def main(args):
chr_number = 1
# Use dictionary keys instead of index keys for args
args = process_args(args)
job = str(args['job']) # must be a number
print('JOB {}'.format(job))
prof_option = args['profile']
sim_option = args['sim option']
path = args['path']
[sim_data_dir, germline_out_dir, sim_results_dir] = create_sim_directories(path)
processedData = process_input_files(args['param file'], args['model file'], args)
using_pseudo_array = True
if not processedData.get('discovery') and not processedData.get('sample') and not processedData.get('daf'):
using_pseudo_array = False
debugPrint(3, "Finished processing input\nprocessedData: ", processedData)
### Create a list of Sequence class instances. These will contain the bulk of all sequence-based data
sequences = create_sequences(processedData)
names = [seq.name for seq in sequences]
n_d = sum([1 for seq in sequences if seq.type == 'discovery'])
debugPrint(1,'name\ttotal\tpanel\tgenotyped')
for seq in sequences:
debugPrint(1,'{}\t{}\t{}\t{}'.format(seq.name, seq.tot, seq.panel, seq.genotyped))
total = sum([seq.tot for seq in sequences])
debugPrint(1, 'total samples: {}'.format(sum([seq.genotyped for seq in sequences if seq.type=='discovery'] + [seq.tot for seq in sequences if seq.type=='sample'])))
### Define simulation size
length = processedData['length']
debugPrint(1, 'Perform simulation and get sequences')
pedmap = args['pedmap']
germline = args['germline']
##########################################################################
################## Perform simulation and get sequences ##################
##########################################################################
### Flag to check if the simulation works
SNPs_exceed_available_sites = True
while SNPs_exceed_available_sites:
# add genetic map to macs_args list
macs_args = []
macs_args = processedData['macs_args']
if sim_option == 'macs':
### Run macs and make bitarray
profile(prof_option, path, job, "start_run_macs")
[sequences,position] = run_macs(macs_args, sequences)
profile(prof_option, path, job, "end_run_macs")
nbss = len(sequences[0].bits) / (sequences[0].tot)
if using_pseudo_array:
## get position of the simulated sites and scale it to the "real" position in the SNP chip
sim_positions = get_sim_positions(position, nbss, length)
elif sim_option == 'macs_file':
### Using a static sim output rather than generating from seed
seq_alleles = AllelesMacsFile('tests/test_data/sites1000000.txt')
set_seq_bits(sequences, seq_alleles)
nbss = len(sequences[0].bits) / (sequences[0].tot)
if using_pseudo_array:
## get position of the simulated sites and scale it to the "real" position in the SNP chip
sim_positions = get_sim_positions_old(seq_alleles, nbss, length)
profile(prof_option, path, job, "start_set_discovery_bits")
set_discovery_bits(sequences)
profile(prof_option, path, job, "end_set_discovery_bits")
debugPrint(1, 'Number of sites in simulation: {}'.format(nbss))
assert nbss > 10, "Number of sites is less than 10: {}".format(nbss)
##########################################################################
### Create pseudo array according to ascertainment scheme and template ###
##########################################################################
if using_pseudo_array:
SNPs = get_SNP_sites(args['SNP file'])
debugPrint(1, 'Number of SNPs in Array: {}'.format(len(SNPs)))
profile(prof_option, path, job, "start_set_panel_bits")
asc_panel_bits = set_panel_bits(nbss, sequences)
profile(prof_option, path, job, "end_set_panel_bits")
debugPrint(1,'Number of chromosomes in asc_panel: {}'.format(asc_panel_bits.length()/nbss))
### Get pseudo array sites
debugPrint(2,'Making pseudo array')
profile(prof_option, path, job, "start_pseudo_array_bits")
[pos_asc, nbss_asc, avail_site_indices, avail_sites] = pseudo_array_bits(asc_panel_bits, processedData['daf'], sim_positions, SNPs)
profile(prof_option, path, job, "end_pseudo_array_bits")
nb_avail_sites = len(avail_sites)
SNPs_exceed_available_sites = ( len(SNPs) >= nb_avail_sites )
else:
SNPs = []
SNPs_exceed_available_sites = False
if using_pseudo_array:
profile(prof_option, path, job, "start_set_asc_bits")
set_asc_bits(sequences, nbss_asc, pos_asc, avail_site_indices)
profile(prof_option, path, job, "end_set_asc_bits")
debugPrint(1, 'Calculating summary statistics')
##########################################################################
###################### Calculate summary statistics ######################
##########################################################################
res, head = [], []
### Calculate summary stats from genomes
if nbss > 0: # Simulations must contain at least one segregating site
profile(prof_option, path, job, "start_store_segregating_site_stats")
stat_tools.store_segregating_site_stats(sequences, res, head)
profile(prof_option, path, job, "end_store_segregating_site_stats")
profile(prof_option, path, job, "start_store_pairwise_FSTs")
stat_tools.store_pairwise_FSTs(sequences, n_d, res, head)
profile(prof_option, path, job, "end_store_pairwise_FSTs")
### Calculate summary stats from the ascertained SNPs
if using_pseudo_array:
if nbss_asc > 0:
profile(prof_option, path, job, "start_store_array_segregating_site_stats")
stat_tools.store_array_segregating_site_stats(sequences, res, head)
profile(prof_option, path, job, "end_store_array_segregating_site_stats")
profile(prof_option, path, job, "start_store_array_FSTs")
stat_tools.store_array_FSTs(sequences, res, head)
profile(prof_option, path, job, "end_store_array_FSTs")
debugPrint(2,'Making ped and map files')
ped_file_name = '{0}/macs_asc_{1}_chr{2}.ped'.format(sim_data_dir, job, str(chr_number))
map_file_name = '{0}/macs_asc_{1}_chr{2}.map'.format(sim_data_dir, job, str(chr_number))
out_file_name = '{0}/macs_asc_{1}_chr{2}'.format(germline_out_dir, job, str(chr_number))
if os.path.isfile(out_file_name + '.match'): # Maybe remove if statement
os.remove(ped_file_name)
os.remove(map_file_name)
if using_pseudo_array and pedmap or germline:
profile(prof_option, path, job, "start_make_ped_file")
make_ped_file(ped_file_name, sequences)
profile(prof_option, path, job, "end_make_ped_file")
profile(prof_option, path, job, "start_make_map_file")
make_map_file(map_file_name, pos_asc, chr_number, avail_sites)
profile(prof_option, path, job, "end_make_map_file")
### Use Germline to find IBD on pseduo array ped and map files
do_i_run_germline = int(args['germline'])
debugPrint(1,'run germline? {}'.format("True" if do_i_run_germline else "False"))
if (do_i_run_germline == True):
########################### <CHANGE THIS LATER> ###########################
### Germline seems to be outputting in the wrong unit - so I am putting the min at 3000000 so that it is 3Mb, but should be the default.
profile(prof_option, path, job, "start_run_germline")
# germline = run_germline(ped_file_name, map_file_name, out_file_name, min_m = 3000000)
profile(prof_option, path, job, "end_run_germline")
germline = run_germline(ped_file_name, map_file_name, out_file_name, min_m=300)
########################### </CHANGE THIS LATER> ##########################
### Get IBD stats from Germline output
if os.path.isfile(out_file_name + '.match'):
print('Reading Germline IBD output')
profile(prof_option, path, job, "start_process_germline_file")
[IBD_pairs, IBD_dict] = process_germline_file(out_file_name, names)
profile(prof_option, path, job, "end_process_germline_file")
print('Calculating summary stats')
stats = OrderedDict([('num', len), ('mean', np.mean), ('med', np.median), ('var', np.var)])
profile(prof_option, path, job, "start_store_IBD_stats")
stat_tools.store_IBD_stats(stats, IBD_pairs, IBD_dict, res, head)
stat_tools.store_IBD_stats(stats, IBD_pairs, IBD_dict, res, head, min_val=30)
profile(prof_option, path, job, "end_store_IBD_stats")
debugPrint(1,'finished calculating ss')
write_sim_results_file(sim_results_dir, job, processedData['param_dict'], res, head)
print('')
print('#########################')
print('### PROGRAM COMPLETED ###')
print('#########################')
print('')
profile(prof_option, path, job, "COMPLETE")
def main(args):
args = processArgs(args)
model_file = args['model file']
param_file = args['param file']
path = args['output']
[sim_data_dir, germline_out_dir,
sim_results_dir] = create_sim_directories(path)
processedData = processInputFiles(param_file, model_file, args)
debugPrint(3, "Finished processing input\nprocessedData: ", processedData)
using_pseudo_array = True
if not processedData.get('discovery') and not processedData.get(
'sample') and not processedData.get('daf'):
using_pseudo_array = False
### Create a list of Sequence class instances. These will contain the bulk of all sequence-based data
sequences = create_sequences(processedData, args)
names = [seq.name for seq in sequences]
n_d = sum([1 for seq in sequences if seq.type == 'discovery'])
debugPrint(1, 'name\ttotal\tpanel\tgenotyped')
for seq in sequences:
debugPrint(
1, '{}\t{}\t{}\t{}'.format(seq.name, seq.tot, seq.panel,
seq.genotyped))
total = sum([seq.tot for seq in sequences])
debugPrint(
1, 'total samples: {}'.format(
sum([
seq.genotyped for seq in sequences if seq.type == 'discovery'
] + [seq.tot for seq in sequences if seq.type == 'sample'])))
##########################################################################
####################### Read Data from tped files ########################
##########################################################################
genome_file = args['genome file']
job = os.path.basename(genome_file)
seq_alleles_genome = AllelesReal(str(genome_file) + '.tped')
set_real_genome_bits(sequences, seq_alleles_genome)
if using_pseudo_array == True:
array_file = args['array file']
job = str(job) + '_' + str(os.path.basename(array_file))
seq_alleles_array = AllelesReal(str(array_file) + '.tped')
set_real_array_bits(sequences, seq_alleles_array)
##########################################################################
###################### Calculate summary statistics ######################
##########################################################################
res, head = [], []
### Calculate summary stats from genomes
stat_tools.store_segregating_site_stats(sequences, res, head)
stat_tools.store_pairwise_FSTs(sequences, n_d, res, head)
### Calculate summary stats from the ascertained SNPs
if using_pseudo_array:
stat_tools.store_array_segregating_site_stats(sequences, res, head)
stat_tools.store_array_FSTs(sequences, res, head)
debugPrint(1, 'Make ped and map files')
ped_file_name = '{0}/{1}.ped'.format(sim_data_dir, job)
map_file_name = '{0}/{1}.map'.format(sim_data_dir, job)
out_file_name = '{0}/{1}'.format(germline_out_dir, job)
### Use Germline to find IBD on pseduo array ped and map files
do_i_run_germline = 1 #fix this later
debugPrint(1, 'run germline? ' + str(do_i_run_germline))
if (do_i_run_germline == 0):
########################### <CHANGE THIS LATER> ###########################
### Germline seems to be outputting in the wrong unit - so I am putting the min at 3000000 so that it is 3Mb, but should be the default.
# germline = run_germline(ped_file_name, map_file_name, out_file_name, min_m = 3000000)
germline = run_germline(ped_file_name,
map_file_name,
out_file_name,
min_m=300)
########################### </CHANGE THIS LATER> ##########################
### Get IBD stats from Germline output
if os.path.isfile(out_file_name + '.match'):
debugPrint(1, 'Reading Germline IBD output')
[IBD_pairs, IBD_dict] = process_germline_file(out_file_name, names)
debugPrint(1, 'Calculating summary stats')
stats = OrderedDict([('num', len), ('mean', np.mean),
('med', np.median), ('var', np.var)])
stat_tools.store_IBD_stats(stats, IBD_pairs, IBD_dict, res, head)
stat_tools.store_IBD_stats(stats,
IBD_pairs,
IBD_dict,
res,
head,
min_val=30)
# print 'finished calculating ss'
write_stats_file(sim_results_dir, job, res, head)
print('')
print('##########################')
print('### PROGRAM COMPLETED ###')
print('##########################')
print('')