def type(self, id=None):
ScoreRankHistogram = model.Stock_250kDB.ScoreRankHistogram
ScoreRankHistogramType = model.Stock_250kDB.ScoreRankHistogramType
query = ScoreRankHistogram.query
if id is None:
id = request.params.get('id', 1)
hist_type_id = id
extra_condition = 's.hist_type_id=%s'%hist_type_id
c.phenotype_info = hc.getPhenotypeInfo(ScoreRankHistogram.table.name, extra_condition)
c.list_info = hc.getListTypeInfo(ScoreRankHistogram.table.name, extra_condition)
c.hist_type = ScoreRankHistogramType.get(hist_type_id)
rows = query.filter_by(hist_type_id=hist_type_id).all()
data_matrix = numpy.zeros([len(c.list_info.list_type_id_ls), len(c.phenotype_info.phenotype_method_id_ls)], numpy.int)
data_matrix[:] = -1
counter = 0
for row in rows:
row_index = c.list_info.list_type_id2index[row.list_type_id]
col_index = c.phenotype_info.phenotype_method_id2index[row.phenotype_method_id]
data_matrix[row_index, col_index] = row.id
counter +=1
c.counter = counter
c.data_matrix = data_matrix
return render('/score_rank_histgram_type.html')
def type_by_snp(self, id=None):
"""
2008-10-17
display SNP region plots in a matrix fashion by spanning the phenotype axis.
"""
SNPRegionPlot = model.Stock_250kDB.SNPRegionPlot
if id is None:
id = request.params.get('id', 1)
extra_condition = 's.plot_type_id=%s'%id
c.phenotype_info = hc.getPhenotypeInfo(SNPRegionPlot.table.name, extra_condition)
c.snp_info = self.getSNPInfo(plot_type_id=id)
data_matrix = numpy.zeros([len(c.snp_info.snp_ls), len(c.phenotype_info.phenotype_method_id_ls)], numpy.int)
data_matrix[:] = -1
counter = 0
rows = SNPRegionPlot.query.filter_by(plot_type_id=id).all()
for row in rows:
snp_key = (row.chromosome, row.start, row.stop)
row_index = c.snp_info.snp2index[snp_key]
col_index = c.phenotype_info.phenotype_method_id2index[row.phenotype_method_id]
data_matrix[row_index, col_index] = row.id
counter +=1
c.counter = counter
c.data_matrix = data_matrix
return render('/snp_region_plot_type_by_snp.html')
def type_by_gene(self, id=None):
"""
2008-10-17
"""
SNPRegionPlot = model.Stock_250kDB.SNPRegionPlot
SNPRegionPlotToGene = model.Stock_250kDB.SNPRegionPlotToGene
if id is None:
id = request.params.get('id', 1)
extra_condition = 's.plot_type_id=%s'%id
c.phenotype_info = hc.getPhenotypeInfo(SNPRegionPlot.table.name, extra_condition)
c.gene_info = self.getGeneInfo(plot_type_id=id)
data_matrix = numpy.zeros([len(c.gene_info.gene_ls), len(c.phenotype_info.phenotype_method_id_ls)], numpy.int)
#data_matrix[:] = -1
counter = 0
rows = SNPRegionPlotToGene.query.filter(SNPRegionPlotToGene.snp_region_plot.has(plot_type_id=id)).all()
for row in rows:
row_index = c.gene_info.gene_id2index[row.gene_id]
c.gene_info.gene_ls[row_index].snp_region_plot_ls.append(row.snp_region_plot)
col_index = c.phenotype_info.phenotype_method_id2index[row.snp_region_plot.phenotype_method_id]
data_matrix[row_index, col_index] = row.snp_region_plot.id
counter +=1
for row_index in range(len(c.gene_info.gene_ls)):
c.gene_info.gene_ls[row_index].snp_region_plot_ls.sort(lambda x, y: int(x.phenotype_method_id-y.phenotype_method_id)) #comparison function must return int. and long is NOT int.
c.gene_info.gene_ls[row_index].snp_region_plot_id_ls = [str(snp_region_plot.id) for snp_region_plot in c.gene_info.gene_ls[row_index].snp_region_plot_ls]
c.counter = counter
c.data_matrix = data_matrix
return render('/snp_region_plot_type_by_gene.html')
def getGeneInfo(self, plot_type_id=None):
"""
2008-10-17
return data structure containing sorted gene list and its info
"""
SNPRegionPlot = model.Stock_250kDB.SNPRegionPlot
SNPRegionPlotToGene = model.Stock_250kDB.SNPRegionPlotToGene
rows = model.db.metadata.bind.execute("select g.gene_id, count(distinct s.phenotype_method_id) as count from %s g, %s s\
where s.id=g.plot_id and s.plot_type_id=%s group by g.gene_id order by count desc, gene_id"\
%(SNPRegionPlotToGene.table.name, SNPRegionPlot.table.name, plot_type_id))
gene_ls = []
gene_id2index = {}
gene_label_ls = []
gene_desc_names = ['gene_id', 'gene_symbol', 'type_of_gene', 'chr', 'start', 'stop', 'protein_label', 'protein_comment', 'protein_text']
for row in rows:
gene_id2index[row.gene_id] = len(gene_ls)
matrix_of_gene_descriptions = hc.returnGeneDescLs(model.gene_annotation, gene_id_ls=[row.gene_id])
if len(matrix_of_gene_descriptions)>0:
gene_desc_ls = matrix_of_gene_descriptions[0]
gene = PassingData(gene_id=row.gene_id, gene_symbol=gene_desc_ls[1], type_of_gene=gene_desc_ls[2], chr=gene_desc_ls[3],\
start=gene_desc_ls[4], stop=gene_desc_ls[5], protein_label=gene_desc_ls[6], protein_comment=gene_desc_ls[7], \
protein_text=gene_desc_ls[8])
else:
gene = PassingData(gene_id=row.gene_id)
gene.count = row.count
gene.snp_region_plot_ls = []
gene_ls.append(gene)
gene_label_ls.append(getattr(gene, 'gene_symbol', row.gene_id))
gene_info = PassingData()
gene_info.gene_id2index = gene_id2index
gene_info.gene_ls = gene_ls
gene_info.gene_label_ls = gene_label_ls
return gene_info
def getCallMethodLs(): """ 2009-1-30 """ affiliated_table_name = model.Stock_250kDB.ResultsMethod.table.name list_info = hc.getCallMethodInfo(affiliated_table_name=affiliated_table_name) call_method_ls = [] for i in range(len(list_info.id_ls)): id = list_info.id_ls[i] label = list_info.label_ls[i] call_method_ls.append([id, label]) return call_method_ls
def getPhenotypeMethodLs(call_method_id): """ 2009-1-30 """ affiliated_table_name = model.Stock_250kDB.ResultsMethod.table.name extra_condition = 's.call_method_id=%s'%(call_method_id) phenotype_info = hc.getPhenotypeInfo(affiliated_table_name=affiliated_table_name, extra_condition=extra_condition) phenotype_method_ls = [] for i in range(len(phenotype_info.phenotype_method_id_ls)): phenotype_method_id = phenotype_info.phenotype_method_id_ls[i] phenotype_method_label = phenotype_info.phenotype_method_label_ls[i] phenotype_method_ls.append([phenotype_method_id, phenotype_method_label]) return phenotype_method_ls
def call_method(self, id=None):
if id is None:
id = request.params.get('id', 17)
MAFVsScorePlot = model.Stock_250kDB.MAFVsScorePlot
affiliated_table_name = model.Stock_250kDB.ResultsMethod.table.name
extra_condition = 's.call_method_id=%s and s.id=m.results_method_id'%id
extra_tables = '%s m'%model.Stock_250kDB.MAFVsScorePlot.table.name
c.phenotype_info = hc.getPhenotypeInfo(affiliated_table_name, extra_condition, extra_tables)
c.analysis_info = hc.getAnalysisMethodInfo(affiliated_table_name, extra_condition, extra_tables)
rows = MAFVsScorePlot.query.filter(MAFVsScorePlot.results_method.has(call_method_id=id))
data_matrix = numpy.zeros([len(c.phenotype_info.phenotype_method_id_ls), len(c.analysis_info.id_ls)], numpy.int)
data_matrix[:] = -1
counter = 0
for row in rows:
row_index = c.phenotype_info.phenotype_method_id2index[row.results_method.phenotype_method_id]
col_index = c.analysis_info.id2index[row.results_method.analysis_method_id]
data_matrix[row_index, col_index] = row.id
counter +=1
c.counter = counter
c.data_matrix = data_matrix
c.call_method_id = id
return render('/maf_vs_score_call_method.html')
def getPhenotypeMethodLs(self, call_method_id=None, affiliated_table_name=None):
"""
2009-4-6
copied from DisplayResults.py
"""
#call_method_id = request.params.getone('call_method_id')
#affiliated_table_name = model.Stock_250kDB.ResultsMethod.table.name
#extra_condition = 's.call_method_id=%s'%(call_method_id)
extra_condition = ''
phenotype_info = hc.getPhenotypeInfo(affiliated_table_name=affiliated_table_name, extra_condition=extra_condition)
phenotype_method_ls = []
for i in range(len(phenotype_info.phenotype_method_id_ls)):
phenotype_method_id = str(phenotype_info.phenotype_method_id_ls[i])
phenotype_method_label = phenotype_info.phenotype_method_label_ls[i]
phenotype_method_ls.append([phenotype_method_id, phenotype_method_label])
return phenotype_method_ls
def getAnalysisMethodLs(call_method_id, phenotype_method_id): """ 2009-1-30 """ affiliated_table_name = model.Stock_250kDB.ResultsMethod.table.name #alias is 's' extra_condition = 's.call_method_id=%s and s.phenotype_method_id=%s'%\ (call_method_id, phenotype_method_id) list_info = hc.getAnalysisMethodInfo(affiliated_table_name, extra_condition=extra_condition) analysis_method_ls = [] for i in range(len(list_info.id_ls)): id = list_info.id_ls[i] label = list_info.label_ls[i] description = list_info.description_ls[i] analysis_method_ls.append([id, label, description]) return analysis_method_ls
def getGeneListTypeLsGivenTypeAndPhenotypeMethodAndAnalysisMethod(): """ 2009-3-4 called by getGeneListTypeLsGivenTypeAndPhenotypeMethodAndAnalysisMethodJson() """ #if not affiliated_table_name: # affiliated_table_name = model.Stock_250kDB.CandidateGeneTopSNPTestRM.table.name #alias is 's' #extra_tables = ' %s c '%model.Stock_250kDB.ResultsMethod.table.name #extra_condition = 's.results_id=c.id and s.type_id=%s and c.call_method_id=%s and c.phenotype_method_id=%s and c.analysis_method_id=%s'%\ # (type_id, call_method_id, phenotype_method_id, analysis_method_id) list_info = hc.getListTypeInfo(affiliated_table_name=None, extra_condition=None, extra_tables=None) ls = [[0, u'No candidate gene list. (All SNPs)']] for i in range(len(list_info.list_type_id_ls)): id = list_info.list_type_id_ls[i] label = list_info.list_type_label_ls[i] ls.append([id, label]) return ls
def getPhenotypeMethodLsGivenType(type_id, call_method_id, extra_table_name=None): """ 2009-2-22 add argument extra_table_name 2008-12-30 """ affiliated_table_name = model.Stock_250kDB.ResultsMethod.table.name if not extra_table_name: extra_table_name = model.Stock_250kDB.CandidateGeneTopSNPTestRM.table.name extra_tables = ' %s c '%extra_table_name extra_condition = 'c.results_id=s.id and c.type_id=%s and s.call_method_id=%s'%(type_id, call_method_id) phenotype_info = hc.getPhenotypeInfo(affiliated_table_name=affiliated_table_name, extra_condition=extra_condition, extra_tables=extra_tables) phenotype_method_ls = [] for i in range(len(phenotype_info.phenotype_method_id_ls)): phenotype_method_id = phenotype_info.phenotype_method_id_ls[i] phenotype_method_label = phenotype_info.phenotype_method_label_ls[i] phenotype_method_ls.append([phenotype_method_id, phenotype_method_label]) return phenotype_method_ls
def getCallMethodLsGivenType(type_id, extra_table_name=None): """ 2009-2-22 add argument extra_table_name 2008-12-30 """ affiliated_table_name = model.Stock_250kDB.ResultsMethod.table.name if not extra_table_name: extra_table_name = model.Stock_250kDB.CandidateGeneTopSNPTestRM.table.name extra_tables = ' %s c '%extra_table_name extra_condition = 'c.results_id=s.id and c.type_id=%s'%type_id list_info = hc.getCallMethodInfo(affiliated_table_name=affiliated_table_name, extra_condition=extra_condition, extra_tables=extra_tables) call_method_ls = [] for i in range(len(list_info.id_ls)): id = list_info.id_ls[i] label = list_info.label_ls[i] call_method_ls.append([id, label]) return call_method_ls
def show_plot(self, id=None, snp_region_plot_ids=None):
"""
2008-10-30
add option snp_region_plot_ids for display_results_gene_one.html to call but not necessary. request.params is good enough.
2008-10-17
get request params: get_other_phenotypes, snp_region_plot_ids
2008-10-06
"""
SNPRegionPlot = model.Stock_250kDB.SNPRegionPlot
if id:
c.snp_region_plot = SNPRegionPlot.get(id)
else:
c.snp_region_plot = None
get_other_phenotypes = request.params.get('get_other_phenotypes', False)
snp_region_plot_ids = request.params.get('snp_region_plot_ids', snp_region_plot_ids)
if get_other_phenotypes:
c.snp_region_plot_ls = SNPRegionPlot.query.filter_by(chromosome=c.snp_region_plot.chromosome).\
filter_by(start=c.snp_region_plot.start).\
filter_by(stop=c.snp_region_plot.stop).\
filter_by(plot_type_id=c.snp_region_plot.plot_type_id).order_by(SNPRegionPlot.phenotype_method_id).all()
elif snp_region_plot_ids:
snp_region_plot_id_ls = snp_region_plot_ids.split(',')
#snp_region_plot_id_ls = map(int, snp_region_plot_id_ls)
c.snp_region_plot_ls = [SNPRegionPlot.get(int(snp_region_plot_id)) for snp_region_plot_id in snp_region_plot_id_ls]
else:
c.snp_region_plot_ls = []
if c.snp_region_plot is None and len(c.snp_region_plot_ls)>0:
c.snp_region_plot = c.snp_region_plot_ls[0]
#plot_file_path = os.path.join(config['app_conf']['plots_store'], 'snp_region_plot_%s.png'%c.snp_region_plot.id)
#if not os.path.isfile(plot_file_path):
# self.savePlot(c.snp_region_plot, plot_file_path)
gene_annotation = model.gene_annotation
gene_id_ls = [plot2gene.gene_id for plot2gene in c.snp_region_plot.plot2gene_ls]
c.matrix_of_gene_descriptions = hc.returnGeneDescLs(gene_annotation, gene_id_ls)
#print abc
#raise Exception('Just testing the interactive debugger!')
#c.matrix_of_gene_descriptions = []
return render('/snp_region_plot_single.html')
def getGeneListTypeLsGivenTypeAndPhenotypeMethodAndAnalysisMethod(type_id, call_method_id, phenotype_method_id, analysis_method_id, affiliated_table_name=None): """ 2009-2-22 add argument affiliated_table_name (but not used) 2008-12-30 """ if not affiliated_table_name: affiliated_table_name = model.Stock_250kDB.CandidateGeneTopSNPTestRM.table.name #alias is 's' extra_tables = ' %s c '%model.Stock_250kDB.ResultsMethod.table.name extra_condition = 's.results_id=c.id and s.type_id=%s and c.call_method_id=%s and c.phenotype_method_id=%s and c.analysis_method_id=%s'%\ (type_id, call_method_id, phenotype_method_id, analysis_method_id) list_info = hc.getListTypeInfo(affiliated_table_name, extra_condition=extra_condition, extra_tables=extra_tables) ls = [] for i in range(len(list_info.list_type_id_ls)): id = list_info.list_type_id_ls[i] label = list_info.list_type_label_ls[i] ls.append([id, label]) return ls
def getCallMethodLsJson(self):
"""
2009-11-30
return all possible call_method_ids in table association_overlapping_stat
"""
affiliated_table_name = model.Stock_250kDB.AssociationOverlappingStat.table.name
list_info = hc.getCallMethodInfo(affiliated_table_name=affiliated_table_name)
call_method_ls = []
for i in range(len(list_info.id_ls)):
id = list_info.id_ls[i]
label = list_info.label_ls[i]
call_method_ls.append([id, label])
result = {
'options': [
dict(id=value, value=id) for id, value in call_method_ls
]
}
#result['options'].append({'id': u'[At the end]', 'value': u''})
result['options'].insert(0, {'id': u'Please Choose ...', 'value': 0})
return simplejson.dumps(result)
def getAnalysisMethodLsGivenTypeAndPhenotypeMethod(type_id, call_method_id, phenotype_method_id, extra_table_name=None): """ 2009-2-22 add argument extra_table_name 2008-12-30 """ affiliated_table_name = model.Stock_250kDB.ResultsMethod.table.name #alias is 's' if not extra_table_name: extra_table_name = model.Stock_250kDB.CandidateGeneTopSNPTestRM.table.name extra_tables = ' %s c '%extra_table_name extra_condition = 'c.results_id=s.id and c.type_id=%s and s.call_method_id=%s and s.phenotype_method_id=%s'%\ (type_id, call_method_id, phenotype_method_id) list_info = hc.getAnalysisMethodInfo(affiliated_table_name, extra_condition=extra_condition, extra_tables=extra_tables) analysis_method_ls = [] for i in range(len(list_info.id_ls)): id = list_info.id_ls[i] label = list_info.label_ls[i] analysis_method_ls.append([id, label]) return analysis_method_ls
def getTrendData(self):
"""
2009-5-3
"""
phenoData = hc.getPhenotypeData()
column_name_type_ls = [("label", ("string","ID Name")), ("date", ("date", "Date")), \
("which_phenotype", ("number","Which Phenotype")), ("phenotype_value", ("number","Phenotype Value"))]
no_of_non_phenotype_cols = len(column_name_type_ls)
phenotype_id_included = []
for phenotype_id in self.phenotype_method_id_list:
i = phenoData.col_id2col_index.get(phenotype_id)
if i is not None:
phenotype_id_included.append(phenotype_id)
description = dict(column_name_type_ls)
return_ls = []
for i in range(len(phenotype_id_included)):
phenotype_id = phenotype_id_included[i]
date=datetime.date(i+1000,1,1)
for j in range(len(phenoData.row_id_ls)):
row_label = phenoData.row_label_ls[j]
row_id = phenoData.row_id_ls[j]
label = '%s ID:%s'%(row_label, row_id)
pheno_row_index = j
pheno_col_index = phenoData.col_id2col_index.get(phenotype_id)
if pheno_row_index is not None and pheno_col_index is not None:
phenotype_value = phenoData.data_matrix[pheno_row_index][pheno_col_index]
if phenotype_value == 'NA' or numpy.isnan(phenotype_value):
phenotype_value = None
else:
phenotype_value = None #numpy.nan can't be recognized by ToJSon()
data = dict(date=date, label=label, which_phenotype=i, phenotype_value=phenotype_value)
return_ls.append(data)
data_table = gviz_api.DataTable(description)
data_table.LoadData(return_ls)
column_ls = [row[0] for row in column_name_type_ls]
json_result = data_table.ToJSon(columns_order=column_ls) #ToJSonResponse only works with google.visualization.Query
return json_result
def getPhenotypeMethodLsGivenType(type_id, extra_table_name=None): """ 2009-4-13 added results_gene2type into the condition to restrict phenotype_methods type_id is actually used now. 2009-3-4 """ affiliated_table_name = model.Stock_250kDB.ResultsMethod.table.name type = ScoreRankHistogramType.get(type_id) call_method_id = type.call_method_id if not extra_table_name: extra_table_name = model.Stock_250kDB.ResultsGene.table.name extra_tables = ' %s c, %s y'%(extra_table_name, "results_gene2type") extra_condition = 'c.results_id=s.id and s.call_method_id=%s and y.score_rank_histogram_type_id=%s and y.results_gene_id=c.id'%\ (call_method_id, type_id) phenotype_info = hc.getPhenotypeInfo(affiliated_table_name=affiliated_table_name, extra_condition=extra_condition, extra_tables=extra_tables) phenotype_method_ls = [] for i in range(len(phenotype_info.phenotype_method_id_ls)): phenotype_method_id = phenotype_info.phenotype_method_id_ls[i] phenotype_method_label = phenotype_info.phenotype_method_label_ls[i] phenotype_method_ls.append([phenotype_method_id, phenotype_method_label]) return phenotype_method_ls, call_method_id
def getOverlappingDataAcrossPhenotypes(self, no_of_top_snps=1000):
"""
2009-11-30
"""
call_method_id = int(request.params.get('call_method_id', config['app_conf']['published_call_method_id']))
no_of_top_snps = int(request.params.get('no_of_top_snps', no_of_top_snps))
phenotype_info = hc.getPhenotypeInfo(affiliated_table_name=model.Stock_250kDB.AssociationOverlappingStat.table.name, \
extra_condition="s.call_method_id=%s"%call_method_id, extra_tables=None, \
with_category_separator=False)
overlapping_type_info = hc.getAssociationOverlappingTypeInfo(affiliated_table_name=model.Stock_250kDB.AssociationOverlappingStat.table.name, \
extra_condition="s.call_method_id=%s"%call_method_id, extra_tables=None)
#construct the full data and turn it into json
column_name_type_ls = [("label", ("string","ID Name")), ("date", ("date", "Date")), ("phenotype_method_id", ("number", "Phenotype ID")), \
("phenotype_name", ("string", "Phenotype Name")), \
("category", ("string","Category")), ]
no_of_non_stat_cols = len(column_name_type_ls)
for i in range(len(overlapping_type_info.list_type_label_ls)):
column_name_type_ls.append(('%s'%overlapping_type_info.list_type_id_ls[i], ("number", overlapping_type_info.list_type_label_ls[i])))
return_ls = []
for i in range(len(phenotype_info.phenotype_method_id2index)):
entry = {}
for column_name_type in column_name_type_ls[no_of_non_stat_cols:]: # set the default value for the overlapping stats
column_name = column_name_type[0]
column_type = column_name_type[1][0]
entry[column_name] = None
return_ls.append(entry)
rows = model.Stock_250kDB.AssociationOverlappingStat.query.filter_by(call_method_id=call_method_id).filter_by(no_of_top_snps=no_of_top_snps)
for row in rows:
phenotype_index = phenotype_info.phenotype_method_id2index[row.phenotype_method_id]
entry = return_ls[phenotype_index]
for column_name_type in column_name_type_ls[:no_of_non_stat_cols]: # only the info related to phenotype part
column_name = column_name_type[0]
column_type = column_name_type[1][0]
if column_type=='string':
default_value = ''
elif column_type=='date':
default_value = datetime.date(2050, 1, 1)
else:
default_value = None
if column_name == 'phenotype_name':
pm = model.Stock_250kDB.PhenotypeMethod.get(row.phenotype_method_id)
column_value = pm.short_name
elif column_name=='category':
pm = model.Stock_250kDB.PhenotypeMethod.get(row.phenotype_method_id)
if pm.biology_category:
column_value = pm.biology_category.short_name
else:
column_value = None
elif column_name=='label':
pm = model.Stock_250kDB.PhenotypeMethod.get(row.phenotype_method_id)
column_value = '%s %s'%(row.phenotype_method_id, pm.short_name)
else:
column_value = getattr(row, column_name, default_value)
if column_value is None:
column_value = default_value
entry[column_name] = column_value
# set the overlapping stat
column_name = '%s'%row.overlapping_type_id
entry[column_name] = row.no_of_overlapping_snps
description = dict(column_name_type_ls)
data_table = gviz_api.DataTable(description)
data_table.LoadData(return_ls)
column_ls = [row[0] for row in column_name_type_ls]
json_result = data_table.ToJSon(columns_order=column_ls) #ToJSonResponse only works with google.visualization.Query
response.headers['Content-Type'] = 'application/json'
return json_result
def getPhenotypeTableData(self):
"""
2009-11-17
"#Accessions" means the number of accessions that were phenotyped.
add "#Genotyped" column in the output, which means the no of accessions that were phenotyped and genotyped (in certain call method).
2009-11-17
getNoOfAccessionsGivenPhenotypeAndCallMethodID() replaces getNoOfAccessionsGivenPhenotypeMethodID()
2009-7-30
fetch the number of accessions for each phenotype by calling hc.getNoOfAccessionsGivenPhenotypeMethodID(), rather than static value in table phenotype_method
2009-5-24
add column transformation_description back
2009-5-13
remove column transformation_description, citations
2009-5-11
add no_of_accessions, growth_condition, phenotype_scoring, citations in the output table
2009-4-21
return a google data table containing information of all phenotypes belonging to a category
"""
call_method_id = request.params.get('call_method_id', int(config['app_conf']['published_call_method_id']))
call_method_id = int(call_method_id)
biology_category_id = request.params.get('biology_category_id', 1)
if biology_category_id==0 or biology_category_id=='0':
biology_category_id=None
#construct the full data and turn it into json
column_name_type_ls = [("id", ("number", "Phenotype ID")), \
("short_name", ("string", "Phenotype Name")), \
("association_results", ("string","Association Results")), \
("no_of_accessions", ("number","#Accessions")), \
("no_of_genotyped_accessions", ("number","#Genotyped")), \
("growth_condition", ("string","Growth Condition")), \
("phenotype_scoring", ("string","Phenotype Scoring")), \
("method_description", ("string","Source")), \
("data_type", ("string", "Data Type")),\
("transformation_description", ("string", "Transformation"))]
rows = model.Stock_250kDB.PhenotypeMethod.query.filter(model.Stock_250kDB.PhenotypeMethod.biology_category_id==biology_category_id)
return_ls = []
for row in rows:
entry = dict()
for column_name_type in column_name_type_ls:
column_name = column_name_type[0]
column_type = column_name_type[1][0]
if column_type=='string':
default_value = ''
elif column_type =='number':
default_value = -1
elif column_type=='date':
default_value = datetime.date(2050, 1, 1)
else:
default_value = None
if column_name == 'association_results':
results = model.Stock_250kDB.ResultsMethod.query.filter_by(call_method_id=call_method_id).filter_by(phenotype_method_id=row.id)
analysis_method_short_name_ls = []
for result in results:
analysis_method_short_name_ls.append(result.analysis_method.short_name)
if analysis_method_short_name_ls:
column_value = ','.join(analysis_method_short_name_ls)
else:
column_value = ""
elif column_name=='no_of_accessions':
column_value = hc.getNoOfAccessionsGivenPhenotypeMethodID(row.id)
elif column_name=='no_of_genotyped_accessions':
column_value = hc.getNoOfAccessionsGivenPhenotypeAndCallMethodID(row.id, call_method_id)
else:
column_value = getattr(row, column_name, default_value)
if column_value is None:
column_value = default_value
entry[column_name] = column_value
return_ls.append(entry)
description = dict(column_name_type_ls)
data_table = gviz_api.DataTable(description)
data_table.LoadData(return_ls)
column_ls = [row[0] for row in column_name_type_ls]
json_result = data_table.ToJSon(columns_order=column_ls) #ToJSonResponse only works with google.visualization.Query
response.headers['Content-Type'] = 'application/json'
return json_result
def getPlot(self):
"""
2010-1-22
add argument call_method_id when calling DrawSNPRegion.drawRegionAroundThisSNP()
2009-4-30
"""
chromosome = int(request.params.get('chromosome', 1))
start = int(request.params.get('start', 1))
stop = int(request.params.get('stop', 10))
if start>stop:
start, stop = stop, start
snps_id = '%s_%s'%(chromosome, start)
call_method_id = int(request.params.getone('call_method_id'))
phenotype_method_id = int(request.params.getone('phenotype_method_id'))
list_type_id = int(request.params.get('list_type_id', None))
plot_key = (chromosome, start, stop, call_method_id, phenotype_method_id, list_type_id)
if not hasattr(model, "plot_key2png_data"):
model.plot_key2png_data = {}
png_data = model.plot_key2png_data.get(plot_key)
if png_data is not None:
if hasattr(png_data, 'getvalue'): # png_data is StringIO
response.headers['Content-type'] = 'image/png'
return png_data.getvalue()
else:
return png_data
if not getattr(model, 'phenotype_id2analysis_method_id2gwr', None):
model.phenotype_id2analysis_method_id2gwr = {}
analysis_method_id2gwr = model.phenotype_id2analysis_method_id2gwr.get(phenotype_method_id)
if not analysis_method_id2gwr:
analysis_method_id2gwr = DrawSNPRegion.getSimilarGWResultsGivenResultsByGene(phenotype_method_id, call_method_id)
model.phenotype_id2analysis_method_id2gwr[phenotype_method_id] = analysis_method_id2gwr
if list_type_id>0: #2009-2-22
candidate_gene_set = GeneListRankTest.dealWithCandidateGeneList(list_type_id, return_set=True)
else:
candidate_gene_set = set()
gene_annotation = model.gene_annotation
snpData = hc.getSNPDataGivenCallMethodID(call_method_id)
pheno_data = hc.getPhenotypeDataInSNPDataOrder(snpData)
if h.ecotype_info is None: #2009-3-6 not used right now
h.ecotype_info = getEcotypeInfo(model.db)
snp_info = getattr(model, 'snp_info', None)
if snp_info is None:
snp_info = DrawSNPRegion.getSNPInfo(model.db)
model.snp_info = snp_info
LD_info = None
output_dir = '/tmp/' #not used at all, place holder
which_LD_statistic = 1
this_snp = SNPPassingData(chromosome=chromosome, position=start, stop=stop, snps_id='%s_%s'%(chromosome, start))
DrawSNPRegion.construct_chr_pos2index_forSNPData(snpData) #prerequisite
after_plot_data = DrawSNPRegion.drawRegionAroundThisSNP(phenotype_method_id, this_snp, candidate_gene_set, \
gene_annotation, snp_info, \
analysis_method_id2gwr, LD_info, output_dir, which_LD_statistic, \
min_distance=20000, list_type_id=list_type_id,
label_gene=True, \
draw_LD_relative_to_center_SNP=False,\
commit=True, snpData=snpData, phenData=pheno_data, \
ecotype_info=h.ecotype_info, snpData_before_impute=None,\
snp_matrix_data_type=1, call_method_id=call_method_id)
if getattr(after_plot_data, 'png_data', None):
response.headers['Content-type'] = 'image/png'
model.plot_key2png_data[plot_key] = after_plot_data.png_data
return after_plot_data.png_data.getvalue()
else:
model.plot_key2png_data[plot_key] = "No Plot"
return model.plot_key2png_data[plot_key]
def getEcotypeAllelePhenotype(self, id=None):
"""
2009-6-22
if phenotype_method.data_type is binary or ordered_categorical, add 1 to phenotype_value
to make accessions with zero-phenotype-value visible in the "Ecotype Allele Phenotype BarChart"
2009-3-5
return json data of ecotype, allele, phenotype for the javascript in templates/snp.html
"""
self.readInRequestParams(request, c)
#1st get call_info_id 2 PC12 mapping
rows = model.Stock_250kDB.CallInfo.query.filter_by(method_id=c.call_method_id).all()
call_info_id2pcs = {}
for row in rows:
call_info_id2pcs[row.id] = row.pc_value_ls[:10]
#if h.ecotype_info is None: #2009-3-6 not used right now
# h.ecotype_info = getEcotypeInfo(model.db)
pheno_data = getattr(model, 'pheno_data', None)
if pheno_data is None:
from variation.src.OutputPhenotype import OutputPhenotype
pheno_data = OutputPhenotype.getPhenotypeData(model.db.metadata.bind, phenotype_avg_table=model.Stock_250kDB.PhenotypeAvg.table.name,\
phenotype_method_table=model.Stock_250kDB.PhenotypeMethod.table.name)
model.pheno_data = pheno_data
snpData = hc.getSNPDataGivenCallMethodID(c.call_method_id)
pm = PhenotypeMethod.get(c.phenotype_method_id)
column_name_type_ls = [("label", ("string","ID Name Phenotype")), ("date", ("date", "Date")), \
("lon",("number", "Longitude")), ("lat",("number", "Latitude")), \
("ecotypeid", ("number", "Ecotype ID")), ("name", ("string", "Native Name")), \
("phenotype", ("number", "Phenotype")), \
("allele", ("string", "Allele")), ("country", ("string", "Country")),\
("pc1", ("number","PC1")), ("pc2", ("number", "PC2")), ("pc3", ("number","PC3")), \
("pc4", ("number", "PC4")), ("pc5", ("number","PC5")), ("pc6", ("number", "PC6"))]
description = dict(column_name_type_ls)
rows = model.db.metadata.bind.execute("select * from view_call where call_method_id=%s"%(c.call_method_id))
return_ls = []
for row in rows:
pcs = call_info_id2pcs.get(row.call_info_id)
if pcs:
pc_value_ls = [getattr(call_info_pc, 'pc_value') for call_info_pc in pcs]
else:
pc_value_ls = [0]*10
pheno_row_index = pheno_data.row_id2row_index.get(row.ecotype_id)
pheno_col_index = pheno_data.col_id2col_index.get(c.phenotype_method_id)
if pheno_row_index is not None and pheno_col_index is not None:
phenotype_value = pheno_data.data_matrix[pheno_row_index][pheno_col_index]
if phenotype_value == 'NA':
phenotype_value = None
else:
phenotype_value = None #numpy.nan can't be recognized by ToJSon()
if (pm.data_type=='binary' or pm.data_type=='ordered_categorical') and phenotype_value is not None:
phenotype_value += 1
label = '%s ID:%s Phenotype:%s.'%(row.nativename, row.ecotype_id, phenotype_value)
snpdata_row_index = snpData.row_id2row_index.get(str(row.ecotype_id))
snpdata_col_index = snpData.col_id2col_index.get('%s_%s'%(c.chromosome, c.position))
if snpdata_row_index is None or snpdata_col_index is None:
allele = -2
else:
allele = snpData.data_matrix[snpdata_row_index][snpdata_col_index]
allele = number2nt[allele]
return_ls.append(dict(date=datetime.date(2009,2,3), ecotypeid=row.ecotype_id, label=label, name=row.nativename, \
lat=row.latitude, lon=row.longitude,\
pc1=pc_value_ls[0], pc2=pc_value_ls[1], pc3=pc_value_ls[2], pc4=pc_value_ls[3], \
pc5=pc_value_ls[4], pc6=pc_value_ls[5], \
phenotype=phenotype_value, allele=allele, country=row.country))
data_table = gviz_api.DataTable(description)
data_table.LoadData(return_ls)
column_ls = [row[0] for row in column_name_type_ls]
#column_ls.sort() #['date', 'ecotypeid', 'label', 'lat', 'lon', 'name', 'pc1', 'pc2', 'phenotype']
json_result = data_table.ToJSon(columns_order=column_ls) #ToJSonResponse only works with google.visualization.Query
return json_result
def getMultiPhenotypeData(self):
"""
2009-5-28
present all phenotype data. no filter by phenotype_method_id_list
2009-5-24
add 'region' as a column into the data
2009-5-3
"""
call_method_id = request.params.get('call_method_id', None)
snpData = hc.getSNPDataGivenCallMethodID(call_method_id)
phenoData = hc.getPhenotypeDataInSNPDataOrder(snpData)
rows = model.Stock_250kDB.CallInfo.query.filter_by(method_id=call_method_id).all()
call_info_id2pcs = {}
for row in rows:
call_info_id2pcs[row.id] = row.pc_value_ls[:10]
column_name_type_ls = [("label", ("string","ID Name Phenotype")), ("date", ("date", "Date")), \
("lon",("number", "Longitude")), ("lat",("number", "Latitude")), \
("ecotypeid", ("number", "Ecotype ID")), ("name", ("string", "Native Name")), \
("country", ("string", "Country")), ("region", ("string", "Region")),\
("pc1", ("number","PC1")), ("pc2", ("number", "PC2")), ("pc3", ("number","PC3")), \
("pc4", ("number", "PC4")), ("pc5", ("number","PC5")), ("pc6", ("number", "PC6"))]
no_of_non_phenotype_cols = len(column_name_type_ls)
phenotype_id_included = []
#for phenotype_id in self.phenotype_method_id_list:
for phenotype_id in phenoData.col_id2col_index:
i = phenoData.col_id2col_index.get(phenotype_id)
if i is not None:
phenotype_name = phenoData.col_label_ls[i]
column_name_type_ls.append(("phenotype %s"%phenotype_id, ("number", phenotype_name)))
phenotype_id_included.append(phenotype_id)
description = dict(column_name_type_ls)
rows = model.db.metadata.bind.execute("select * from view_call where call_method_id=%s"%(call_method_id))
return_ls = []
phenoData_row_id_type = type(phenoData.row_id2row_index.keys()[0])
for row in rows:
pcs = call_info_id2pcs.get(row.call_info_id)
if pcs:
pc_value_ls = [getattr(call_info_pc, 'pc_value') for call_info_pc in pcs]
else:
pc_value_ls = [0]*10
label = '%s ID:%s'%(row.nativename, row.ecotype_id)
data = dict(date=datetime.date(2009,2,3), ecotypeid=row.ecotype_id, label=label, name=row.nativename, \
lat=row.latitude, lon=row.longitude,\
pc1=pc_value_ls[0], pc2=pc_value_ls[1], pc3=pc_value_ls[2], pc4=pc_value_ls[3], \
pc5=pc_value_ls[4], pc6=pc_value_ls[5], \
country=row.country, region=row.region)
# construct a tuple with (phenotype_name, phenotype_value) as entry, incorporated into data in the end
phenotype_name_value_tup_ls = [] #
for i in range(no_of_non_phenotype_cols, len(column_name_type_ls)):
phenotype_name = column_name_type_ls[i][0]
phenotype_id = phenotype_id_included[i-no_of_non_phenotype_cols]
pheno_row_index = phenoData.row_id2row_index.get(phenoData_row_id_type(row.ecotype_id))
pheno_col_index = phenoData.col_id2col_index.get(phenotype_id)
if pheno_row_index is not None and pheno_col_index is not None:
phenotype_value = phenoData.data_matrix[pheno_row_index][pheno_col_index]
if phenotype_value == 'NA' or numpy.isnan(phenotype_value):
phenotype_value = None
else:
phenotype_value = None #numpy.nan can't be recognized by ToJSon()
phenotype_name_value_tup_ls.append((phenotype_name, phenotype_value))
data.update(dict(phenotype_name_value_tup_ls))
return_ls.append(data)
data_table = gviz_api.DataTable(description)
data_table.LoadData(return_ls)
column_ls = [row[0] for row in column_name_type_ls]
json_result = data_table.ToJSon(columns_order=column_ls) #ToJSonResponse only works with google.visualization.Query
return json_result
def type(cls, id=None, TypeClass=model.Stock_250kDB.CandidateGeneTopSNPTestRMType, \
template='/display_top_snp_test_rm_type.html', \
phenotype_id_ls_str = '1-7,39-61,80-82,9-13,32-38,65-74', \
list_type_id_ls_str = '3,6,8,28,51,64,65,68,71,76,129,24,29,30,130,131',\
analysis_method_id_ls_str = '1,5,6,7',\
figure_type=1, call_method_id=17):
"""
2009-10-13
allow user to specify phenotype_id_ls_str and list_type_id_ls_str in URL
add figure_type argument:
1: candidate vs non-candidate ratio (in db beforehand)
2: 2D matrix-plot of pvalue for enrichment statistic. row is cutoff. column is distance between gene and SNP.
add call_method_id argument
add argument analysis_method_id_ls_str
2008-10-30
generalized so that DisplayResultsGene could call it
"""
ResultsMethod = model.Stock_250kDB.ResultsMethod
if id is None:
id = request.params.get('id', '1')
phenotype_id_ls_str = request.params.get('phenotype_id_ls_str', phenotype_id_ls_str) # 2009-10-13
list_type_id_ls_str = request.params.get('list_type_id_ls_str', list_type_id_ls_str) # 2009-10-13
analysis_method_id_ls_str = request.params.get('analysis_method_id_ls_str', analysis_method_id_ls_str) # 2009-10-13
c.figure_type = request.params.get('figure_type', figure_type) # 2009-10-13
call_method_id = request.params.get('call_method_id', call_method_id) # 2009-10-13
phenotype_id_ls = getListOutOfStr(phenotype_id_ls_str, data_type=str)
list_type_id_ls = getListOutOfStr(list_type_id_ls_str, data_type=str)
analysis_method_id_ls = getListOutOfStr(analysis_method_id_ls_str, data_type=int)
#extra_tables = ' %s c '%(CandidateGeneTopSNPTestRM.table.name)
extra_condition = 'p.id in (%s)'%(','.join(phenotype_id_ls))
c.phenotype_info = hc.getPhenotypeInfo(extra_condition=extra_condition, extra_tables=None)
#extra_condition = 's.type_id =%s'%id
extra_condition = 'p.id in (%s)'%(','.join(list_type_id_ls))
c.list_info = hc.getListTypeInfo(extra_condition=extra_condition)
data_matrix = []
no_of_rows = len(c.list_info.list_type_id_ls)
no_of_cols = len(c.phenotype_info.phenotype_method_id_ls)
for i in range(no_of_rows):
data_matrix.append([None]*no_of_cols)
c.type = TypeClass.get(id)
#2008-10-29 a faster way to come up the data_matrix but data is not guaranteed inside
rows = ResultsMethod.query.filter(ResultsMethod.phenotype_method_id.in_(map(int, phenotype_id_ls))).\
filter(ResultsMethod.analysis_method_id.in_(analysis_method_id_ls)).\
filter_by(call_method_id=call_method_id).\
order_by(ResultsMethod.analysis_method_id)
counter = 0
for row in rows:
col_index = c.phenotype_info.phenotype_method_id2index.get(row.phenotype_method_id)
if col_index is None:
continue
for list_type_id, row_index in c.list_info.list_type_id2index.iteritems():
if list_type_id>0:
if data_matrix[row_index][col_index] is None:
data_matrix[row_index][col_index] = []
data_matrix[row_index][col_index].append((row.id, row.analysis_method.short_name))
counter +=1
"""
rows = CandidateGeneTopSNPTestRM.query.filter_by(type_id=id)
for row in rows:
row_index = c.list_info.list_type_id2index[row.list_type_id]
col_index = c.phenotype_info.phenotype_method_id2index[row.result.phenotype_method_id]
if data_matrix[row_index][col_index] is None:
data_matrix[row_index][col_index] = Set()
data_matrix[row_index][col_index].add(row.results_id)
counter +=1
"""
c.counter = counter
c.data_matrix = data_matrix
return render(template)
