def run(self):
"""
2008-06-02
"""
if self.debug:
import pdb
pdb.set_trace()
if self.row_matching_by_which_value == 0:
snpData1 = SNPData(input_fname=self.input_fname1,
turn_into_array=1,
ignore_2nd_column=1)
else:
snpData1 = SNPData(input_fname=self.input_fname1,
turn_into_array=1)
snpData2 = SNPData(input_fname=self.input_fname2, turn_into_array=1)
if self.row_matching_by_which_value == 1 or self.row_matching_by_which_value == 2:
row_matching_by_which_value = self.row_matching_by_which_value - 1
else:
row_matching_by_which_value = None
twoSNPData = TwoSNPData(
SNPData1=snpData1,
SNPData2=snpData2,
debug=self.debug,
row_matching_by_which_value=row_matching_by_which_value)
newSnpData = twoSNPData.order2ndSNPDataRowsSameAs1stSNPData()
newSnpData.tofile(self.output_fname)
def qcDataMatrixVSsnpData(self, pdata, snps_name2snps_id, snpData2, curs,
session, readme):
"""
2008-08-16
split from run() to enable one_by_one option
"""
#swap the ecotype_id_ls and call_info_id_ls when passing them to SNPData. now strain_acc_list=ecotype_id_ls
snpData1 = SNPData(header=pdata.header, strain_acc_list=pdata.ecotype_id_ls, category_list=pdata.call_info_id_ls, data_matrix=pdata.data_matrix, \
min_probability=self.min_probability, call_method_id=self.call_method_id, col_id2id=snps_name2snps_id,\
max_call_info_mismatch_rate=self.max_call_info_mismatch_rate, snps_table='stock_250k.snps')
#snps_table is set to the stock_250k snps_table
twoSNPData = TwoSNPData(SNPData1=snpData1, SNPData2=snpData2, curs=curs, \
QC_method_id=self.QC_method_id, user=self.user, row_matching_by_which_value=0, debug=self.debug)
if self.run_type == 1:
row_id2NA_mismatch_rate = twoSNPData.cmp_row_wise()
elif self.run_type == 2:
twoSNPData.save_col_wise(session, readme)
row_id2NA_mismatch_rate = {}
else:
sys.stderr.write("run_type=%s is not supported.\n" % self.run_type)
sys.exit(5)
passingdata = PassingData()
passingdata.row_id2NA_mismatch_rate = row_id2NA_mismatch_rate
passingdata.row_id12row_id2 = twoSNPData.row_id12row_id2
return passingdata
def run(self):
if self.debug:
import pdb
pdb.set_trace()
db = StockDB.StockDB(drivername=self.drivername, username=self.db_user,
password=self.db_passwd, hostname=self.hostname, database=self.dbname)
db.setup(create_tables=False)
session = db.session
session.begin()
self.cmp_data_filename = self.findOutCmpDataFilename(self.cmp_data_filename, self.QC_method_id, StockDB.QCMethod)
header, strain_acc_list, category_list, data_matrix = read_data(self.cmp_data_filename)
strain_acc_list = map(int, strain_acc_list) #it's ecotypeid, cast it to integer to be compatible to the later ecotype_id_ls from db
snpData2 = SNPData(header=header, strain_acc_list=strain_acc_list, \
data_matrix=data_matrix) #category_list is not used.
readme = formReadmeObj(sys.argv, self.ad, StockDB.README)
session.save(readme)
import MySQLdb
conn = MySQLdb.connect(db=self.dbname, host=self.hostname, user = self.db_user, passwd = self.db_passwd)
curs = conn.cursor()
from dbSNP2data import dbSNP2data
snp_id2index, snp_id_list, snp_id2info = dbSNP2data.get_snp_id2index_m(curs, StockDB.Calls.table.name, StockDB.SNPs.table.name)
strain_info_data = self.get_strain_id_info(self.QC_method_id)
data_matrix = self.get_data_matrix(db, strain_info_data.strain_id2index, snp_id2index, StockDB.Calls.table.name)
strain_acc_list = [strain_info_data.strain_id2acc[strain_id] for strain_id in strain_info_data.strain_id_list]
category_list = [strain_info_data.strain_id2category[strain_id] for strain_id in strain_info_data.strain_id_list]
header = ['ecotypeid', 'strainid']
for snp_id in snp_id_list:
snp_name, chromosome, position = snp_id2info[snp_id]
header.append(snp_name)
snpData1 = SNPData(header=header, strain_acc_list=strain_acc_list, category_list=category_list, data_matrix=data_matrix, \
snps_table='stock.snps') #snps_table is set to the stock_250k snps_table
twoSNPData = TwoSNPData(SNPData1=snpData1, SNPData2=snpData2, curs=curs, \
QC_method_id=self.QC_method_id, user=self.db_user, row_matching_by_which_value=0, debug=self.debug)
if self.run_type==1:
row_id2NA_mismatch_rate = twoSNPData.cmp_row_wise()
elif self.run_type==2:
#twoSNPData.save_col_wise(session, readme) #2008-08-18 need to implement a new one for 149SNP
row_id2NA_mismatch_rate = {}
else:
sys.stderr.write("run_type=%s is not supported.\n"%self.run_type)
sys.exit(5)
if self.output_fname and self.run_type==1 and row_id2NA_mismatch_rate:
self.output_row_id2NA_mismatch_rate(row_id2NA_mismatch_rate, self.output_fname)
if self.run_type==1 and self.commit and not self.input_dir and row_id2NA_mismatch_rate:
#if self.input_dir is given, no db submission. call_info_id2fname here is fake, it's actually keyed by (array_id, ecotypeid)
#row_id2NA_mismatch_rate might be None if it's method 0.
self.submit_to_call_QC(session, row_id2NA_mismatch_rate, self.QC_method_id, self.db_user, \
twoSNPData.row_id12row_id2, readme)
if self.commit:
session.commit()
else:
session.rollback()
def qcDataMatrixVSsnpData(self, pdata, snps_name2snps_id, snpData2, curs, session, readme):
"""
2008-08-16
split from run() to enable one_by_one option
"""
# swap the ecotype_id_ls and call_info_id_ls when passing them to SNPData. now strain_acc_list=ecotype_id_ls
snpData1 = SNPData(
header=pdata.header,
strain_acc_list=pdata.ecotype_id_ls,
category_list=pdata.call_info_id_ls,
data_matrix=pdata.data_matrix,
min_probability=self.min_probability,
call_method_id=self.call_method_id,
col_id2id=snps_name2snps_id,
max_call_info_mismatch_rate=self.max_call_info_mismatch_rate,
snps_table="stock_250k.snps",
)
# snps_table is set to the stock_250k snps_table
twoSNPData = TwoSNPData(
SNPData1=snpData1,
SNPData2=snpData2,
curs=curs,
QC_method_id=self.QC_method_id,
user=self.user,
row_matching_by_which_value=0,
debug=self.debug,
)
if self.run_type == 1:
row_id2NA_mismatch_rate = twoSNPData.cmp_row_wise()
elif self.run_type == 2:
twoSNPData.save_col_wise(session, readme)
row_id2NA_mismatch_rate = {}
else:
sys.stderr.write("run_type=%s is not supported.\n" % self.run_type)
sys.exit(5)
passingdata = PassingData()
passingdata.row_id2NA_mismatch_rate = row_id2NA_mismatch_rate
passingdata.row_id12row_id2 = twoSNPData.row_id12row_id2
return passingdata
def plone_run(self, min_call_info_mismatch_rate=0.1):
"""
2009-6-9
pass self.max_mismatch_rate, self.min_no_of_non_NA_pairs to TwoSNPData to filter entries stored in db.
2009-4-13
add min_call_info_mismatch_rate
2009-2-5
add "create_tables=False" to db.setup()
2008-07-02
fix a bug which causes the program to continue read data even while call_info_id2fname is empty and input_dir is null.
2008-07-01
adjust to the newest functions in QC_250k.py
2008-04-25
return None if QC_method_id==0
2008-04-20
for plone to call it just to get row_id2NA_mismatch_rate
"""
import MySQLdb
conn = MySQLdb.connect(db=self.dbname, host=self.hostname, user = self.user, passwd = self.passwd)
curs = conn.cursor()
self.curs = curs
#database connection and etc
db = Stock_250kDB.Stock_250kDB(username=self.user,
password=self.passwd, hostname=self.hostname, database=self.dbname)
db.setup(create_tables=False)
session = db.session
session.begin()
#transaction = session.create_transaction()
# if cmp_data_filename not specified, try to find in the data_description column in table QC_method.
qm = QCMethod.query.get(self.QC_method_id)
if not self.cmp_data_filename and self.QC_method_id!=0:
if qm.data_description:
data_description_ls = qm.data_description.split('=')
if len(data_description_ls)>1:
self.cmp_data_filename = qm.data_description.split('=')[1].strip()
#after db query, cmp_data_filename is still nothing, exit program.
if not self.cmp_data_filename and self.QC_method_id!=0:
sys.stderr.write("cmp_data_filename is still nothing even after db query. please specify it on the commandline.\n")
sys.exit(3)
#from variation.src.FilterStrainSNPMatrix import FilterStrainSNPMatrix
header, strain_acc_list, category_list, data_matrix = read_data(self.cmp_data_filename)
strain_acc_list = map(int, strain_acc_list) #it's ecotypeid, cast it to integer to be compatible to the later ecotype_id_ls from db
snpData2 = SNPData(header=header, strain_acc_list=strain_acc_list, \
data_matrix=data_matrix, snps_table=self.QC_method_id2snps_table.get(self.QC_method_id), ignore_het=qm.ignore_het)
#category_list is not used.
if self.input_dir:
#04/22/08 Watch: call_info_id2fname here is fake, it's actually keyed by (array_id, ecotypeid)
#no submission to db
call_info_id2fname = self.get_array_id2fname(curs, self.input_dir)
else:
#call_info_id2fname = self.get_call_info_id2fname(curs, self.call_info_table, self.call_QC_table, self.QC_method_id)
call_data = self.get_call_info_id2fname(db, self.QC_method_id, self.call_method_id, \
filter_calls_QCed=0, max_call_info_mismatch_rate=1, min_call_info_mismatch_rate=min_call_info_mismatch_rate,\
debug=self.debug)
call_info_id2fname = call_data.call_info_id2fname
call_info_ls_to_return = call_data.call_info_ls_to_return
#2008-07-01 pick the call_info_ids to be handled
new_call_info_id2fname = {}
for call_info_id_wanted in self.call_info_id_ls:
if call_info_id_wanted in call_info_id2fname:
new_call_info_id2fname[call_info_id_wanted] = call_info_id2fname[call_info_id_wanted]
elif self.report:
sys.stderr.write("%s not in call_info_id2fname.\n"%(call_info_id_wanted))
call_info_id2fname = new_call_info_id2fname
if call_info_id2fname:
pdata = self.read_call_matrix(call_info_id2fname, self.min_probability)
header = pdata.header
call_info_id_ls = pdata.call_info_id_ls
array_id_ls = pdata.array_id_ls
ecotype_id_ls = pdata.ecotype_id_ls
data_matrix = pdata.data_matrix
elif self.input_dir: #2008-07-02
#input file is SNP by strain format. double header (1st two lines)
header, snps_name_ls, category_list, data_matrix = FilterStrainSNPMatrix.read_data(self.input_dir, double_header=1)
ecotype_id_ls = header[0][2:]
call_info_id_ls = header[1][2:]
data_matrix = numpy.array(data_matrix)
data_matrix = data_matrix.transpose()
header = ['', ''] + snps_name_ls #fake a header for SNPData
else: #2008-07-02
sys.stderr.write("No good arrays.\n")
return None
snps_name2snps_id = None
#swap the ecotype_id_ls and call_info_id_ls when passing them to SNPData. now strain_acc_list=ecotype_id_ls
snpData1 = SNPData(header=header, strain_acc_list=ecotype_id_ls, category_list= call_info_id_ls, data_matrix=data_matrix, \
min_probability=self.min_probability, call_method_id=self.call_method_id, col_id2id=snps_name2snps_id,\
max_call_info_mismatch_rate=self.max_call_info_mismatch_rate, snps_table='stock_250k.snps') #snps_table is set to the stock_250k snps_table
twoSNPData = TwoSNPData(SNPData1=snpData1, SNPData2=snpData2, curs=curs, \
QC_method_id=self.QC_method_id, user=self.user, row_matching_by_which_value=0, debug=self.debug,\
max_mismatch_rate=self.max_mismatch_rate, min_no_of_non_NA_pairs=self.min_no_of_non_NA_pairs)
#2009-6-9 cross-matching results whose mismatch_rates are below max_mismatch_rate would be put into db.
row_id2NA_mismatch_rate = None
#2008-05-01 create a cross match table temporarily
twoSNPData.qc_cross_match_table = 'qc_cross_match'
twoSNPData.new_QC_cross_match_table = self.new_QC_cross_match_table
twoSNPData.cal_row_id2pairwise_dist() #database submission is done along.
return row_id2NA_mismatch_rate
def prepareTwoSNPData(self,
db,
max_mismatch_rate=0.25,
min_no_of_non_NA_pairs=40,
report=0):
"""
2009-9-23
add arguments max_mismatch_rate & min_no_of_non_NA_pairs, and pass them to twoSNPData.
However it's useless to control what should be inserted into db because TwoSNPData.qc_cross_match_table is
not defined and even if it's defined, the table it'll create doesn't concord to the one in 149SNP db.
2008-09-10
if self.input_fname is given, get 149SNP data from it , instead of database
2008-8-28
split out of run() so that MpiQC149CrossMatch could call this easily
"""
import MySQLdb
conn = MySQLdb.connect(db=self.dbname,
host=self.hostname,
user=self.db_user,
passwd=self.db_passwd)
curs = conn.cursor()
if self.input_fname:
header, strain_acc_list, category_list, data_matrix = read_data(
self.input_fname)
else:
from dbSNP2data import dbSNP2data
snp_id2index, snp_id_list, snp_id2info = dbSNP2data.get_snp_id2index_m(
curs, StockDB.Calls.table.name, StockDB.SNPs.table.name)
strain_info_data = self.get_strain_id_info(
self.QC_method_id, ignore_strains_with_qc=False)
data_matrix = self.get_data_matrix(
db, strain_info_data.strain_id2index, snp_id2index,
StockDB.Calls.table.name)
strain_acc_list = [
strain_info_data.strain_id2acc[strain_id]
for strain_id in strain_info_data.strain_id_list
] #tg_ecotypeid
category_list = [
strain_info_data.strain_id2category[strain_id]
for strain_id in strain_info_data.strain_id_list
] #strainid
header = ['ecotypeid', 'strainid']
for snp_id in snp_id_list:
snp_name, chromosome, position = snp_id2info[snp_id]
header.append(snp_name)
snpData1 = SNPData(header=header, strain_acc_list=strain_acc_list, category_list=category_list, data_matrix=data_matrix, \
snps_table='stock.snps') #snps_table is set to the stock_250k snps_table
if self.QC_method_id == 4:
snpData2 = snpData1
else:
self.cmp_data_filename = self.findOutCmpDataFilename(
self.cmp_data_filename, self.QC_method_id, StockDB.QCMethod)
header, strain_acc_list, category_list, data_matrix = read_data(
self.cmp_data_filename)
strain_acc_list = map(
int, strain_acc_list
) #it's ecotypeid, cast it to integer to be compatible to the later ecotype_id_ls from db
snpData2 = SNPData(header=header, strain_acc_list=strain_acc_list, \
data_matrix=data_matrix) #category_list is not used to facilitate row-id matching
twoSNPData = TwoSNPData(SNPData1=snpData1, SNPData2=snpData2, \
QC_method_id=self.QC_method_id, user=self.db_user, row_matching_by_which_value=0, debug=self.debug,\
max_mismatch_rate=max_mismatch_rate, min_no_of_non_NA_pairs=min_no_of_non_NA_pairs, report=report)
return twoSNPData
def doFilter(self, snpData, snpData_qc_strain, snpData_qc_snp, min_call_probability, max_call_mismatch_rate, max_call_NA_rate,\
max_snp_mismatch_rate, max_snp_NA_rate, npute_window_size , output_dir=None):
"""
2009-10-11
replace imputeData() with NPUTE.samplingImpute(..., no_of_accessions_per_sampling=300, coverage=3) to avoid memory blowup.
2008-12-22
replace '=' and ',' with '_' in the output filename
2008-05-19
matrix_ls has to be of length >0 before concatenation
2008-05-19
use SNPData structure
2008-05-18
add onlyCommon=True to FilterAccessions.filterByError()
2008-05-17
add argument output_dir. if it's available, output data matrix before and after imputation
2008-05-12
add
qcdata.no_of_accessions_filtered_by_mismatch
qcdata.no_of_accessions_filtered_by_na
qcdata.no_of_snps_filtered_by_mismatch
qcdata.no_of_snps_filtered_by_na
qcdata.no_of_monomorphic_snps_removed
2008-05-11
split up from computing_node_handler
"""
qcdata = PassingData()
twoSNPData = TwoSNPData(SNPData1=snpData, SNPData2=snpData_qc_strain, \
row_matching_by_which_value=0, debug=self.debug)
row_id2NA_mismatch_rate = twoSNPData.cmp_row_wise()
del twoSNPData
newSnpData = SNPData.removeRowsByMismatchRate(snpData,
row_id2NA_mismatch_rate,
max_call_mismatch_rate)
qcdata.no_of_accessions_filtered_by_mismatch = newSnpData.no_of_rows_removed
newSnpData = SNPData.removeRowsByNARate(newSnpData, max_call_NA_rate)
qcdata.no_of_accessions_filtered_by_na = newSnpData.no_of_rows_removed
twoSNPData = TwoSNPData(SNPData1=newSnpData, SNPData2=snpData_qc_snp, \
row_matching_by_which_value=0, debug=self.debug)
col_id2NA_mismatch_rate = twoSNPData.cmp_col_wise()
del twoSNPData
newSnpData = SNPData.removeColsByMismatchRate(newSnpData,
col_id2NA_mismatch_rate,
max_snp_mismatch_rate)
qcdata.no_of_snps_filtered_by_mismatch = newSnpData.no_of_cols_filtered_by_mismatch
newSnpData = SNPData.removeColsByNARate(newSnpData, max_snp_NA_rate)
qcdata.no_of_snps_filtered_by_na = newSnpData.no_of_cols_filtered_by_na
twoSNPData = TwoSNPData(SNPData1=newSnpData, SNPData2=snpData_qc_snp, \
row_matching_by_which_value=0, debug=self.debug)
newSnpData = twoSNPData.mergeTwoSNPData(priority=2)
del twoSNPData
#MergeSnpsData.merge(snpsd_250k_tmp, snpsd_ls_qc_snp, unionType=0, priority=2)
newSnpData = SNPData.removeMonomorphicCols(newSnpData)
qcdata.no_of_monomorphic_snps_removed = newSnpData.no_of_monomorphic_cols
#FilterSnps.filterMonomorphic(snpsd_250k_tmp)
if output_dir:
#output data here
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
output_fname_prefix_ls = ['min_oligo_call_probability_%s'%min_call_probability,\
'max_array_mismatch_rate_%s'%max_call_mismatch_rate,\
'max_array_NA_rate_%s'%max_call_NA_rate,\
'max_snp_mismatch_rate_%s'%max_snp_mismatch_rate,\
'max_snp_NA_rate_%s'%max_snp_NA_rate,\
'npute_window_size_%s'%npute_window_size]
output_fname = os.path.join(
output_dir,
'_'.join(output_fname_prefix_ls + ['before_imputation.tsv']))
newSnpData.tofile(output_fname)
#chromosomes = [snpsd_250k_tmp[i].chromosome for i in range(len(snpsd_250k_tmp))]
#snpsdata.writeRawSnpsDatasToFile(output_fname, snpsd_250k_tmp, chromosomes=chromosomes, deliminator=',', withArrayIds = True)
"""
qcdata.no_of_snps_filtered_by_mismatch = 0
qcdata.no_of_snps_filtered_by_na = 0
qcdata.no_of_monomorphic_snps_removed = 0
for snpsd in snpsd_250k_tmp:
qcdata.no_of_snps_filtered_by_mismatch += snpsd.no_of_snps_filtered_by_mismatch
qcdata.no_of_snps_filtered_by_na += snpsd.no_of_snps_filtered_by_na
qcdata.no_of_monomorphic_snps_removed += snpsd.no_of_monomorphic_snps_removed
"""
#snpData0 = RawSnpsData_ls2SNPData(snpsd_250k_tmp)
twoSNPData0 = TwoSNPData(SNPData1=newSnpData, SNPData2=snpData_qc_strain, \
row_matching_by_which_value=0)
row_id2NA_mismatch_rate0 = twoSNPData0.cmp_row_wise()
col_id2NA_mismatch_rate0 = twoSNPData0.cmp_col_wise()
del twoSNPData0
result = []
#for npute_window_size in npute_window_size_ls:
#snpsd_250k_tmp_1 = copy.deepcopy(snpsd_250k_tmp) #deepcopy, otherwise snpsd_250k_tmp_1[i].snps = [] would clear snpsd_250k_tmp up as well
if len(newSnpData.row_id_ls) > 5:
snps_name_ls = newSnpData.col_id_ls
## 2009-10-8 use NPUTE.samplingImpute()
imputed_matrix, new_snps_name_ls = NPUTE.samplingImpute(snps_name_ls, newSnpData.data_matrix, \
input_file_format=1, input_NA_char=0, lower_case_for_imputation=False,\
npute_window_size=int(npute_window_size), \
no_of_accessions_per_sampling=300, coverage=3)
snpData_imputed = SNPData(row_id_ls=newSnpData.row_id_ls,
col_id_ls=new_snps_name_ls,
data_matrix=imputed_matrix)
"""
## 2009-10-8 use NPUTE.samplingImpute() instead. comment out below
chr2no_of_snps = NPUTE.get_chr2no_of_snps(snps_name_ls)
chr_ls = chr2no_of_snps.keys()
chr_ls.sort()
snpData_imputed = SNPData(row_id_ls = newSnpData.row_id_ls, col_id_ls=[])
matrix_ls = []
for chromosome in chr_ls:
if chr2no_of_snps[chromosome]>5: #enough for imputation
npute_data_struc = NPUTESNPData(snps_name_ls=snps_name_ls, data_matrix=newSnpData.data_matrix, chromosome=chromosome, \
input_file_format=1, input_NA_char=0)
imputeData(npute_data_struc, int(npute_window_size))
matrix_ls.append(npute_data_struc.snps)
snpData_imputed.col_id_ls += npute_data_struc.chosen_snps_name_ls
if len(matrix_ls)>0:
snpData_imputed.data_matrix = numpy.transpose(numpy.concatenate(matrix_ls))
"""
if output_dir: #2008-05-16 write the data out if output_fname is available
#chromosomes = [snpsd_250k_tmp[i].chromosome for i in range(len(snpsd_250k_tmp))] #already produced in the previous before_imputation output
output_fname = os.path.join(
output_dir, '_'.join(output_fname_prefix_ls +
['after_imputation.tsv']))
#snpsdata.writeRawSnpsDatasToFile(output_fname, snpsd_250k_tmp, chromosomes=chromosomes, deliminator=',', withArrayIds = True)
snpData_imputed.tofile(output_fname)
twoSNPData1 = TwoSNPData(SNPData1=snpData_imputed, SNPData2=snpData_qc_strain, \
row_matching_by_which_value=0)
qcdata.row_id2NA_mismatch_rate1 = twoSNPData1.cmp_row_wise()
qcdata.col_id2NA_mismatch_rate1 = twoSNPData1.cmp_col_wise()
del twoSNPData1, snpData_imputed
else:
snpData_imputed = None
#qcdata.row_id2NA_mismatch_rate1 = {}
#qcdata.col_id2NA_mismatch_rate1 = {}
del newSnpData
"""
for i in range(len(snpsd_250k_tmp)):
#snpsd_250k_tmp_1[i].snps = [] #clear it up
if len(snpsd_250k_tmp[i].accessions)>5 and len(snpsd_250k_tmp[i].positions)>5: #not enough for imputation
npute_data_struc = NPUTESNPData(inFile=snpsd_250k_tmp[i], input_NA_char='NA', input_file_format=4, lower_case_for_imputation=0)
imputeData(npute_data_struc, int(npute_window_size))
snpsd_250k_tmp[i].snps = npute_data_struc.snps
del npute_data_struc
"""
qcdata.row_id2NA_mismatch_rate0 = row_id2NA_mismatch_rate0
qcdata.col_id2NA_mismatch_rate0 = col_id2NA_mismatch_rate0
qcdata.min_call_probability = min_call_probability
qcdata.max_call_mismatch_rate = max_call_mismatch_rate
qcdata.max_call_NA_rate = max_call_NA_rate
qcdata.max_snp_mismatch_rate = max_snp_mismatch_rate
qcdata.max_snp_NA_rate = max_snp_NA_rate
qcdata.npute_window_size = npute_window_size
result.append(qcdata)
return result
def run(self):
if self.debug:
import pdb
pdb.set_trace()
db = StockDB.StockDB(drivername=self.drivername,
username=self.db_user,
password=self.db_passwd,
hostname=self.hostname,
database=self.dbname)
db.setup(create_tables=False)
session = db.session
session.begin()
self.cmp_data_filename = self.findOutCmpDataFilename(
self.cmp_data_filename, self.QC_method_id, StockDB.QCMethod)
header, strain_acc_list, category_list, data_matrix = read_data(
self.cmp_data_filename)
strain_acc_list = map(
int, strain_acc_list
) #it's ecotypeid, cast it to integer to be compatible to the later ecotype_id_ls from db
snpData2 = SNPData(header=header, strain_acc_list=strain_acc_list, \
data_matrix=data_matrix) #category_list is not used.
readme = formReadmeObj(sys.argv, self.ad, StockDB.README)
session.save(readme)
import MySQLdb
conn = MySQLdb.connect(db=self.dbname,
host=self.hostname,
user=self.db_user,
passwd=self.db_passwd)
curs = conn.cursor()
from dbSNP2data import dbSNP2data
snp_id2index, snp_id_list, snp_id2info = dbSNP2data.get_snp_id2index_m(
curs, StockDB.Calls.table.name, StockDB.SNPs.table.name)
strain_info_data = self.get_strain_id_info(self.QC_method_id)
data_matrix = self.get_data_matrix(db,
strain_info_data.strain_id2index,
snp_id2index,
StockDB.Calls.table.name)
strain_acc_list = [
strain_info_data.strain_id2acc[strain_id]
for strain_id in strain_info_data.strain_id_list
]
category_list = [
strain_info_data.strain_id2category[strain_id]
for strain_id in strain_info_data.strain_id_list
]
header = ['ecotypeid', 'strainid']
for snp_id in snp_id_list:
snp_name, chromosome, position = snp_id2info[snp_id]
header.append(snp_name)
snpData1 = SNPData(header=header, strain_acc_list=strain_acc_list, category_list=category_list, data_matrix=data_matrix, \
snps_table='stock.snps') #snps_table is set to the stock_250k snps_table
twoSNPData = TwoSNPData(SNPData1=snpData1, SNPData2=snpData2, curs=curs, \
QC_method_id=self.QC_method_id, user=self.db_user, row_matching_by_which_value=0, debug=self.debug)
if self.run_type == 1:
row_id2NA_mismatch_rate = twoSNPData.cmp_row_wise()
elif self.run_type == 2:
#twoSNPData.save_col_wise(session, readme) #2008-08-18 need to implement a new one for 149SNP
row_id2NA_mismatch_rate = {}
else:
sys.stderr.write("run_type=%s is not supported.\n" % self.run_type)
sys.exit(5)
if self.output_fname and self.run_type == 1 and row_id2NA_mismatch_rate:
self.output_row_id2NA_mismatch_rate(row_id2NA_mismatch_rate,
self.output_fname)
if self.run_type == 1 and self.commit and not self.input_dir and row_id2NA_mismatch_rate:
#if self.input_dir is given, no db submission. call_info_id2fname here is fake, it's actually keyed by (array_id, ecotypeid)
#row_id2NA_mismatch_rate might be None if it's method 0.
self.submit_to_call_QC(session, row_id2NA_mismatch_rate, self.QC_method_id, self.db_user, \
twoSNPData.row_id12row_id2, readme)
if self.commit:
session.commit()
else:
session.rollback()
def plone_run(self, min_call_info_mismatch_rate=0.1):
"""
2009-6-9
pass self.max_mismatch_rate, self.min_no_of_non_NA_pairs to TwoSNPData to filter entries stored in db.
2009-4-13
add min_call_info_mismatch_rate
2009-2-5
add "create_tables=False" to db.setup()
2008-07-02
fix a bug which causes the program to continue read data even while call_info_id2fname is empty and input_dir is null.
2008-07-01
adjust to the newest functions in QC_250k.py
2008-04-25
return None if QC_method_id==0
2008-04-20
for plone to call it just to get row_id2NA_mismatch_rate
"""
import MySQLdb
conn = MySQLdb.connect(db=self.dbname,
host=self.hostname,
user=self.user,
passwd=self.passwd)
curs = conn.cursor()
self.curs = curs
#database connection and etc
db = Stock_250kDB.Stock_250kDB(username=self.user,
password=self.passwd,
hostname=self.hostname,
database=self.dbname)
db.setup(create_tables=False)
session = db.session
session.begin()
#transaction = session.create_transaction()
# if cmp_data_filename not specified, try to find in the data_description column in table QC_method.
qm = QCMethod.query.get(self.QC_method_id)
if not self.cmp_data_filename and self.QC_method_id != 0:
if qm.data_description:
data_description_ls = qm.data_description.split('=')
if len(data_description_ls) > 1:
self.cmp_data_filename = qm.data_description.split(
'=')[1].strip()
#after db query, cmp_data_filename is still nothing, exit program.
if not self.cmp_data_filename and self.QC_method_id != 0:
sys.stderr.write(
"cmp_data_filename is still nothing even after db query. please specify it on the commandline.\n"
)
sys.exit(3)
#from variation.src.FilterStrainSNPMatrix import FilterStrainSNPMatrix
header, strain_acc_list, category_list, data_matrix = read_data(
self.cmp_data_filename)
strain_acc_list = map(
int, strain_acc_list
) #it's ecotypeid, cast it to integer to be compatible to the later ecotype_id_ls from db
snpData2 = SNPData(header=header, strain_acc_list=strain_acc_list, \
data_matrix=data_matrix, snps_table=self.QC_method_id2snps_table.get(self.QC_method_id), ignore_het=qm.ignore_het)
#category_list is not used.
if self.input_dir:
#04/22/08 Watch: call_info_id2fname here is fake, it's actually keyed by (array_id, ecotypeid)
#no submission to db
call_info_id2fname = self.get_array_id2fname(curs, self.input_dir)
else:
#call_info_id2fname = self.get_call_info_id2fname(curs, self.call_info_table, self.call_QC_table, self.QC_method_id)
call_data = self.get_call_info_id2fname(db, self.QC_method_id, self.call_method_id, \
filter_calls_QCed=0, max_call_info_mismatch_rate=1, min_call_info_mismatch_rate=min_call_info_mismatch_rate,\
debug=self.debug)
call_info_id2fname = call_data.call_info_id2fname
call_info_ls_to_return = call_data.call_info_ls_to_return
#2008-07-01 pick the call_info_ids to be handled
new_call_info_id2fname = {}
for call_info_id_wanted in self.call_info_id_ls:
if call_info_id_wanted in call_info_id2fname:
new_call_info_id2fname[
call_info_id_wanted] = call_info_id2fname[
call_info_id_wanted]
elif self.report:
sys.stderr.write("%s not in call_info_id2fname.\n" %
(call_info_id_wanted))
call_info_id2fname = new_call_info_id2fname
if call_info_id2fname:
pdata = self.read_call_matrix(call_info_id2fname,
self.min_probability)
header = pdata.header
call_info_id_ls = pdata.call_info_id_ls
array_id_ls = pdata.array_id_ls
ecotype_id_ls = pdata.ecotype_id_ls
data_matrix = pdata.data_matrix
elif self.input_dir: #2008-07-02
#input file is SNP by strain format. double header (1st two lines)
header, snps_name_ls, category_list, data_matrix = FilterStrainSNPMatrix.read_data(
self.input_dir, double_header=1)
ecotype_id_ls = header[0][2:]
call_info_id_ls = header[1][2:]
data_matrix = numpy.array(data_matrix)
data_matrix = data_matrix.transpose()
header = ['', ''] + snps_name_ls #fake a header for SNPData
else: #2008-07-02
sys.stderr.write("No good arrays.\n")
return None
snps_name2snps_id = None
#swap the ecotype_id_ls and call_info_id_ls when passing them to SNPData. now strain_acc_list=ecotype_id_ls
snpData1 = SNPData(header=header, strain_acc_list=ecotype_id_ls, category_list= call_info_id_ls, data_matrix=data_matrix, \
min_probability=self.min_probability, call_method_id=self.call_method_id, col_id2id=snps_name2snps_id,\
max_call_info_mismatch_rate=self.max_call_info_mismatch_rate, snps_table='stock_250k.snps') #snps_table is set to the stock_250k snps_table
twoSNPData = TwoSNPData(SNPData1=snpData1, SNPData2=snpData2, curs=curs, \
QC_method_id=self.QC_method_id, user=self.user, row_matching_by_which_value=0, debug=self.debug,\
max_mismatch_rate=self.max_mismatch_rate, min_no_of_non_NA_pairs=self.min_no_of_non_NA_pairs)
#2009-6-9 cross-matching results whose mismatch_rates are below max_mismatch_rate would be put into db.
row_id2NA_mismatch_rate = None
#2008-05-01 create a cross match table temporarily
twoSNPData.qc_cross_match_table = 'qc_cross_match'
twoSNPData.new_QC_cross_match_table = self.new_QC_cross_match_table
twoSNPData.cal_row_id2pairwise_dist(
) #database submission is done along.
return row_id2NA_mismatch_rate
def doFilter(
self,
snpData,
snpData_qc_strain,
snpData_qc_snp,
min_call_probability,
max_call_mismatch_rate,
max_call_NA_rate,
max_snp_mismatch_rate,
max_snp_NA_rate,
npute_window_size,
output_dir=None,
):
"""
2009-10-11
replace imputeData() with NPUTE.samplingImpute(..., no_of_accessions_per_sampling=300, coverage=3) to avoid memory blowup.
2008-12-22
replace '=' and ',' with '_' in the output filename
2008-05-19
matrix_ls has to be of length >0 before concatenation
2008-05-19
use SNPData structure
2008-05-18
add onlyCommon=True to FilterAccessions.filterByError()
2008-05-17
add argument output_dir. if it's available, output data matrix before and after imputation
2008-05-12
add
qcdata.no_of_accessions_filtered_by_mismatch
qcdata.no_of_accessions_filtered_by_na
qcdata.no_of_snps_filtered_by_mismatch
qcdata.no_of_snps_filtered_by_na
qcdata.no_of_monomorphic_snps_removed
2008-05-11
split up from computing_node_handler
"""
qcdata = PassingData()
twoSNPData = TwoSNPData(
SNPData1=snpData, SNPData2=snpData_qc_strain, row_matching_by_which_value=0, debug=self.debug
)
row_id2NA_mismatch_rate = twoSNPData.cmp_row_wise()
del twoSNPData
newSnpData = SNPData.removeRowsByMismatchRate(snpData, row_id2NA_mismatch_rate, max_call_mismatch_rate)
qcdata.no_of_accessions_filtered_by_mismatch = newSnpData.no_of_rows_filtered_by_mismatch
newSnpData = SNPData.removeRowsByNARate(newSnpData, max_call_NA_rate)
qcdata.no_of_accessions_filtered_by_na = newSnpData.no_of_rows_filtered_by_na
twoSNPData = TwoSNPData(
SNPData1=newSnpData, SNPData2=snpData_qc_snp, row_matching_by_which_value=0, debug=self.debug
)
col_id2NA_mismatch_rate = twoSNPData.cmp_col_wise()
del twoSNPData
newSnpData = SNPData.removeColsByMismatchRate(newSnpData, col_id2NA_mismatch_rate, max_snp_mismatch_rate)
qcdata.no_of_snps_filtered_by_mismatch = newSnpData.no_of_cols_filtered_by_mismatch
newSnpData = SNPData.removeColsByNARate(newSnpData, max_snp_NA_rate)
qcdata.no_of_snps_filtered_by_na = newSnpData.no_of_cols_filtered_by_na
twoSNPData = TwoSNPData(
SNPData1=newSnpData, SNPData2=snpData_qc_snp, row_matching_by_which_value=0, debug=self.debug
)
newSnpData = twoSNPData.mergeTwoSNPData(priority=2)
del twoSNPData
# MergeSnpsData.merge(snpsd_250k_tmp, snpsd_ls_qc_snp, unionType=0, priority=2)
newSnpData = SNPData.removeMonomorphicCols(newSnpData)
qcdata.no_of_monomorphic_snps_removed = newSnpData.no_of_monomorphic_cols
# FilterSnps.filterMonomorphic(snpsd_250k_tmp)
if output_dir:
# output data here
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
output_fname_prefix_ls = [
"min_oligo_call_probability_%s" % min_call_probability,
"max_array_mismatch_rate_%s" % max_call_mismatch_rate,
"max_array_NA_rate_%s" % max_call_NA_rate,
"max_snp_mismatch_rate_%s" % max_snp_mismatch_rate,
"max_snp_NA_rate_%s" % max_snp_NA_rate,
"npute_window_size_%s" % npute_window_size,
]
output_fname = os.path.join(output_dir, "_".join(output_fname_prefix_ls + ["before_imputation.tsv"]))
newSnpData.tofile(output_fname)
# chromosomes = [snpsd_250k_tmp[i].chromosome for i in range(len(snpsd_250k_tmp))]
# snpsdata.writeRawSnpsDatasToFile(output_fname, snpsd_250k_tmp, chromosomes=chromosomes, deliminator=',', withArrayIds = True)
"""
qcdata.no_of_snps_filtered_by_mismatch = 0
qcdata.no_of_snps_filtered_by_na = 0
qcdata.no_of_monomorphic_snps_removed = 0
for snpsd in snpsd_250k_tmp:
qcdata.no_of_snps_filtered_by_mismatch += snpsd.no_of_snps_filtered_by_mismatch
qcdata.no_of_snps_filtered_by_na += snpsd.no_of_snps_filtered_by_na
qcdata.no_of_monomorphic_snps_removed += snpsd.no_of_monomorphic_snps_removed
"""
# snpData0 = RawSnpsData_ls2SNPData(snpsd_250k_tmp)
twoSNPData0 = TwoSNPData(SNPData1=newSnpData, SNPData2=snpData_qc_strain, row_matching_by_which_value=0)
row_id2NA_mismatch_rate0 = twoSNPData0.cmp_row_wise()
col_id2NA_mismatch_rate0 = twoSNPData0.cmp_col_wise()
del twoSNPData0
result = []
# for npute_window_size in npute_window_size_ls:
# snpsd_250k_tmp_1 = copy.deepcopy(snpsd_250k_tmp) #deepcopy, otherwise snpsd_250k_tmp_1[i].snps = [] would clear snpsd_250k_tmp up as well
if len(newSnpData.row_id_ls) > 5:
snps_name_ls = newSnpData.col_id_ls
## 2009-10-8 use NPUTE.samplingImpute()
imputed_matrix, new_snps_name_ls = NPUTE.samplingImpute(
snps_name_ls,
newSnpData.data_matrix,
input_file_format=1,
input_NA_char=0,
lower_case_for_imputation=False,
npute_window_size=int(npute_window_size),
no_of_accessions_per_sampling=300,
coverage=3,
)
snpData_imputed = SNPData(
row_id_ls=newSnpData.row_id_ls, col_id_ls=new_snps_name_ls, data_matrix=imputed_matrix
)
"""
## 2009-10-8 use NPUTE.samplingImpute() instead. comment out below
chr2no_of_snps = NPUTE.get_chr2no_of_snps(snps_name_ls)
chr_ls = chr2no_of_snps.keys()
chr_ls.sort()
snpData_imputed = SNPData(row_id_ls = newSnpData.row_id_ls, col_id_ls=[])
matrix_ls = []
for chromosome in chr_ls:
if chr2no_of_snps[chromosome]>5: #enough for imputation
npute_data_struc = NPUTESNPData(snps_name_ls=snps_name_ls, data_matrix=newSnpData.data_matrix, chromosome=chromosome, \
input_file_format=1, input_NA_char=0)
imputeData(npute_data_struc, int(npute_window_size))
matrix_ls.append(npute_data_struc.snps)
snpData_imputed.col_id_ls += npute_data_struc.chosen_snps_name_ls
if len(matrix_ls)>0:
snpData_imputed.data_matrix = num.transpose(num.concatenate(matrix_ls))
"""
if output_dir: # 2008-05-16 write the data out if output_fname is available
# chromosomes = [snpsd_250k_tmp[i].chromosome for i in range(len(snpsd_250k_tmp))] #already produced in the previous before_imputation output
output_fname = os.path.join(output_dir, "_".join(output_fname_prefix_ls + ["after_imputation.tsv"]))
# snpsdata.writeRawSnpsDatasToFile(output_fname, snpsd_250k_tmp, chromosomes=chromosomes, deliminator=',', withArrayIds = True)
snpData_imputed.tofile(output_fname)
twoSNPData1 = TwoSNPData(
SNPData1=snpData_imputed, SNPData2=snpData_qc_strain, row_matching_by_which_value=0
)
qcdata.row_id2NA_mismatch_rate1 = twoSNPData1.cmp_row_wise()
qcdata.col_id2NA_mismatch_rate1 = twoSNPData1.cmp_col_wise()
del twoSNPData1, snpData_imputed
else:
snpData_imputed = None
# qcdata.row_id2NA_mismatch_rate1 = {}
# qcdata.col_id2NA_mismatch_rate1 = {}
del newSnpData
"""
for i in range(len(snpsd_250k_tmp)):
#snpsd_250k_tmp_1[i].snps = [] #clear it up
if len(snpsd_250k_tmp[i].accessions)>5 and len(snpsd_250k_tmp[i].positions)>5: #not enough for imputation
npute_data_struc = NPUTESNPData(inFile=snpsd_250k_tmp[i], input_NA_char='NA', input_file_format=4, lower_case_for_imputation=0)
imputeData(npute_data_struc, int(npute_window_size))
snpsd_250k_tmp[i].snps = npute_data_struc.snps
del npute_data_struc
"""
qcdata.row_id2NA_mismatch_rate0 = row_id2NA_mismatch_rate0
qcdata.col_id2NA_mismatch_rate0 = col_id2NA_mismatch_rate0
qcdata.min_call_probability = min_call_probability
qcdata.max_call_mismatch_rate = max_call_mismatch_rate
qcdata.max_call_NA_rate = max_call_NA_rate
qcdata.max_snp_mismatch_rate = max_snp_mismatch_rate
qcdata.max_snp_NA_rate = max_snp_NA_rate
qcdata.npute_window_size = npute_window_size
result.append(qcdata)
return result
