def discover(args) :
paramsfile = args.params
sample_req = args.sample
hetsnp = args.hetsnp
tagsnp = args.tagsnp
vcf_file = args.vcf
if hetsnp == 'True' or hetsnp == 'TRUE':
hetsnp = True
else:
hetsnp = False
if tagsnp == 'True' or tagsnp == 'TRUE':
tagsnp = True
else:
tagsnp = False
datafile = args.rpkm_matrix
f_dir = os.path.dirname(datafile)
if f_dir != '':
f_dir = f_dir + '/'
if args.output:
outputfile = f_dir + str(args.output)
tagsnp_file = args.tagsnp_file
mode = args.mode
sample_flag = False #used to check whether sample_req exists
# Build a reference set
if mode == 'single' or mode == 'baseline' or mode == 'reference' or mode == 'ref':
print 'Building the reference dataset...'
dataloader = DataManager(datafile)
samples_np = dataloader.getAllSamples()
dataloader.closeFile()
print 'Baseline is Done.'
print 'Loading data file...',
dataloader = DataManager(datafile)
print 'Done!'
print 'Loading paramters...',
params = dataloader.getParams(paramsfile)
print 'Done!'
dataloader.skipHeadline()
sample = dataloader.getNextSample()
targets_list = dataloader.getTargetsList()
output_aux = file(outputfile+'.aux', 'w')
output_aux.write('SAMPLE_ID\tCNV_TYPE\tFULL_INTERVAL\tINDEX\tINTERVAL\tREAD_DEPTH\n')
output = file(outputfile,'w')
output.write('SAMPLE_ID\tCNV_TYPE\tINTERVAL\tCHROMOSOME\tSTART\tSTOP\tLENGTH\n')
if (hetsnp or tagsnp) and vcf_file == '':
print 'Error: please indicate a vcf file!'
system.exit(0)
if vcf_file != '':
vcf_reader = VCFReader(vcf_file)
else:
vcf_reader = False
if tagsnp:
print 'Loading tagSNP information ...',
cnp_dict = vcf_reader.loadTagSNP(tagsnp_file)
print 'Done!'
while sample :
if sample_req == '' or (sample_req != '' and sample['sample_id'] == sample_req):
sample_flag = True
print time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) ,sample_req,'......'
#Renjie added: To check whether the VCF contains sample_req.
vcf_checker = vcf.Reader(open(vcf_file,'r'))
if sample['sample_id'] in vcf_checker.samples:
sample_in_VCF = True
elif sample_req in vcf_checker.samples:
sample_in_VCF = True
else:
print 'No sample %s in VCF file.'%sample_req
sample_in_VCF = False
if hetsnp and sample_in_VCF :
print 'Parsing SNV information from VCF file for: ' + sample['sample_id']
snp_info = vcf_reader.getSNPInfo(sample['sample_id'], targets_list)
if tagsnp and sample_in_VCF:
print 'Analysing tagSNP information from tagSNP database for: ' + sample['sample_id'],
cnp_list = vcf_reader.findTagSNPForSample(sample['sample_pop'], sample['sample_id'], cnp_dict)
tagsnp_info_list = vcf_reader.findExonWithTagSNP(cnp_list, targets_list, overlap_threshold=0.5)
print len(tagsnp_info_list)
#estimate NB paramters from sample['observations']
sample_observations = []
remove_list = []
sample['observations'] = [ float(x) for x in sample['observations']]
#slicing: target_index is used to split observations sequence
target_index_begin = 0
target_index_end = 0
temp = 1
sample_observations_list = []
snp_info_list = []
for i, targets in enumerate(targets_list):
target_index_end = target_index_begin + len(targets)
if hetsnp and sample_in_VCF:
snp_info_list.append(snp_info[target_index_begin:target_index_end])
sample_observations_list.append(sample['observations'][target_index_begin:target_index_end])
target_index_begin = target_index_end
# Filtering:
if mode == 'svd' or mode == 'SVD' or mode == 'pooled' or mode == 'pooled-sample':
for i in range(len(sample_observations_list)):
sample_observations_list[i] = ndarray.tolist(stats.zscore(sample_observations_list[i]))
elif mode == 'baseline' or mode == 'reference' or mode == 'single' or mode == 'single-sample':
# filtering lists whose observation equals to 0
for i in range(len(targets_list)):
rem_index = []
for j in range(len(targets_list[i])):
value = sample_observations_list[i][j]
if np.isnan(float(value)):
rem_index.append(j)
#filter target_list, snp_list and observation_list
targets_list[i] = jf.filter_list_by_list(targets_list[i], rem_index)
sample_observations_list[i] = jf.filter_list_by_list(sample_observations_list[i], rem_index)
if hetsnp and sample_in_VCF:
snp_info_list[i] = jf.filter_list_by_list(snp_info_list[i], rem_index)
if tagsnp and sample_in_VCF:
tagsnp_info_list[i] = jf.filter_list_by_list(tagsnp_info_list[i], rem_index)
#Parameters estimation
observations_all_list = []
for i in range(len(sample_observations_list)):
observations_all_list.extend(sample_observations_list[i])
parameterLoader = ParameterEstimation(observations_all_list)
parameterList = parameterLoader.fit(observations_all_list,0.01,0.99)
print "Estimated Paramters: ",parameterList
params.append(parameterList[0])#mu
params.append(parameterList[1])#sd
for i, targets in enumerate(targets_list):
print 'Running HMM for sample[' + sample['sample_id'] + ']: ',
print 'chr' + targets[0]._chr + ' [' + str(temp) + '|' + str(len(targets_list)) + ']'
temp += 1
#Run the HMM
if not hetsnp and not tagsnp:
modelParams = ModelParams(mode, params, targets, het_nums=0, tagsnp=0)
elif sample_in_VCF and hetsnp and not tagsnp:
modelParams = ModelParams(mode, params, targets, snp_info_list[i], tagsnp=0)
elif sample_in_VCF and not hetsnp and tagsnp:
modelParams = ModelParams(mode, params, targets, het_nums=0, tagsnp=tagsnp_info_list[i])
elif sample_in_VCF and hetsnp and tagsnp:
modelParams = ModelParams(mode, params, targets, snp_info_list[i], tagsnp_info_list[i])
elif not sample_in_VCF and hetsnp and tagsnp:
modelParams = ModelParams(mode, params, targets, het_nums=0, tagsnp=0)
else:
pdb.set_trace()
model = Model(mode, modelParams, sample_observations_list[i])
pathlist = list()
if vcf_reader and sample_in_VCF:
pathlist = model.forwardBackward_Viterbi(mode, if_snp = True)
else:
pathlist = model.forwardBackward_Viterbi(mode, if_snp = False)
dataloader.outputCNVaux(output_aux, sample['sample_id'], targets, pathlist, sample_observations_list[i])
dataloader.outputCNV(output, sample['sample_id'], targets, pathlist, sample_observations_list[i])
sample = dataloader.getNextSample()
output.close()
output_aux.close()
dataloader.closeFile()
if not sample_flag:
print 'Could not find the sample_id specified.'
