': 'Gene',
'gene_after': 'GeneAfter',
'nom' : 'Phenotype'
}, inplace=True) #rename column to make them look nicer
jsonData = dataframe.to_json(orient='records')
significantPhenotypes.rename(columns={'nom' : 'Phenotypes'}, inplace=True);
# Then we pass the dataframe to the client with json format
response = json.dumps({
'noResult' : False,
'phenotypes': significantPhenotypes.to_json(orient='records'),
'data' : jsonData
},
sort_keys=True,
indent=4,
separators=(',', ': ')
)
return HttpResponse(response)
else:
print "There is no phenotype !"
response = json.dumps({
'noResult' : True,
'phenotypes': [],
'data' : []
},
sort_keys=True,
indent=4,
separators=(',', ': ')
)
return HttpResponse(response)
