def fetchTopCandidateGenesFromOneResultOneGeneList(self, id=None, type_id=None, list_type_id=0, \
max_rank=200):
"""
2009-7-2 split out of showTopCandidateGenesFromOneResultOneGeneList()
a server API which returns a google visualization table in json
used by templates underlying both showTopCandidateGenesFromOneResultOneGeneList and showResultsGeneForOnePhenotype
"""
results_id = request.params.get('id', id)
type_id = request.params.get('type_id', type_id)
list_type_id = int(request.params.get('list_type_id', list_type_id))
max_rank = request.params.get('max_rank', max_rank)
if max_rank is not None:
max_rank = int(max_rank)
c.max_rank = max_rank
c.type = ScoreRankHistogramType.get(type_id)
c.list_type = model.Stock_250kDB.GeneListType.get(list_type_id)
c.snp_gene_association_id2desc = self.snp_gene_association_id2desc
c.row_ls = []
c.counter = 0
if results_id is None:
results_id = request.params.get('results_id', None)
if not results_id:
call_method_id = request.params.get('call_method_id', None)
if call_method_id is None:
call_method_id = c.type.call_method_id
phenotype_method_id = request.params.get('phenotype_method_id', None)
analysis_method_id = request.params.get('analysis_method_id', None)
rm = ResultsMethod.query.filter_by(call_method_id=call_method_id).filter_by(phenotype_method_id=phenotype_method_id).\
filter_by(analysis_method_id=analysis_method_id).first()
if rm is None:
c.result = None
candidate_gene_set = set() #return nothing
return self.getAssociationsGivenGene(type_id, maxRank=1, results_id=results_id, candidate_gene_set=candidate_gene_set)
results_id = rm.id
if results_id is None:
candidate_gene_set = set() #return nothing
return self.getAssociationsGivenGene(type_id, maxRank=1, results_id=results_id, candidate_gene_set=candidate_gene_set)
from variation.src.GeneListRankTest import GeneListRankTest
if list_type_id>0: #2009-2-22
candidate_gene_set = GeneListRankTest.dealWithCandidateGeneList(list_type_id, return_set=True)
else:
candidate_gene_set = None
return self.getAssociationsGivenGene(type_id, maxRank=c.max_rank, results_id=results_id, candidate_gene_set=candidate_gene_set)
def getOneResultJsonData(cls, rm, min_MAF, no_of_top_snps):
"""
2009-4-24
refactored out of fetchOne()
called upon only if its return is not in db.
"""
log.info("Getting json_data from result %s ... \n"%rm.id)
param_data = PassingData(min_MAF=min_MAF)
genome_wide_result = GeneListRankTest.getResultMethodContent(rm, min_MAF=min_MAF, pdata=param_data)
max_value = genome_wide_result.max_value
chr2length = {}
max_length = 0
for chr, min_max_pos in genome_wide_result.chr2min_max_pos.iteritems():
chr2length[chr] = min_max_pos[1]-min_max_pos[0]
max_length = max(max_length, chr2length[chr])
return_ls = []
description = {"position": ("number", "Position"),"value": ("number", "-log Pvalue")}
chr2data = {}
for i in range(no_of_top_snps):
data_obj = genome_wide_result.get_data_obj_at_given_rank(i+1)
if data_obj.chromosome not in chr2data:
chr2data[data_obj.chromosome] = []
chr2data[data_obj.chromosome].append(dict(position=data_obj.position, value=data_obj.value))
json_result = {}
for chr in chr2data:
data_table = gviz_api.DataTable(description)
data_table.LoadData(chr2data[chr])
json_result[chr] = data_table.ToJSon(columns_order=("position", "value"))
#,order_by="position")
"""
for i in range(10000):
data_obj = genome_wide_result.get_data_obj_at_given_rank(i+1)
return_ls.append(dict(chromosome=data_obj.chromosome, position=data_obj.position, value=data_obj.value))
"""
result = {
'chr2data': json_result,
'chr2length': chr2length,
'max_value': max_value,
'max_length': max_length,
}
log.info("Done.\n")
return simplejson.dumps(result)
def fetchGeneList(self, id=None):
"""
2009-7-2
fixed and functional
2009-7-2
return a google visualization data table of genes based on gene list
"""
list_type_id = int(request.params.get('list_type_id', id))
column_name_type_ls = [("gene_symbol", ("string", "Symbol")), ("description", ("string", "Description")), \
("type_of_gene", ("string", "Type Of Gene")), \
("dbxrefs", ("string", "DB Cross References")), \
("gene_id", ("string","Gene ID")), ("chromosome", ("string","chromosome")),\
("strand", ("string","Strand")),\
("start", ("number","Start")),("stop", ("number","Stop"))]
description = dict(column_name_type_ls)
from variation.src.GeneListRankTest import GeneListRankTest
if list_type_id>0: #2009-2-22
candidate_gene_set = GeneListRankTest.dealWithCandidateGeneList(list_type_id, return_set=True)
else:
candidate_gene_set = set()
return_ls = []
counter = 0
for gene_id in candidate_gene_set:
entry = dict()
self.fillGVizDataEntryForOneGene(entry, gene_id, column_name_type_ls)
counter += 1
return_ls.append(entry)
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]