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 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 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 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 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 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 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 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)
