def readInMonkeysFromDB(self, db_vervet=None, countryIDList=None, maxLongitude=None, addOnlyVWPMonkeys=False):
"""
2012.12.2 add argument addOnlyVWPMonkeys
2012.11.26
"""
sys.stderr.write("Fetching all monkeys of country %s from db , maxLongitude = %s ..."%\
(utils.getStrOutOfList(countryIDList), maxLongitude))
monkeyID2Info = {}
query_string = "select * from view_individual"
where_condition_ls = ["country_id in (%s)"%(utils.getStrOutOfList(countryIDList)), \
"latitude is not null and longitude is not null"]
if maxLongitude is not None:
where_condition_ls.append(" longitude<=%s "%(maxLongitude))
if addOnlyVWPMonkeys:
where_condition_ls.append(" ucla_id ~ '^VWP' ")
if where_condition_ls:
query_string = "%s where %s"%(query_string, " and ".join(where_condition_ls))
query = db_vervet.metadata.bind.execute(query_string)
for row in query:
monkeyID2Info[row.ucla_id] = PassingData(latitude=row.latitude, longitude=row.longitude, sex=row.sex, db_id=row.id,\
country=row.country, site_name=row.site_name, \
alignment_id=None, alignment_depth=None)
properAlignment = db_vervet.getProperAlignmentGivenIndividualID(ucla_id=row.ucla_id)
if properAlignment:
monkeyID2Info[row.ucla_id].alignment_id = properAlignment.alignment_id
monkeyID2Info[row.ucla_id].alignment_depth = properAlignment.median_depth
sys.stderr.write("%s monkeys.\n"%(len(monkeyID2Info)))
return monkeyID2Info
def readInSequencedMonkeys(self, db_vervet=None, countryIDList=None):
"""
2012.11.26
"""
sys.stderr.write("Fetching sequenced monkeys of country %s from db ..."%\
(utils.getStrOutOfList(countryIDList)))
query = db_vervet.metadata.bind.execute("select * from view_individual_sequence where country_id in (%s)"%\
(utils.getStrOutOfList(countryIDList)))
monkey_UCLAID_set = set()
for row in query:
monkey_UCLAID_set.add(row.ucla_id)
sys.stderr.write("%s monkeys.\n"%(len(monkey_UCLAID_set)))
return monkey_UCLAID_set
def handleTitle(self, ):
"""
2012.8.16
add min_overlap_ratio, no of phenotypes, total_no_of_results into the title.
"""
if self.title:
title = self.title
else:
phenotype_method_id_ls = list(self.phenotype_method_id_set)
no_of_phenotypes = len(phenotype_method_id_ls)
title = 'min_overlap %s, #results %s, #phenotypes %s'%(utils.getStrOutOfList(list(self.min_overlap_ratio_set)),\
utils.getStrOutOfList(list(self.total_no_of_results_set)), no_of_phenotypes)
#title = yh_matplotlib.constructTitleFromTwoDataSummaryStat(self.invariantPData.x_ls, self.invariantPData.y_ls)
pylab.title(title)
return title
def discoverAssociationLocus(self, associationPeakGraph=None, min_overlap_ratio=0.1):
"""
2012.12.12 try to output the peaks that are associated with one locus. for each peak, output
* result-id
* phenotype id
* chromosome
* start
* stop
* start_locus
* stop_locus
* no_of_loci
* peak_locus
* peak-score
2012.11.20
2012.6.24
"""
sys.stderr.write("Discovering association loci from graph of %s nodes. %s edges. %s connected components..."%\
(associationPeakGraph.number_of_nodes(), associationPeakGraph.number_of_edges(), \
nx.number_connected_components(associationPeakGraph) ))
cc_graph_list = nx.connected_component_subgraphs(associationPeakGraph)
counter = 0
associationLocusList = []
for cc_graph in cc_graph_list:
#calculate connectivity of this component
ne = cc_graph.number_of_edges()
nn = cc_graph.number_of_nodes()
if nn>1:
connectivity = ne/float(nn*(nn-1)/2)
else:
connectivity = 1
start_ls = []
stop_ls = []
association_peak_ls = []
#get span of each node, then take median of all its start/stop
result_id_set = set()
chromosome_set = set() #should be only one chromosome
phenotype_id_set = set()
for n in cc_graph:
nodeObject = associationPeakGraph.node[n]
chromosome_set.add(nodeObject['chromosome'])
span = nodeObject['span']
start_ls.append(span[0])
stop_ls.append(span[1])
association_peak_ls.extend(nodeObject['association_peak_ls'])
result_id_set.add(nodeObject['result_id'])
phenotype_id_set.add(nodeObject['phenotype_method_id'])
if len(chromosome_set)>1:
sys.stderr.write("Error: %s chromosomes (%s) in one connected component.\n"%(len(chromosome_set), repr(chromosome_set)))
sys.exit(7)
median_start = numpy.median(start_ls)
median_stop = numpy.median(stop_ls)
no_of_results = len(result_id_set)
associationLocus = PassingDataList()
#assign each value separately to impose the order of variables in associationLocus's internal list
associationLocus.chromosome = chromosome_set.pop()
associationLocus.start=median_start
associationLocus.stop=median_stop
associationLocus.no_of_peaks=nn
associationLocus.connectivity=connectivity
associationLocus.no_of_results=no_of_results
associationLocus.association_peak_ls=association_peak_ls
phenotype_id_ls = list(phenotype_id_set)
phenotype_id_ls.sort()
associationLocus.phenotype_id_ls_in_str = utils.getStrOutOfList(phenotype_id_ls)
#PassingDataList is sortable via (chromosome, start, stop ...)
associationLocusList.append(associationLocus)
counter += 1
sys.stderr.write("%s association loci.\n"%(counter))
return associationLocusList
