def write_strains_with_HI_and_sequence(flutype='H3N2'):
HI_titers = read_tables(flutype)
HI_trevor = read_trevor_table(flutype)
HI_strains = set(HI_titers.index)
HI_strains.update(HI_trevor[0])
from Bio import SeqIO
good_strains = set()
with myopen("data/"+flutype+"_strains_with_HI.fasta", 'w') as outfile, \
myopen("data/"+flutype+"_gisaid_epiflu_sequence.fasta", 'r') as infile:
for seq_rec in SeqIO.parse(infile, 'fasta'):
tmp_name = seq_rec.description.split('|')[0].strip()
reduced_name = HI_fix_name(tmp_name)
if reduced_name in HI_strains and (reduced_name
not in good_strains):
SeqIO.write(seq_rec, outfile, 'fasta')
good_strains.add(reduced_name)
titer_count = defaultdict(int)
measurements = get_all_titers_flat(flutype)
for ii, rec in measurements.iterrows():
test, ref, src_id, val = rec
titer_count[test] += 1
with myopen("data/" + flutype + "_HI_strains.txt",
'w') as HI_strain_outfile:
for strain, count in sorted(titer_count.items(),
key=lambda x: x[1],
reverse=True):
HI_strain_outfile.write(strain + '\t' + str(count) + '\n')
if fix_name(strain) != strain:
HI_strain_outfile.write(
fix_name(strain) + '\t' + str(count) + '\n')
def write_strains_with_HI_and_sequence(flutype='H3N2'):
HI_titers = read_tables(flutype)
HI_trevor = read_trevor_table(flutype)
HI_strains = set(HI_titers.index)
HI_strains.update(HI_trevor[0])
from Bio import SeqIO
good_strains = set()
with myopen("data/"+flutype+"_strains_with_HI.fasta", 'w') as outfile, \
myopen("data/"+flutype+"_gisaid_epiflu_sequence.fasta", 'r') as infile:
for seq_rec in SeqIO.parse(infile, 'fasta'):
tmp_name = seq_rec.description.split('|')[0].strip()
reduced_name = HI_fix_name(tmp_name)
if reduced_name in HI_strains and (reduced_name not in good_strains):
SeqIO.write(seq_rec, outfile,'fasta')
good_strains.add(reduced_name)
titer_count = defaultdict(int)
measurements = get_all_titers_flat(flutype)
for ii, rec in measurements.iterrows():
test, ref, src_id, val = rec
titer_count[test]+=1
with myopen("data/"+flutype+"_HI_strains.txt", 'w') as HI_strain_outfile:
for strain, count in sorted(titer_count.items(), key=lambda x:x[1], reverse=True):
HI_strain_outfile.write(strain + '\t'+str(count)+'\n')
if fix_name(strain)!=strain:
HI_strain_outfile.write(fix_name(strain) + '\t'+str(count)+'\n')
def check_sources(self):
self.source_HIs = defaultdict(dict)
with myopen(self.HI_fname, 'r') as infile:
for line in infile:
test, ref_virus, serum, src_id, val_str = line.strip().split()
try:
val = float(val_str)
if not np.isnan(val):
self.source_HIs[src_id][test,
(ref_virus,
serum)] = self.normalize(
(ref_virus, serum),
float(val))
except:
print(test, ref_virus, serum, src_id, float(val))
self.source_validation = {}
for src_id in self.source_HIs:
print('\n############### \n', src_id, '\n############### \n')
print("number of measurements:", len(self.source_HIs[src_id]))
try:
n_checks = self.validate(
validation_set=self.source_HIs[src_id],
incl_ref_strains='no')
self.source_validation[src_id] = [
self.abs_error, self.rms_error, self.slope, self.intercept,
n_checks
]
except:
print("skipped due to too few measurements")
def write_flat_HI_titers(flutype = 'H3N2', fname = None):
measurements = get_all_titers_flat(flutype)
with myopen('data/'+flutype+'_HI_strains.txt') as infile:
strains = [HI_fix_name(line.strip().split('\t')[0]).upper() for line in infile]
if fname is None:
fname = 'data/'+flutype+'_HI_titers.txt'
written = 0
skipped = 0
with myopen(fname, 'w') as outfile:
for ii, rec in measurements.iterrows():
test, ref, src_id, val = rec
if HI_fix_name(test).upper() in strains and HI_fix_name(rec[1][0]).upper() in strains:
outfile.write('\t'.join(map(str, [test, ref[0], ref[1], src_id, val]))+'\n')
written+=1
else:
skipped+=1
print "written",written,"records"
print "skipped",skipped,"records"
def load(self):
from cPickle import load
for attr_name, fname in self.file_dumps.iteritems():
if os.path.isfile(fname):
with myopen(fname, 'r') as ifile:
if attr_name == 'tree':
continue
else:
setattr(self, attr_name, load(ifile))
fname = self.file_dumps['tree']
if os.path.isfile(fname):
self.build_tree(fname)
def dump(self):
from cPickle import dump
from Bio import Phylo
for attr_name, fname in self.file_dumps.iteritems():
if hasattr(self, attr_name):
print("dumping", attr_name)
if attr_name == 'seqs': self.seqs.raw_seqs = None
with myopen(fname, 'wb') as ofile:
if attr_name == 'tree':
Phylo.write(self.tree.tree, ofile, 'newick')
else:
dump(getattr(self, attr_name), ofile, -1)
def dump(self):
from cPickle import dump
from Bio import Phylo
for attr_name, fname in self.file_dumps.iteritems():
if hasattr(self,attr_name):
print("dumping",attr_name)
if attr_name=='seqs': self.seqs.raw_seqs = None
with myopen(fname, 'wb') as ofile:
if attr_name=='tree':
Phylo.write(self.tree.tree, ofile, 'newick')
else:
dump(getattr(self,attr_name), ofile, -1)
def load(self):
from cPickle import load
for attr_name, fname in self.file_dumps.iteritems():
if os.path.isfile(fname):
with myopen(fname, 'r') as ifile:
if attr_name=='tree':
continue
else:
setattr(self, attr_name, load(ifile))
fname = self.file_dumps['tree']
if os.path.isfile(fname):
self.build_tree(fname)
def write_flat_HI_titers(flutype='H3N2', fname=None):
measurements = get_all_titers_flat(flutype)
with myopen('data/' + flutype + '_HI_strains.txt') as infile:
strains = [
HI_fix_name(line.strip().split('\t')[0]).upper() for line in infile
]
if fname is None:
fname = 'data/' + flutype + '_HI_titers.txt'
written = 0
skipped = 0
with myopen(fname, 'w') as outfile:
for ii, rec in measurements.iterrows():
test, ref, src_id, val = rec
if HI_fix_name(test).upper() in strains and HI_fix_name(
rec[1][0]).upper() in strains:
outfile.write(
'\t'.join(map(str, [test, ref[0], ref[1], src_id, val])) +
'\n')
written += 1
else:
skipped += 1
print "written", written, "records"
print "skipped", skipped, "records"
def read_HI_titers(self, fname):
strains = set()
measurements = defaultdict(list)
sources = set()
with myopen(fname, 'r') as infile:
for line in infile:
entries = line.strip().split()
test, ref_virus, serum, src_id, val = (entries[0], entries[1],
entries[2], entries[3],
float(entries[4]))
ref = (ref_virus, serum)
if src_id not in self.excluded_tables:
try:
measurements[(test, (ref_virus, serum))].append(val)
strains.update([test, ref])
sources.add(src_id)
except:
print(line.strip())
return measurements, strains, sources
def read_trevor_table(flutype):
trevor_table = 'data/'+flutype+'_HI.tsv'
import csv
measurements = []
sera = set()
strains = set()
if os.path.isfile(trevor_table):
with myopen(trevor_table) as infile:
table_reader = csv.reader(infile, delimiter="\t")
header = table_reader.next()
for row in table_reader:
val = titer_to_number(row[6])
if not np.isnan(val):
strains.add(HI_fix_name(row[1]))
serum = (HI_fix_name(row[4]), row[3])
src_id = row[-1]
sera.add(serum)
measurements.append([HI_fix_name(row[1]), serum, src_id, val])
else:
print trevor_table, "not found"
print "trevor total:", len(measurements), "measurements"
return strains, sera, pd.DataFrame(measurements)
def read_trevor_table(flutype):
trevor_table = 'data/' + flutype + '_HI.tsv'
import csv
measurements = []
sera = set()
strains = set()
if os.path.isfile(trevor_table):
with myopen(trevor_table) as infile:
table_reader = csv.reader(infile, delimiter="\t")
header = table_reader.next()
for row in table_reader:
val = titer_to_number(row[6])
if not np.isnan(val):
strains.add(HI_fix_name(row[1]))
serum = (HI_fix_name(row[4]), row[3])
src_id = row[-1]
sera.add(serum)
measurements.append(
[HI_fix_name(row[1]), serum, src_id, val])
else:
print trevor_table, "not found"
print "trevor total:", len(measurements), "measurements"
return strains, sera, pd.DataFrame(measurements)
def parse_HI_matrix(fname):
from string import strip
import csv
name_abbrev = {
'HK': "HONGKONG",
'SWITZ': "SWITZERLAND",
'VIC': "VICTORIA",
'STOCK': "STOCKHOLM",
'STHAFR': "SOUTHAFRICA",
'SAFRICA': "SOUTHAFRICA",
"ENG": "ENGLAND",
"NIB-85": "A/ALMATY/2958/2013",
'NOR': 'NORWAY',
'NTHCAROL': "NORTHCAROLINA",
'ALA': "ALABAMA",
'NY': "NEWYORK",
"GLAS": "GLASGOW",
"AL": "ALABAMA",
"NETH": "NETHERLANDS",
"FIN": "FINLAND",
"BRIS": "BRISBANE",
"MARY": "MARYLAND",
"ST.P'BURG": "ST.PETERSBURG",
'CAL': 'CALIFORNIA',
'AUCK': 'AUCKLAND',
"C'CHURCH": 'CHRISTCHURCH',
'CHCH': 'CHRISTCHURCH',
'ASTR': 'ASTRAKHAN',
'ASTRAK': 'ASTRAKHAN',
'ST.P': "ST.PETERSBURG",
'ST P': "ST.PETERSBURG",
'STP': "ST.PETERSBURG",
'JHB': 'JOHANNESBURG',
'FOR': 'FORMOSA',
'MAL': 'MALAYSIA',
'STHAUS': 'SOUTHAUSTRALIA',
'FL': 'FLORIDA',
'MASS': 'MASSACHUSETTS',
'NOVO': 'NOVOSIBIRSK',
'WIS': 'WISCONSIN',
'BANG': 'BANGLADESH',
'EG': 'EGYPT'
}
src_id = fname.split('/')[-1]
print fname
with myopen(fname) as infile:
csv_reader = csv.reader(infile)
# parse sera
row1 = csv_reader.next()
row2 = csv_reader.next()
row3 = csv_reader.next()
ref_sera = [[HI_fix_name(e1 + '/' + e2),
e3.replace(' ', '')]
for e1, e2, e3 in zip(row1, row2, row3)[4:]]
for ri in xrange(len(ref_sera)):
abbr = ref_sera[ri][0].split('/')[1].rstrip('01234566789')
if abbr in name_abbrev:
ref_sera[ri][0] = HI_fix_name(ref_sera[ri][0].replace(
abbr, name_abbrev[abbr]))
else:
ref_sera[ri][0] = HI_fix_name(ref_sera[ri][0])
# strip numbers
tmp = ref_sera[ri][0].split('/')
ref_sera[ri][0] = '/'.join([tmp[0], tmp[1].rstrip('0123456789')] +
tmp[2:])
try:
y = int(ref_sera[ri][0].split('/')[-1])
if y < 100:
if y < 20:
ref_sera[ri][0] = '/'.join(
ref_sera[ri][0].split('/')[:-1]) + '/' + str(2000 +
y)
else:
ref_sera[ri][0] = '/'.join(
ref_sera[ri][0].split('/')[:-1]) + '/' + str(1900 +
y)
except:
print ref_sera[ri]
fields = [
'source', 'ref/test', 'genetic group', 'collection date',
'passage history'
] + map(tuple, ref_sera)
#print fields
for row in csv_reader: # advance until the reference virus
if row[0].startswith('REFERENCE'):
break
ref_strains = []
ref_matrix = []
for row in csv_reader:
if row[0].startswith('TEST'):
break
else: # load matrices until the test virus section starts
ref_strains.append(HI_fix_name(row[0].strip()))
ref_matrix.append([src_id, 'ref'] + map(strip, row[1:4]) +
map(titer_to_number, row[4:]))
test_strains = []
test_matrix = []
for row in csv_reader: # load test viruses until it is no longer an A/ flu name
if not (row[0].startswith('A/') or row[0].startswith('B/')):
break
else:
test_strains.append(HI_fix_name(row[0].strip()))
test_matrix.append([src_id, 'test'] + map(strip, row[1:4]) +
map(titer_to_number, row[4:]))
print len(ref_sera), ref_sera
print len(ref_strains), len(test_strains)
HI_table = pd.DataFrame(ref_matrix + test_matrix,
index=ref_strains + test_strains,
columns=fields)
return HI_table
def parse_HI_matrix(fname):
from string import strip
import csv
name_abbrev = {'HK':"HONGKONG", 'SWITZ':"SWITZERLAND", 'VIC':"VICTORIA", 'STOCK':"STOCKHOLM",
'STHAFR':"SOUTHAFRICA", 'SAFRICA':"SOUTHAFRICA", "ENG":"ENGLAND", "NIB-85":"A/ALMATY/2958/2013", 'NOR':'NORWAY',
'NTHCAROL':"NORTHCAROLINA",'ALA':"ALABAMA", 'NY':"NEWYORK", "GLAS":"GLASGOW", "AL":"ALABAMA",
"NETH":"NETHERLANDS", "FIN":"FINLAND", "BRIS":"BRISBANE", "MARY":"MARYLAND",
"ST.P'BURG":"ST.PETERSBURG", 'CAL':'CALIFORNIA', 'AUCK':'AUCKLAND', "C'CHURCH":'CHRISTCHURCH',
'CHCH':'CHRISTCHURCH', 'ASTR':'ASTRAKHAN', 'ASTRAK':'ASTRAKHAN', 'ST.P':"ST.PETERSBURG",'ST P':"ST.PETERSBURG",'STP':"ST.PETERSBURG",
'JHB':'JOHANNESBURG', 'FOR':'FORMOSA','MAL':'MALAYSIA', 'STHAUS':'SOUTHAUSTRALIA',
'FL':'FLORIDA', 'MASS':'MASSACHUSETTS','NOVO':'NOVOSIBIRSK','WIS':'WISCONSIN','BANG':'BANGLADESH','EG':'EGYPT' }
src_id = fname.split('/')[-1]
print fname
with myopen(fname) as infile:
csv_reader = csv.reader(infile)
# parse sera
row1 = csv_reader.next()
row2 = csv_reader.next()
row3 = csv_reader.next()
ref_sera = [[HI_fix_name(e1+'/'+e2), e3.replace(' ','')] for e1,e2,e3 in zip(row1, row2, row3)[4:]]
for ri in xrange(len(ref_sera)):
abbr = ref_sera[ri][0].split('/')[1].rstrip('01234566789')
if abbr in name_abbrev:
ref_sera[ri][0] = HI_fix_name(ref_sera[ri][0].replace(abbr, name_abbrev[abbr]))
else:
ref_sera[ri][0] = HI_fix_name(ref_sera[ri][0])
# strip numbers
tmp = ref_sera[ri][0].split('/')
ref_sera[ri][0] = '/'.join([tmp[0], tmp[1].rstrip('0123456789')]+tmp[2:])
try:
y = int(ref_sera[ri][0].split('/')[-1])
if y<100:
if y<20:
ref_sera[ri][0] = '/'.join(ref_sera[ri][0].split('/')[:-1])+'/'+str(2000+y)
else:
ref_sera[ri][0] = '/'.join(ref_sera[ri][0].split('/')[:-1])+'/'+str(1900+y)
except:
print ref_sera[ri]
fields = ['source','ref/test', 'genetic group', 'collection date', 'passage history']+map(tuple, ref_sera)
#print fields
for row in csv_reader: # advance until the reference virus
if row[0].startswith('REFERENCE'):
break
ref_strains = []
ref_matrix = []
for row in csv_reader:
if row[0].startswith('TEST'):
break
else: # load matrices until the test virus section starts
ref_strains.append(HI_fix_name(row[0].strip()))
ref_matrix.append([src_id,'ref']+map(strip, row[1:4])+map(titer_to_number, row[4:]))
test_strains = []
test_matrix = []
for row in csv_reader: # load test viruses until it is no longer an A/ flu name
if not (row[0].startswith('A/') or row[0].startswith('B/')):
break
else:
test_strains.append(HI_fix_name(row[0].strip()))
test_matrix.append([src_id,'test']+map(strip,row[1:4])+map(titer_to_number, row[4:]))
print len(ref_sera), ref_sera
print len(ref_strains), len(test_strains)
HI_table = pd.DataFrame(ref_matrix+test_matrix, index = ref_strains+test_strains, columns= fields)
return HI_table
