def run(self): """ 2008-08-19 """ if self.debug: import pdb pdb.set_trace() db = Stock_250kDB.Stock_250kDB(drivername=self.drivername, username=self.db_user, password=self.db_passwd, hostname=self.hostname, database=self.dbname, schema=self.schema) db.setup(create_tables=False) session = db.session total_gene_id_ls = get_total_gene_ls(db.metadata.bind) no_of_total_genes = len(total_gene_id_ls) #no_of_total_genes = self.getNoOfTotalGenes(db, self.gene_table, self.tax_id) #if self.commit: # session.begin() _type = self.getTopSNPTestType(self.get_closest, self.min_MAF, \ self.allow_two_sample_overlapping, self.results_type,\ self.test_type_id, self.null_distribution_type_id) snps_context_wrapper = self.dealWithSnpsContextWrapper(self.snps_context_picklef, self.min_distance, self.get_closest) pd = PassingData(list_type_id=self.list_type_id, snps_context_wrapper=snps_context_wrapper, \ no_of_total_genes=no_of_total_genes, results_directory=self.results_directory, \ min_MAF=self.min_MAF, get_closest=self.get_closest, min_distance=self.min_distance,\ no_of_top_snps=self.no_of_top_snps, min_sample_size=self.min_sample_size, test_type_id=self.test_type_id, \ results_type=self.results_type, no_of_permutations=self.no_of_permutations,\ no_of_min_breaks=self.no_of_min_breaks, type_id=_type.id,\ null_distribution_type_id=self.null_distribution_type_id,\ allow_two_sample_overlapping=self.allow_two_sample_overlapping, total_gene_id_ls=total_gene_id_ls,\ min_score=self.min_score, commit=self.commit) if getattr(self, 'output_fname', None): writer = csv.writer(open(self.output_fname, 'w'), delimiter='\t') header_row = [] for column in CandidateGeneTopSNPTest.c.keys(): header_row.append(column) writer.writerow(header_row) else: writer = None #2008-10-31 setting up list accordingly if self.min_score: pd.min_score_ls = [self.min_score] else: pd.no_of_top_snps_ls = [self.no_of_top_snps] for results_id in self.results_id_ls: pd.results_id = results_id #self.runEnrichmentTestToGetNullData(session, pd) return_data = self.runHGTest(pd) result = return_data.result_ls[0] if result is not None: result.type = _type #assign the type here row = [] for column in result.c.keys(): row.append(getattr(result, column)) print '%s: %s'%(column, row[-1]) if writer: writer.writerow(row) session.save(result) if self.commit: session.flush()
def run(self):
"""
2008-08-20
"""
self.communicator = MPI.world.duplicate()
node_rank = self.communicator.rank
free_computing_nodes = range(1, self.communicator.size-1) #exclude the 1st and last node
free_computing_node_set = Set(free_computing_nodes)
output_node_rank = self.communicator.size-1
#2008-10-30 comment out because computing node is gonna save the stuff itself.
if node_rank!=output_node_rank: #to reduce the number of connections/queries to the master
self.hostname = self.alter_hostname
db = Stock_250kDB.Stock_250kDB(drivername=self.drivername, username=self.db_user,
password=self.db_passwd, hostname=self.hostname, database=self.dbname, schema=self.schema)
db.setup(create_tables=False)
session = db.session
if self.results_type==1:
ResultsClass = Stock_250kDB.ResultsMethod
TestResultClass = Stock_250kDB.CandidateGeneTopSNPTestRM
elif self.results_type==2:
ResultsClass = Stock_250kDB.ResultsByGene
TestResultClass = Stock_250kDB.CandidateGeneTopSNPTest
elif self.results_type==3:
ResultsClass = Stock_250kDB.ResultsMethod
TestResultClass = Stock_250kDB.CandidateGeneTopSNPTestRG
else:
sys.stderr.write("Invalid results type : %s.\n"%pd.results_type)
sys.exit(3)
if node_rank == 0:
pdata_for_computing = PassingData()
pdata_for_computing.total_gene_id_ls = get_total_gene_ls(db.metadata.bind)
pdata_for_computing.no_of_total_genes = len(pdata_for_computing.total_gene_id_ls)
param_obj = PassingData(call_method_id=self.call_method_id, \
analysis_method_id=getattr(self, 'analysis_method_id', None),\
analysis_method_id_ls=getattr(self, 'analysis_method_id_ls', None),\
phenotype_method_id_ls=getattr(self, 'phenotype_method_id_ls', None),\
list_type_id_ls=self.list_type_id_ls, \
results_type=self.results_type)
params_ls = self.generate_params(param_obj, self.min_no_of_genes)
cutoff_ls = self.generate_cutoff_ls(self.no_of_top_snps, self.min_score, self.rank_gap, self.stop_rank, self.window_with_same_rank_gap)
params_ls = self.addCutoffToParamsLs(cutoff_ls, params_ls)
pdata_for_computing.snps_context_wrapper = self.dealWithSnpsContextWrapper(self.snps_context_picklef, self.min_distance, self.get_closest)
if self.debug:
params_ls = params_ls[:100]
pdata_for_computing_pickle = cPickle.dumps(pdata_for_computing, -1)
for node in free_computing_nodes: #send it to the computing_node
sys.stderr.write("passing initial data to nodes from %s to %s ... "%(node_rank, node))
self.communicator.send(pdata_for_computing_pickle, node, 0)
sys.stderr.write(".\n")
del pdata_for_computing_pickle
del pdata_for_computing
elif node_rank in free_computing_node_set:
data, source, tag = self.communicator.receiveString(0, 0)
data = cPickle.loads(data)
sys.stderr.write(".\n")
else:
pass
_type = self.getTopSNPTestType(self.get_closest, self.min_MAF, \
self.allow_two_sample_overlapping, self.results_type,\
self.test_type_id, self.null_distribution_type_id)
self.synchronize()
if node_rank == 0:
parameter_list = [params_ls]
self.input_node(parameter_list, free_computing_nodes, input_handler=self.input_handler, message_size=self.message_size)
elif node_rank in free_computing_node_set:
comp_param_obj = PassingData(snps_context_wrapper=data.snps_context_wrapper, \
results_directory=self.results_directory, min_MAF=self.min_MAF,\
no_of_total_genes=data.no_of_total_genes, \
total_gene_id_ls=data.total_gene_id_ls,\
type_id=_type.id, #_type is placeholder. output_node decides on this.
session=session)
self.computing_node(comp_param_obj, self.computing_node_handler)
else:
if getattr(self, 'output_fname', None):
writer = csv.writer(open(self.output_fname, 'w'), delimiter='\t')
header_row = []
for column in TestResultClass.c.keys():
header_row.append(column)
writer.writerow(header_row)
else:
writer = None
output_param_obj = PassingData(writer=writer, session=session, commit=self.commit, TestResultClass=TestResultClass,
_type=_type)
self.output_node(free_computing_nodes, output_param_obj, self.output_node_handler)
del writer
self.synchronize() #to avoid some node early exits
def pick_candidate_genes(self, pd):
"""
2009-4-10
rank is now part of the whole unique constraint in table Stock_250kDB.ResultsGene.
2008-11-12
if entry already exists in ResultsGene, make sure it doesn't have the current type associated with
upon failure of 'session.flush()', expunge the current entry and report error. avoid failure of the whole program.
2008-10-28
"""
sys.stderr.write("Picking candidates genes from results (id=%s) for list_type (id=%s) ..."%(pd.results_id, pd.list_type_id))
rm = Stock_250kDB.ResultsMethod.get(pd.results_id)
session = getattr(pd, 'session', None)
if session is None:
sys.stderr.write("session is None. no db connection.\n")
return None
if pd.list_type_id==0:
total_gene_id_ls = get_total_gene_ls(Stock_250kDB.ResultsMethod.table.bind)
candidate_gene_set = Set(total_gene_id_ls)
else:
candidate_gene_set = self.dealWithCandidateGeneList(pd.list_type_id, return_set=True) #internal cache
pd.construct_data_obj_id2index = False #default in getResultMethodContent is True
pd.construct_chr_pos2index = False #no need for this as well
pd.need_candidate_association = True
pd.need_snp_index = False
pd.candidate_gene_set = candidate_gene_set
return_data = self.prepareDataForPermutationRankTest(rm, pd.snps_context_wrapper, pd)
counter = 0
for snps_id, disp_pos, gene_id, score, rank in return_data.candidate_association_ls:
rows = Stock_250kDB.ResultsGene.query.filter_by(snps_id=snps_id).\
filter_by(gene_id=gene_id).\
filter_by(results_id=pd.results_id).filter_by(rank=rank)
if rows.count()==1:
row = rows.first()
already_in_db = 0
if pd.type.id: #2008-11-12
for hist_type in row.types:
if hist_type.id==pd.type.id:
already_in_db = 1
break
if not already_in_db: #2008-11-12
row.types.append(pd.type)
session.save_or_update(row)
counter += 1
elif rows.count()>1:
sys.stderr.write("Error: more than 1 db entries with snps_id=%s, gene_id=%s, results_id=%s.\n"%\
(snps_id, gene_id, results_id))
continue
else:
row = Stock_250kDB.ResultsGene(snps_id=snps_id, gene_id=gene_id, disp_pos=disp_pos,\
results_id=pd.results_id, score=score, rank=rank)
row.types.append(pd.type)
session.save_or_update(row)
counter += 1
if pd.commit:
try: #2008-11-12 don't wanna db failure to bog down the whole program
session.flush()
except:
session.expunge(row)
for column in row.c.keys():
sys.stderr.write("\t%s=%s.\n"%(column, getattr(row, column)))
traceback.print_exc()
sys.stderr.write('%s.\n'%repr(sys.exc_info()))
sys.stderr.write("%s entries saved. Done.\n"%counter)
def pick_candidate_genes(self, pd):
"""
2010-4-19
report the total count as well.
2009-4-10
rank is now part of the whole unique constraint in table Stock_250kDB.ResultsGene.
2008-11-12
if entry already exists in ResultsGene, make sure it doesn't have the current type associated with
upon failure of 'session.flush()', expunge the current entry and report error. avoid failure of the whole program.
2008-10-28
"""
sys.stderr.write("Picking candidates genes from results (id=%s) for list_type (id=%s) ..."%\
(pd.results_id, pd.list_type_id))
rm = Stock_250kDB.ResultsMethod.get(pd.results_id)
session = getattr(pd, 'session', None)
if session is None:
sys.stderr.write("session is None. no db connection.\n")
return None
if pd.list_type_id==0:
total_gene_id_ls = get_total_gene_ls(Stock_250kDB.ResultsMethod.table.bind)
candidate_gene_set = Set(total_gene_id_ls)
else:
candidate_gene_set = self.dealWithCandidateGeneList(pd.list_type_id, return_set=True) #internal cache
pd.construct_data_obj_id2index = False #default in getResultMethodContent is True
pd.construct_chr_pos2index = False #no need for this as well
pd.need_candidate_association = True
pd.need_snp_index = False
pd.candidate_gene_set = candidate_gene_set
return_data = self.prepareDataForPermutationRankTest(rm, pd.snps_context_wrapper, pd)
counter = 0
total_count = 0
for snps_id, disp_pos, gene_id, score, rank in return_data.candidate_association_ls:
total_count += 1
rows = Stock_250kDB.ResultsGene.query.filter_by(snps_id=snps_id).\
filter_by(gene_id=gene_id).\
filter_by(results_id=pd.results_id).filter_by(rank=rank)
if rows.count()==1:
row = rows.first()
already_in_db = 0
if pd.type.id: #2008-11-12
for hist_type in row.types:
if hist_type.id==pd.type.id:
already_in_db = 1
break
if not already_in_db: #2008-11-12
row.types.append(pd.type)
session.save_or_update(row)
counter += 1
elif rows.count()>1:
if self.report:
sys.stderr.write("Error: more than 1 db entries with snps_id=%s, gene_id=%s, results_id=%s.\n"%\
(snps_id, gene_id, results_id))
continue
else:
row = Stock_250kDB.ResultsGene(snps_id=snps_id, gene_id=gene_id, disp_pos=disp_pos,\
results_id=pd.results_id, score=score, rank=rank)
row.types.append(pd.type)
session.add(row)
counter += 1
if pd.commit:
try: #2008-11-12 don't wanna db failure to bog down the whole program
session.flush()
except:
session.expunge(row)
for column in row.c.keys():
sys.stderr.write("\t%s=%s.\n"%(column, getattr(row, column)))
traceback.print_exc()
sys.stderr.write('%s.\n'%repr(sys.exc_info()))
sys.stderr.write("%s out of %s entries saved. Done.\n"%(counter, total_count))
def run(self):
"""
2008-12-08 if the plot under configuration is already in db, abort only if the program is gonna commit the database transaction.
2008-10-19
save figures in database if commit
"""
if self.debug:
import pdb
pdb.set_trace()
db = Stock_250kDB.Stock_250kDB(drivername=self.drivername, username=self.db_user,
password=self.db_passwd, hostname=self.hostname, database=self.dbname, schema=self.schema)
db.setup(create_tables=False)
session = db.session
#session.begin()
if self.results_type==1:
ResultsClass = Stock_250kDB.ResultsMethod
snps_context_wrapper = self.dealWithSnpsContextWrapper(self.snps_context_picklef, self.min_distance, self.get_closest)
elif self.results_type==2:
ResultsClass = Stock_250kDB.ResultsByGene
else:
sys.stderr.write("Invalid results type : %s.\n"%self.results_type)
return None
hist_type = self.getHistType(self.call_method_id, self.min_distance, self.get_closest, self.min_MAF, \
self.allow_two_sample_overlapping, self.results_type, self.null_distribution_type_id)
candidate_gene_list = self.getGeneList(self.list_type_id)
if len(candidate_gene_list)<self.min_sample_size:
sys.stderr.write("Candidate gene list of %s too small: %s.\n"%(self.list_type_id, len(candidate_gene_list)))
sys.exit(4)
#candidate_gene_list = [] #2009-01-12 just to plot the histogram of pvalue
candidate_gene_set = Set(candidate_gene_list)
list_type = Stock_250kDB.GeneListType.get(self.list_type_id)
if list_type is None:
sys.exit(3)
phenotype_id2results_id_ls = self.getResultsIDLs(db, ResultsClass, self.results_type, self.phenotype_id_ls, \
self.min_distance, self.get_closest, self.min_MAF, self.call_method_id)
param_data = PassingData(results_directory=self.results_directory, candidate_gene_list=candidate_gene_list, \
min_MAF=self.min_MAF, allow_two_sample_overlapping=self.allow_two_sample_overlapping, need_the_value=1, \
do_log10_transformation=False)
#need_the_value means to get the pvalue/score
#force no log10 transformation. otherwise, transformation based on analysis_method
if self.null_distribution_type_id==2 or self.null_distribution_type_id==3: #gw-looping or random gene list
snp_info = DrawSNPRegion.getSNPInfo(db)
candidate_gene_snp_index_ls = self.get_candidate_gene_snp_index_ls(candidate_gene_set, snp_info.chr_pos_ls, snps_context_wrapper)
no_of_snps = len(snp_info.chr_pos_ls)
no_of_permutations = no_of_snps/len(candidate_gene_snp_index_ls) + 1
param_data.chr_pos2index = snp_info.chr_pos2index #pass to getGenomeWideResultFromFile
if self.null_distribution_type_id==2:
non_candidate_gene_snp_index_ls = self.get_non_candidate_gene_snp_index_ls_by_permutation(candidate_gene_snp_index_ls, no_of_snps, no_of_permutations)
elif self.null_distribution_type_id == 3:
gene_id_ls = get_total_gene_ls(db.metadata.bind)
no_of_candidate_genes = len(candidate_gene_set)
non_candidate_gene_snp_index_ls = numpy.zeros(0, numpy.int)
while len(non_candidate_gene_snp_index_ls)<no_of_snps:
non_candidate_gene_set = Set(random.sample(gene_id_ls, no_of_candidate_genes))
_non_candidate_gene_snp_index_ls = self.get_candidate_gene_snp_index_ls(non_candidate_gene_set, snp_info.chr_pos_ls, snps_context_wrapper)
non_candidate_gene_snp_index_ls = numpy.hstack((non_candidate_gene_snp_index_ls, _non_candidate_gene_snp_index_ls))
for phenotype_id, results_id_ls in phenotype_id2results_id_ls.iteritems():
if hist_type.id: #hist_type already in database
rows = Stock_250kDB.ScoreRankHistogram.query.filter_by(phenotype_method_id=phenotype_id).\
filter_by(list_type_id=self.list_type_id).filter_by(hist_type_id=hist_type.id)
if rows.count()>0 and self.commit: #2008-12-08 only skip if the database transaction is gonna commit.
row = rows.first()
sys.stderr.write("Histogram already in database. id=%s, phenotype_id=%s, list_type_id=%s, hist_type_id=%s.\n"%\
(row.id, row.phenotype_method_id, row.list_type_id, row.hist_type_id))
continue
phenotype_method = Stock_250kDB.PhenotypeMethod.get(phenotype_id)
if not phenotype_method:
continue
score_rank_data_ls = []
sys.stderr.write("Checking phenotype %s (%s) on list_type %s (%s) ...\n"%\
(phenotype_method.id, phenotype_method.short_name, list_type.id, list_type.short_name))
for results_id in results_id_ls:
try:
rm = ResultsClass.get(results_id)
score_rank_data = None
if self.null_distribution_type_id==1:
if self.results_type==1:
permData = self.prepareDataForPermutationRankTest(rm, snps_context_wrapper, param_data)
if not permData:
continue
score_rank_data = PassingData(candidate_score_ls=permData.candidate_gene_snp_value_ls, \
candidate_rank_ls=permData.candidate_gene_snp_rank_ls,\
non_candidate_score_ls=permData.non_candidate_gene_snp_value_ls, non_candidate_rank_ls=permData.non_candidate_gene_snp_rank_ls,\
analysis_method=rm.analysis_method)
del permData
elif self.results_type==2:
score_rank_data = self.getScoreRankFromRBG(rm, candidate_gene_set, self.results_directory)
elif self.null_distribution_type_id==2 or self.null_distribution_type_id==3:
genome_wide_result = self.getResultMethodContent(rm, param_data.results_directory, param_data.min_MAF, pdata=param_data)
if not genome_wide_result:
continue
score_rank_data = self.getScoreRankFromPermIndexLs(genome_wide_result, candidate_gene_snp_index_ls, non_candidate_gene_snp_index_ls)
if score_rank_data:
score_rank_data.analysis_method = rm.analysis_method
if score_rank_data:
score_rank_data_ls.append(score_rank_data)
except:
sys.stderr.write("Exception happened for results_id=%s, phenotype_id=%s.\n"%(results_id, phenotype_id))
traceback.print_exc()
sys.stderr.write('%s.\n'%repr(sys.exc_info()))
continue
if score_rank_data_ls:
score_png_data, score_svg_data = self.plotHistForOnePhenotype(phenotype_method, list_type, score_rank_data_ls, self.output_dir, data_type='score', commit=self.commit)
rank_png_data, rank_svg_data = self.plotHistForOnePhenotype(phenotype_method, list_type, score_rank_data_ls, self.output_dir, data_type='rank', commit=self.commit)
if self.commit:
score_rank_hist = Stock_250kDB.ScoreRankHistogram(phenotype_method_id=phenotype_id, list_type_id=list_type.id)
score_rank_hist.hist_type = hist_type
score_rank_hist.score_hist = score_png_data.getvalue()
score_rank_hist.score_hist_svg = score_svg_data.getvalue()
score_rank_hist.rank_hist = rank_png_data.getvalue()
score_rank_hist.rank_hist_svg = rank_svg_data.getvalue()
session.save(score_rank_hist)
session.flush()
del score_png_data, score_svg_data, rank_png_data, rank_svg_data
"""
def run(self):
"""
2008-08-20
"""
self.communicator = MPI.world.duplicate()
node_rank = self.communicator.rank
free_computing_nodes = range(1, self.communicator.size -
1) #exclude the 1st and last node
free_computing_node_set = Set(free_computing_nodes)
output_node_rank = self.communicator.size - 1
#2008-10-30 comment out because computing node is gonna save the stuff itself.
if node_rank != output_node_rank: #to reduce the number of connections/queries to the master
self.hostname = self.alter_hostname
db = Stock_250kDB.Stock_250kDB(drivername=self.drivername,
username=self.db_user,
password=self.db_passwd,
hostname=self.hostname,
database=self.dbname,
schema=self.schema)
db.setup(create_tables=False)
session = db.session
if self.results_type == 1:
ResultsClass = Stock_250kDB.ResultsMethod
TestResultClass = Stock_250kDB.CandidateGeneTopSNPTestRM
elif self.results_type == 2:
ResultsClass = Stock_250kDB.ResultsByGene
TestResultClass = Stock_250kDB.CandidateGeneTopSNPTest
elif self.results_type == 3:
ResultsClass = Stock_250kDB.ResultsMethod
TestResultClass = Stock_250kDB.CandidateGeneTopSNPTestRG
else:
sys.stderr.write("Invalid results type : %s.\n" % pd.results_type)
sys.exit(3)
if node_rank == 0:
pdata_for_computing = PassingData()
pdata_for_computing.total_gene_id_ls = get_total_gene_ls(
db.metadata.bind)
pdata_for_computing.no_of_total_genes = len(
pdata_for_computing.total_gene_id_ls)
param_obj = PassingData(call_method_id=self.call_method_id, \
analysis_method_id=getattr(self, 'analysis_method_id', None),\
analysis_method_id_ls=getattr(self, 'analysis_method_id_ls', None),\
phenotype_method_id_ls=getattr(self, 'phenotype_method_id_ls', None),\
list_type_id_ls=self.list_type_id_ls, \
results_type=self.results_type)
params_ls = self.generate_params(param_obj, self.min_no_of_genes)
cutoff_ls = self.generate_cutoff_ls(self.no_of_top_snps,
self.min_score, self.rank_gap,
self.stop_rank,
self.window_with_same_rank_gap)
params_ls = self.addCutoffToParamsLs(cutoff_ls, params_ls)
pdata_for_computing.snps_context_wrapper = self.dealWithSnpsContextWrapper(
self.snps_context_picklef, self.min_distance, self.get_closest)
if self.debug:
params_ls = params_ls[:100]
pdata_for_computing_pickle = cPickle.dumps(pdata_for_computing, -1)
for node in free_computing_nodes: #send it to the computing_node
sys.stderr.write(
"passing initial data to nodes from %s to %s ... " %
(node_rank, node))
self.communicator.send(pdata_for_computing_pickle, node, 0)
sys.stderr.write(".\n")
del pdata_for_computing_pickle
del pdata_for_computing
elif node_rank in free_computing_node_set:
data, source, tag = self.communicator.receiveString(0, 0)
data = cPickle.loads(data)
sys.stderr.write(".\n")
else:
pass
_type = self.getTopSNPTestType(self.get_closest, self.min_MAF, \
self.allow_two_sample_overlapping, self.results_type,\
self.test_type_id, self.null_distribution_type_id)
self.synchronize()
if node_rank == 0:
parameter_list = [params_ls]
self.input_node(parameter_list,
free_computing_nodes,
input_handler=self.input_handler,
message_size=self.message_size)
elif node_rank in free_computing_node_set:
comp_param_obj = PassingData(snps_context_wrapper=data.snps_context_wrapper, \
results_directory=self.results_directory, min_MAF=self.min_MAF,\
no_of_total_genes=data.no_of_total_genes, \
total_gene_id_ls=data.total_gene_id_ls,\
type_id=_type.id, #_type is placeholder. output_node decides on this.
session=session)
self.computing_node(comp_param_obj, self.computing_node_handler)
else:
if getattr(self, 'output_fname', None):
writer = csv.writer(open(self.output_fname, 'w'),
delimiter='\t')
header_row = []
for column in TestResultClass.c.keys():
header_row.append(column)
writer.writerow(header_row)
else:
writer = None
output_param_obj = PassingData(writer=writer,
session=session,
commit=self.commit,
TestResultClass=TestResultClass,
_type=_type)
self.output_node(free_computing_nodes, output_param_obj,
self.output_node_handler)
del writer
self.synchronize() #to avoid some node early exits
