with open(config_file, 'r') as f:
cfg = yaml.safe_load(f)
min_DP = cfg['min_DP']
var_type = cfg['variant_type']
vcf_pat = cfg['vcf_pattern']
ped_file = cfg['ped_file']
known_vars = cfg['known_variants']
output_dir = cfg['output_directory']
test_set_pat = output_dir + '/%s'
m_pkl = joblib.load(model)
list_of_features = m_pkl['features']
if lvl == 0:
m_pkl['extra_col_names'] = []
m_pkl['y_name'] = []
myped = ped.Ped(ped_file)
myped.addTestFile(field='ind_id', file_pat=test_set_pat)
myped.ped.dropna(subset=['test'], inplace=True)
myped.ped.reset_index(inplace=True)
test_labels = numpy.array([], dtype=int)
pred_labels = numpy.array([], dtype=int)
test_var_id = numpy.array([], dtype=str)
test_alleles = numpy.array([], dtype=str)
pred_prob = numpy.array([], dtype=float)
dp_offspring = numpy.array([], dtype=int)
dp_father = numpy.array([], dtype=int)
dp_mother = numpy.array([], dtype=int)
for i, row in myped.ped.iterrows():
if row['ind_id'] != child_id:
import ped
import sys
infile_ped = '/mnt/scratch/asalomatov/data/SSC/SSCped/SSC.ped'
myped = ped.Ped(infile_ped, ['collection'])
myped.addBam(
file_pat='/mnt/scratch/asalomatov/data/SSC/wes/bam/%s.realigned.recal.bam')
myped.ped.dropna(subset=['bam'], inplace=True)
myped.ped.shape
myped.addBai(
file_pat=
'/mnt/scratch/asalomatov/data/SSC/wes/bam/%s.realigned.recal.bam.bai')
myped.ped.dropna(subset=['bai'], inplace=True)
myped.ped.shape
myped.ped.head()
probands = myped.getAllProbands()
siblings = list(myped.ped.ind_id[myped.ped.ind_id.str.contains('s1')])
print 'probands ', len(probands)
print 'siblings ', len(siblings)
out_dir = '/mnt/scratch/asalomatov/data/SSC/wes/dnm_files/bam'
for p in probands + siblings:
fa = myped.getChildsFather(p)
mo = myped.getChildsMother(p)
df = myped.ped[['ind_id', 'bam']][myped.ped.ind_id.isin([fa, mo, p])]
print df.shape
if df.shape[0] != 3:
continue
df.to_csv
print df
def summarizeMutations(infile,
outp_dir,
config_file,
exac_anno='/mnt/scratch/asalomatov/data/ExAC/fordist_cleaned_exac_r03_march16_z_pli_rec_null_data.txt'):
with open(config_file, 'r') as f:
cfg = yaml.safe_load(f)
ped_file = cfg['ped_file']
myped = ped.Ped(cfg['ped_file'])
myped.addVcf(file_pat=cfg['vcf_pattern'])
myped.ped.dropna(subset=['vcf'], inplace=True)
myped.ped.reset_index(inplace=True)
#kv_vcf = pandas.read_csv('/mnt/scratch/asalomatov/data/columbia/feature_sets/known/all_known.txt', sep='\t')
#kv_vcf = kv_vcf[['ind_id','CHROM', 'POS', 'REF_offspring', 'ALT_base_offspring', 'status', 'descr', 'DP_offspring', 'DP_father', 'DP_mother']]
#kv_vcf = kv_vcf[kv_vcf.descr.isin(['after'])]
#kv_vcf['var_id'] = kv_vcf.ind_id.astype(str)+'_'+kv_vcf.CHROM.astype(str)+'_'+kv_vcf.POS.astype(str)
#effects_of_interest = effects_loss_of_func + '|' + effect_damaging_missense + '|' + effect_synon
exac = pandas.read_table(exac_anno)
vn = pandas.read_table(infile)
vn.columns = vn.columns.str.translate(None, '#')
print vn.shape
vn.ix[:, 'gene'] = vn['ANN[*].GENE']
vn = vn.merge(
exac[[u'syn_z', u'mis_z', u'lof_z', u'pLI', u'pRec', u'pNull', u'gene']],
on='gene', how='left')
print vn.shape
#sys.exit(1)
vn['v_id'] = vn.ind_id + '_' +\
vn['CHROM'].astype(str) + '_' +\
vn.POS.astype(str) + '_' +\
vn['ANN[*].GENE'] + '_' +\
vn['ANN[*].EFFECT'] + '_' +\
vn['ANN[*].IMPACT']
vn['var_id'] = vn.ind_id + '_' +\
vn['CHROM'].astype(str) + '_' +\
vn.POS.astype(str)
vn['chr_pos'] = vn['CHROM'].astype(str) + '_' +\
vn.POS.astype(str)
#vn = vn.merge(kv_vcf[['var_id', 'status']], on='var_id', how='left')
#print vn.shape
vn = vn[~vn.v_id.duplicated()]
# stats before any filtering
print '\ndeduped and annotated vars, pred_labels value_counts:'
print vn.pred_labels.value_counts()
print 'deduped and annotated vars, test_labels value_counts:'
print vn.status.value_counts()
calcMetr(vn, msg='deduped metrics')
vn_full = vn
var_freq = vn.groupby('chr_pos').apply(lambda x: len(x['ind_id'].unique()))
var_freq_2 = var_freq[var_freq > cfg['max_cohort_freq']]
vn = vn[~vn.chr_pos.isin(var_freq_2.index)]
print '\ncohort freq vars, pred_labels value_counts:'
print vn.pred_labels.value_counts()
print 'cohort freq vars, test_labels value_counts:'
print vn.status.value_counts()
calcMetr(vn, msg='cohort_freq')
vn_diff = getDiff(vn_full, vn, msg='cohort_freq')
vn.ix[:, 'effect_cat'] = None
vn.ix[vn['ANN[*].EFFECT'].str.contains(
'|'.join(cfg['snpeff']['effect_synon'])), 'effect_cat'] = 'syn'
vn.ix[vn['ANN[*].EFFECT'].str.contains(
'|'.join(cfg['snpeff']['effect_dmgmis'])), 'effect_cat'] = 'mis'
vn.ix[vn['ANN[*].EFFECT'].str.contains(
'|'.join(cfg['snpeff']['effect_lof'])), 'effect_cat'] = 'lof'
print vn.shape
vn_full = vn
vn = vn.dropna(subset=['effect_cat'], axis=0)
print vn.shape
#vn = vn[vn['ANN[*].EFFECT'].str.contains(effects_of_interest)]
print '\neffects of interest vars, pred_labels value_counts:'
print vn.pred_labels.value_counts()
print 'effects of interest vars, test_labels value_counts:'
print vn.status.value_counts()
calcMetr(vn, msg='effects metrics')
vn_diff = pandas.concat([vn_diff, getDiff(vn_full, vn, msg='effects')])
vn.ix[vn.dbNSFP_1000Gp3_AF.isin(['.']), 'dbNSFP_1000Gp3_AF'] = '0'
vn.ix[vn.dbNSFP_ExAC_AF.isin(['.']), 'dbNSFP_ExAC_AF'] = '0'
vn.ix[:, 'dbNSFP_1000Gp3_AF'] = vn.dbNSFP_1000Gp3_AF.str.replace('ZZZ', '0')
vn.ix[:, 'dbNSFP_ExAC_AF'] = vn.dbNSFP_ExAC_AF.str.replace('ZZZ', '0')
vn.ix[:, 'dbNSFP_1000Gp3_AF'] = vn.dbNSFP_1000Gp3_AF.str.replace(',.', ',0')
vn.ix[:, 'dbNSFP_ExAC_AF'] = vn.dbNSFP_ExAC_AF.str.replace(',.', ',0')
vn.ix[:, 'dbNSFP_1000Gp3_AF'] = vn.dbNSFP_1000Gp3_AF.str.replace('.,', '0,')
vn.ix[:, 'dbNSFP_ExAC_AF'] = vn.dbNSFP_ExAC_AF.str.replace('.,', '0,')
vn.ix[:, 'dbNSFP_1000Gp3_AF'] = vn.dbNSFP_1000Gp3_AF.apply(lambda x: min(map(float, x.split(','))))
vn.ix[:, 'dbNSFP_ExAC_AF'] = vn.dbNSFP_ExAC_AF.apply(lambda x: min(map(float, x.split(','))))
vn.ix[:, 'dbNSFP_1000Gp3_AF'] = vn.dbNSFP_1000Gp3_AF.astype(float)
vn.ix[:, 'dbNSFP_ExAC_AF'] = vn.dbNSFP_ExAC_AF.astype(float)
vn_full = vn
vn = vn[(vn.dbNSFP_1000Gp3_AF < cfg['population_AF']) &
(vn.dbNSFP_ExAC_AF < cfg['population_AF'])]
print '\nAF vars, pred_labels value_counts:'
print vn.pred_labels.value_counts()
print 'AF vars, test_labels value_counts:'
print vn.status.value_counts()
calcMetr(vn, msg='AF metrics')
vn_diff = pandas.concat([vn_diff, getDiff(vn_full, vn, msg='pop_freq')])
vn_full = vn
vn = vn[vn['ANN[*].BIOTYPE'].str.contains('|'.join(cfg['snpeff']['biotype']))]
print '\nprotein coding vars, pred_labels value_counts:'
print vn.pred_labels.value_counts()
print 'protein coding vars, test_labels value_counts:'
print vn.status.value_counts()
calcMetr(vn, msg='protein coding metrics')
vn_diff = pandas.concat([vn_diff, getDiff(vn_full, vn, msg='protein')])
vn_full = vn
allele_frac = vn.alt_DP.astype(float)/vn.DP
vn = vn[(allele_frac > cfg['alt_allele_frac_range'][0]) & (allele_frac < cfg['alt_allele_frac_range'][1])]
print '\nallele fraction, pred_labels value_counts:'
print vn.pred_labels.value_counts()
print 'allel fraction vars, test_labels value_counts:'
print vn.status.value_counts()
calcMetr(vn, msg='all fraction metrics')
vn_diff = pandas.concat([vn_diff, getDiff(vn_full, vn, msg='allele_frac')])
vn = vn.replace('ZZZ', '.')
vn['FILTER'] = vn.apply(func.getFieldFromVCF, args=(myped,), axis=1)
vn = vn[~vn.FILTER.isnull()]
c_missense = vn['effect_cat'] == 'mis'
c_lof = vn['effect_cat'] == 'lof'
c_syn = vn['effect_cat'] == 'syn'
c_metaSVM_D = vn.dbNSFP_MetaSVM_pred.str.contains('|'.join(cfg['db_nsfp']['metaSVM_pred']))
c_metaSVM_null = vn.dbNSFP_MetaSVM_pred.isin(['ZZZ', '.'])
c_cadd_null = vn.dbNSFP_CADD_phred.isin(['ZZZ', '.'])
c_cadd_D = vn.dbNSFP_CADD_phred[~c_cadd_null].apply(
lambda x: min(map(float, x.split(',')))) >= cfg['db_nsfp']['cadd_phred']
c_cadd_15 = vn.dbNSFP_CADD_phred[~c_cadd_null].apply(
lambda x: min(map(float, x.split(',')))) >= cfg['db_nsfp']['combined']['cadd_phred']
c_poly_HVAR_null = vn.dbNSFP_Polyphen2_HVAR_pred.isin(['ZZZ', '.'])
c_poly_HDIV_null = vn.dbNSFP_Polyphen2_HVAR_pred.isin(['ZZZ', '.'])
c_poly_HVAR_D = vn.dbNSFP_Polyphen2_HVAR_pred.str.contains(
'|'.join(cfg['db_nsfp']['combined']['polyphen2_pred']))
c_poly_HDIV_D = vn.dbNSFP_Polyphen2_HVAR_pred.str.contains(
'|'.join(cfg['db_nsfp']['combined']['polyphen2_pred']))
c_sift_null = vn.dbNSFP_SIFT_pred.isin(['ZZZ', '.'])
c_sift_D = vn.dbNSFP_SIFT_pred.str.contains(
'|'.join(cfg['db_nsfp']['combined']['sift_pred']))
c_new = (vn.pred_labels == 1) & (~vn.status.isin(['Y']))
c_dmg_miss = c_metaSVM_D | c_cadd_D | ((c_poly_HDIV_D | c_poly_HVAR_D) & c_sift_D & c_cadd_15)
vn_full = vn[c_missense]
vn_mis = vn[c_dmg_miss & c_missense]
vn_diff = pandas.concat([vn_diff, getDiff(vn_full, vn_mis, msg='dmg_miss')])
c_impact_lof = vn['ANN[*].IMPACT'].str.contains(
'|'.join(cfg['snpeff']['impact_lof']))
vn_full = vn[c_lof]
vn_lof = vn[c_lof & c_impact_lof]
vn_diff = pandas.concat([vn_diff, getDiff(vn_full, vn_lof, msg='impact_lof')])
vn_syn = vn[c_syn]
print vn.shape
c_FN = (vn.pred_labels == 0) & vn.status.isin(['Y'])
vn_FN = vn[cols_to_output][c_FN]
#vn_FN = vn_FN[~vn_FN.v_id.duplicated()]
c_TP = (vn.pred_labels == 1) & vn.status.isin(['Y'])
vn_TP = vn[cols_to_output][c_TP]
#vn_TP = vn_TP[~vn_TP.v_id.duplicated()]
var_type = cfg['variant_type']
outp_suffix = '{:%Y-%m-%d_%H-%M-%S-%f}'.format(datetime.datetime.now())
def writeVariants(df, cols_to_output, var_type, prefix, suffix, outp_dir):
if df.empty:
print('%s is empty' % prefix)
return None
df[cols_to_output].to_csv(os.path.join(outp_dir,
'_'.join([prefix,
var_type,
suffix,
'.csv'])), index=False)
writeVariants(vn, cols_to_output[:-2], var_type, 'ALL', outp_suffix, outp_dir)
writeVariants(vn_FN, cols_to_output[:-2], var_type, 'FN', outp_suffix, outp_dir)
writeVariants(vn_TP, cols_to_output[:-2], var_type, 'TP', outp_suffix, outp_dir)
writeVariants(vn_mis, cols_to_output[:-2], var_type, 'MIS', outp_suffix, outp_dir)
writeVariants(vn_lof, cols_to_output[:-2], var_type, 'LOF', outp_suffix, outp_dir)
writeVariants(vn_syn, cols_to_output[:-2], var_type, 'SYN', outp_suffix, outp_dir)
writeVariants(vn_diff, cols_to_output[:-2]+['step'], var_type, 'DIFF', outp_suffix, outp_dir)
# vn_TP[cols_to_output[:-2]].to_csv(
# os.path.join(outp_dir, 'true_pos_snp' + outp_suffix + '.csv'), index=False)
# vn_mis[cols_to_output[:-2]][c_new].to_csv(
# os.path.join(outp_dir, 'dmg_missense' + outp_suffix + '.csv'), index=False)
# vn_lof[cols_to_output[:-2]][c_new].to_csv(
# os.path.join(outp_dir, 'lof' + outp_suffix + '.csv'), index=False)
# vn_syn[cols_to_output[:-2]][c_new].to_csv(
# os.path.join(outp_dir, 'syn' + outp_suffix + '.csv'), index=False)
# vn_diff[cols_to_output[:-2] + ['step']].to_csv(
# os.path.join(outp_dir, 'lostTP' + outp_suffix + '.csv'), index=False)
cfg['predictions_file'] = infile
with open(os.path.join(outp_dir,'cfg' + outp_suffix + '.yml'), 'w') as f:
yaml.dump(cfg, f, default_flow_style=False)
def summarizeMutations(infile,
infile_vep,
prefix,
outp_dir,
config_file,
exac_anno='/mnt/xfs1/scratch/asalomatov/data/ExAC/fordist_cleaned_exac_r03_march16_z_pli_rec_null_data.txt',
asd_gene_prob_anno='/mnt/xfs1/scratch/asalomatov/data/gene-scores/asd_gene_prediction_olga.csv',
ios_anno='/mnt/xfs1/scratch/asalomatov/data/gene-scores/ioss_lgd_rvis.scores.csv',
sfari_scores='/mnt/xfs1/scratch/asalomatov/data/SFARI/gene-score-only.csv'):
with open(config_file, 'r') as f:
cfg = yaml.safe_load(f)
ped_file = cfg['ped_file']
ped_file_extended = cfg['ped_file_extended']
# populate ped DF
if ped_file and ped_file_extended:
sys.exit('only one of ped_file, ped_file_extended may be non-empty')
if ped_file:
myped = ped.Ped(ped_file)
myped.addVcf(file_pat=cfg['vcf_pattern'])
myped.ped.dropna(subset=['vcf'], inplace=True)
myped.ped.reset_index(inplace=True)
elif ped_file_extended:
myped = ped.Ped(ped_file_extended, ['bam', 'vcf'])
else:
sys.exit('ped_file or ped_file_extended must be defined')
#kv_vcf = pandas.read_csv('/mnt/xfs1/scratch/asalomatov/data/columbia/feature_sets/known/all_known.txt', sep='\t')
#kv_vcf = kv_vcf[['ind_id','CHROM', 'POS', 'REF_offspring', 'ALT_base_offspring', 'status', 'descr', 'DP_offspring', 'DP_father', 'DP_mother']]
#kv_vcf = kv_vcf[kv_vcf.descr.isin(['after'])]
#kv_vcf['var_id'] = kv_vcf.ind_id.astype(str)+'_'+kv_vcf.CHROM.astype(str)+'_'+kv_vcf.POS.astype(str)
#effects_of_interest = effects_loss_of_func + '|' + effect_damaging_missense + '|' + effect_synon
exac = pandas.read_table(exac_anno)
sfari_scores_df = pandas.read_csv(sfari_scores)
vn = pandas.read_table(infile)
vn.columns = vn.columns.str.translate(None, '#')
vn.columns = [i.replace('[*]', '') for i in vn.columns]
# read vep
vep = func.readVcfToDF(infile_vep)
vep = vep.merge(vep.apply(lambda row: func.vepVar2vcfVar(row, cfg['genome_ref']), axis=1),
right_index=True, left_index=True)
vn.ix[:, 'gene'] = vn['ANN.GENE']
vn = vn.merge(
exac[[u'syn_z', u'syn_z_rank', u'syn_z_perc_rank',
u'mis_z', u'mis_z_rank', u'mis_z_perc_rank',
u'lof_z', u'lof_z_rank', u'lof_z_perc_rank',
u'pLI', u'pLI_rank', u'pLI_perc_rank',
u'pRec', u'pRec_rank', u'pRec_perc_rank',
u'pNull', u'pNull_rank', u'pNull_perc_rank',
u'gene']],
on='gene', how='left')
vn = vn.merge(
sfari_scores_df,
on='gene', how='left')
print(vn.shape)
vn['v_id'] = vn.ind_id.astype(str) + '_' +\
vn['CHROM'].astype(str) + '_' +\
vn.POS.astype(str) + '_' +\
vn['ANN.GENE'] # + '_' +\
# vn['ANN[*].FEATUREID']
# vn['ANN[*].EFFECT'] + '_' +\
# vn['ANN[*].IMPACT']
vn['var_id'] = vn.ind_id.astype(str) + '_' +\
vn['CHROM'].astype(str) + '_' +\
vn.POS.astype(str)
vn['chr_pos'] = vn['CHROM'].astype(str) + '_' +\
vn.POS.astype(str)
vn['chr_pos_allel_tr'] = vn['CHROM'].astype(str) + '_' +\
vn.POS.astype(str) + '_' +\
vn.REF.astype(str) + '_' +\
vn.ALT.astype(str) + '_' +\
vn['ANN.FEATUREID'].astype(str)
# if VEP anno is refseq based, translate to ensembl
if vep.Feature.str.startswith('NM').sum() > 0 or\
vep.Feature.str.startswith('XM').sum() > 0:
ens_refseq = pandas.read_csv(cfg['ens_refseq'])
vep['vep_transcript'] = vep.Feature.apply(
lambda i: i.split('.')[0])
vep = vep.merge(ens_refseq[['enst', 'nm']],
how='left',
left_on='vep_transcript',
right_on='nm')
else:
vep['enst'] = vep.Feature
vep['chr_pos'] = vep['CHROM'].astype(str) + '_' +\
vep.POS.astype(str)
vep['chr_pos_allel'] = vep['CHROM'].astype(str) + '_' +\
vep.POS.astype(str) + '_' +\
vep.REF.astype(str) + '_' +\
vep.ALT.astype(str)
# join all transcripts
vep_by_var = vep.groupby('chr_pos').apply(func.mergeFieldsForVariant).to_frame()
vep_by_var.reset_index(inplace=True)
vep_by_var.columns = ['chr_pos', 'HGVSc;Exon;Intron;HGVSp']
vep['chr_pos_allel_tr'] = vep['CHROM'].astype(str) + '_' +\
vep.POS.astype(str) + '_' +\
vep.REF.astype(str) + '_' +\
vep.ALT.astype(str) + '_' +\
vep.enst.astype(str)
print(vn.chr_pos_allel_tr)
print(vep.chr_pos_allel_tr)
print('vn dim before merging with vep:')
print(vn.shape)
vn = vn.merge(vep, how='left',
left_on='chr_pos_allel_tr',
right_on='chr_pos_allel_tr',
suffixes=['', '_vep'])
vn = vn.merge(vep_by_var, how='left',
left_on='chr_pos',
right_on='chr_pos',
suffixes=['', '_vep'])
# vn = vn.merge(kv_vcf[['var_id', 'status']], on='var_id', how='left')
print('vn dim after merging with vep:')
print(vn.shape)
print('before dedup')
print(vn.shape)
vn_dups = vn[vn.v_id.duplicated()]
vn = vn[~vn.v_id.duplicated()]
print('after dedup')
print(vn.shape)
# vn_all = vn
# stats before any filtering
# print('\ndeduped and annotated vars, pred_labels value_counts:')
# print(vn.pred_labels.value_counts())
# print('deduped and annotated vars, test_labels value_counts:')
# print(vn.status.value_counts())
# calcMetr(vn, msg='deduped metrics')
# vn_full = vn
var_freq = vn.groupby('chr_pos').apply(lambda x: len(x['ind_id'].unique()))
c_cohort_freq = var_freq > cfg['max_cohort_freq']
var_freq_2 = var_freq[c_cohort_freq]
# vn = vn[~vn.chr_pos.isin(var_freq_2.index)]
vn['c_cohort_freq'] = ~vn.chr_pos.isin(var_freq_2.index)
# print('\ncohort freq vars, pred_labels value_counts:')
# print(vn.pred_labels.value_counts())
# print('cohort freq vars, test_labels value_counts:')
# print(vn.status.value_counts())
# calcMetr(vn, msg='cohort_freq')
# vn_diff = getDiff(vn_full, vn, msg='cohort_freq')
vn.ix[:, 'effect_cat'] = 'other'
vn.ix[vn['ANN.EFFECT'].str.contains(
'|'.join(cfg['snpeff']['effect_synon'])), 'effect_cat'] = 'syn'
vn.ix[vn['ANN.EFFECT'].str.contains(
'|'.join(cfg['snpeff']['effect_dmgmis'])), 'effect_cat'] = 'mis'
vn.ix[vn['ANN.EFFECT'].str.contains(
'|'.join(cfg['snpeff']['effect_lof'])), 'effect_cat'] = 'lof'
vn['c_effect_cat'] = ~vn.effect_cat.isin(['other'])
# print(vn.shape)
# vn_full = vn
# vn = vn.dropna(subset=['effect_cat'], axis=0)
# print(vn.shape)
# print('\neffects of interest vars, pred_labels value_counts:')
# print(vn.pred_labels.value_counts())
# print('effects of interest vars, test_labels value_counts:')
# print(vn.status.value_counts())
# calcMetr(vn, msg='effects metrics')
# vn_diff = pandas.concat([vn_diff, getDiff(vn_full, vn, msg='effects')])
vn.ix[vn.dbNSFP_1000Gp3_AF.isin(['.']), 'dbNSFP_1000Gp3_AF'] = '0'
vn.ix[vn.dbNSFP_ExAC_AF.isin(['.']), 'dbNSFP_ExAC_AF'] = '0'
vn.ix[:, 'dbNSFP_1000Gp3_AF'] = vn.dbNSFP_1000Gp3_AF.str.replace('ZZZ', '0')
vn.ix[:, 'dbNSFP_ExAC_AF'] = vn.dbNSFP_ExAC_AF.str.replace('ZZZ', '0')
vn.ix[:, 'dbNSFP_1000Gp3_AF'] = vn.dbNSFP_1000Gp3_AF.str.replace(',.', ',0')
vn.ix[:, 'dbNSFP_ExAC_AF'] = vn.dbNSFP_ExAC_AF.str.replace(',.', ',0')
vn.ix[:, 'dbNSFP_1000Gp3_AF'] = vn.dbNSFP_1000Gp3_AF.str.replace('.,', '0,')
vn.ix[:, 'dbNSFP_ExAC_AF'] = vn.dbNSFP_ExAC_AF.str.replace('.,', '0,')
vn.ix[:, 'dbNSFP_1000Gp3_AF'] = vn.dbNSFP_1000Gp3_AF.apply(lambda x: min(map(float, x.split(','))))
vn.ix[:, 'dbNSFP_ExAC_AF'] = vn.dbNSFP_ExAC_AF.apply(lambda x: min(map(float, x.split(','))))
vn.ix[:, 'dbNSFP_1000Gp3_AF'] = vn.dbNSFP_1000Gp3_AF.astype(float)
vn.ix[:, 'dbNSFP_ExAC_AF'] = vn.dbNSFP_ExAC_AF.astype(float)
# vn_full = vn
vn['c_pop_freq'] = (vn.dbNSFP_1000Gp3_AF < cfg['population_AF']) &\
(vn.dbNSFP_ExAC_AF < cfg['population_AF'])
# vn = vn[(vn.dbNSFP_1000Gp3_AF < cfg['population_AF']) &
# (vn.dbNSFP_ExAC_AF < cfg['population_AF'])]
# print('\nAF vars, pred_labels value_counts:')
# print(vn.pred_labels.value_counts())
# print('AF vars, test_labels value_counts:')
# print(vn.status.value_counts())
# calcMetr(vn, msg='AF metrics')
# vn_diff = pandas.concat([vn_diff, getDiff(vn_full, vn, msg='pop_freq')])
# vn_full = vn
vn['c_biotype'] = vn['ANN.BIOTYPE'].str.contains(
'|'.join(cfg['snpeff']['biotype']))
# vn = vn[vn['ANN.BIOTYPE'].str.contains('|'.join(cfg['snpeff']['biotype']))]
# print('\nprotein coding vars, pred_labels value_counts:')
# print(vn.pred_labels.value_counts())
# print('protein coding vars, test_labels value_counts:')
# print(vn.status.value_counts())
# calcMetr(vn, msg='protein coding metrics')
# vn_diff = pandas.concat([vn_diff, getDiff(vn_full, vn, msg='protein')])
# vn_full = vn
vn['allele_frac'] = None # vn.alt_DP.astype(float)/vn.DP
vn['c_allele_frac'] = None # (vn.allele_frac > cfg['alt_allele_frac_range'][0]) &\
# (vn.allele_frac < cfg['alt_allele_frac_range'][1])
# vn = vn[(allele_frac > cfg['alt_allele_frac_range'][0]) & (allele_frac < cfg['alt_allele_frac_range'][1])]
# print('\nallele fraction, pred_labels value_counts:')
# print(vn.pred_labels.value_counts())
# print('allel fraction vars, test_labels value_counts:')
# print(vn.status.value_counts())
# calcMetr(vn, msg='all fraction metrics')
# vn_diff = pandas.concat([vn_diff, getDiff(vn_full, vn, msg='allele_frac')])
vn = vn.replace('ZZZ', '.')
# if vn.empty:
# print('No de novo mutation of interest')
# return 0
print('vn shape')
print(vn.shape)
vn['FILTER'] = None # vn.apply(func.getFieldFromVCF, args=(myped,), axis=1)
# vn = vn[~vn.FILTER.isnull()]
print('vn shape in vcf')
print(vn.shape)
non_coding_vars = ['intron_variant', 'downstream_gene_variant',
'upstream_gene_variant', 'sequence_feature',
'5_prime_UTR_variant', '3_prime_UTR_variant']
vn['coding_var'] = True
for i in non_coding_vars:
vn.ix[vn['ANN.EFFECT'] == i, 'coding_var'] = False
c_missense = vn['effect_cat'] == 'mis'
c_lof = vn['effect_cat'] == 'lof'
c_syn = vn['effect_cat'] == 'syn'
c_other = vn['effect_cat'] == 'other'
vn['c_missense'] = c_missense
vn['c_lof'] = c_lof
vn['c_syn'] = c_syn
c_M_CAP_D = vn.dbNSFP_M_CAP_pred.str.contains(
'|'.join(cfg['db_nsfp']['M_CAP_pred']))
c_metaSVM_D = vn.dbNSFP_MetaSVM_pred.str.contains(
'|'.join(cfg['db_nsfp']['metaSVM_pred']))
# c_metaSVM_null = vn.dbNSFP_MetaSVM_pred.isin(['ZZZ', '.'])
c_cadd_null = vn.dbNSFP_CADD_phred.isin(['ZZZ', '.'])
vn.ix[c_cadd_null, 'dbNSFP_CADD_phred'] = 0
vn.ix[:, 'dbNSFP_CADD_phred'] = vn.dbNSFP_CADD_phred.astype(
str).str.replace(',\.', ',0')
vn.ix[:, 'dbNSFP_CADD_phred'] = vn.dbNSFP_CADD_phred.astype(
str).str.replace('\.,', '0,')
c_cadd_D = vn.dbNSFP_CADD_phred.astype(str).apply(
lambda x: max(map(float, x.split(',')))) >=\
cfg['db_nsfp']['cadd_phred']
c_cadd_15 = vn.dbNSFP_CADD_phred.astype(str).apply(
lambda x: max(map(float, x.split(',')))) >=\
cfg['db_nsfp']['combined']['cadd_phred']
c_poly_HVAR_D = vn.dbNSFP_Polyphen2_HVAR_pred.str.contains(
'|'.join(cfg['db_nsfp']['combined']['polyphen2_pred']))
c_poly_HDIV_D = vn.dbNSFP_Polyphen2_HVAR_pred.str.contains(
'|'.join(cfg['db_nsfp']['combined']['polyphen2_pred']))
# c_sift_null = vn.dbNSFP_SIFT_pred.isin(['ZZZ', '.'])
c_sift_D = vn.dbNSFP_SIFT_pred.str.contains(
'|'.join(cfg['db_nsfp']['combined']['sift_pred']))
# c_new = (vn.pred_labels == 1) & (~vn.status.isin(['Y']))
c_dmg_miss = c_M_CAP_D | c_metaSVM_D | c_cadd_D |\
((c_poly_HDIV_D | c_poly_HVAR_D) & c_sift_D & c_cadd_15)
print('N dmg_mis w/o M_CAP %s' % sum(c_metaSVM_D | c_cadd_D |
((c_poly_HDIV_D | c_poly_HVAR_D) &
c_sift_D & c_cadd_15)))
print('N dmg_mis w M_CAP %s' % sum(c_M_CAP_D | c_metaSVM_D | c_cadd_D |
((c_poly_HDIV_D | c_poly_HVAR_D) &
c_sift_D & c_cadd_15)))
vn['c_dmg_miss'] = c_dmg_miss
vn['c_dmg_miss_woMCAP'] = c_metaSVM_D | c_cadd_D |\
((c_poly_HDIV_D | c_poly_HVAR_D) &
c_sift_D & c_cadd_15)
# vn_full = vn[c_missense]
c_impact_lof = vn['ANN.IMPACT'].str.contains(
'|'.join(cfg['snpeff']['impact_lof']))
vn['c_impact_lof'] = c_impact_lof
c_all_denovo = vn.c_cohort_freq & vn.c_pop_freq
c_prev = vn.c_cohort_freq &\
vn.c_pop_freq
# vn.c_allele_frac
# vn.c_effect_cat &\
# vn.c_biotype &\
print('sum(c_prev)')
print(sum(c_prev))
c_spark_genes = vn['ANN.GENE'].str.contains(
'|'.join(cfg['snpeff']['genes']))
vn['c_spark_genes'] = c_spark_genes
vn_mis = vn[c_dmg_miss & c_missense & c_prev]
vn_mis_clinical = vn[c_dmg_miss & c_missense & c_prev & c_spark_genes]
print('shape vn_mis')
print(vn_mis.shape)
# vn_diff = pandas.concat([vn_diff, getDiff(vn_full, vn_mis, msg='dmg_miss')])
# vn_full = vn[c_lof]
vn_lof = vn[c_lof & c_impact_lof & c_prev]
vn_lof_clinical = vn[c_lof & c_impact_lof & c_prev & c_spark_genes]
# vn_diff = pandas.concat([vn_diff, getDiff(vn_full, vn_lof, msg='impact_lof')])
vn_lof_dmis_clinical = pandas.concat([vn_lof_clinical, vn_mis_clinical])
vn_syn = vn[c_syn & c_prev]
vn_syn_clinical = vn[c_syn & c_prev & c_spark_genes]
vn_other = vn[c_other & c_prev]
vn_other_clinical = vn[c_other & c_prev & c_spark_genes]
# print(vn.shape)
c_FN = (vn.pred_labels == 0) & vn.status.isin(['Y'])
vn_FN = vn[cols_to_output][c_FN]
#vn_FN = vn_FN[~vn_FN.v_id.duplicated()]
c_TP = (vn.pred_labels == 1) & vn.status.isin(['Y'])
vn_TP = vn[cols_to_output][c_TP]
#vn_TP = vn_TP[~vn_TP.v_id.duplicated()]
var_type = cfg['variant_type']
outp_suffix = '' # '{:%Y-%m-%d_%H-%M-%S-%f}'.format(datetime.datetime.now())
def writeVariants(df, cols_to_output, var_type, prefix, suffix, outp_dir):
if df.empty:
print('%s is empty' % prefix)
return None
df = df[cols_to_output]
df.columns = map(lambda i: i[2:] if i.startswith('c_') else i,
df.columns)
df.to_csv(os.path.join(outp_dir,
'_'.join([prefix,
var_type,
suffix]) + '.csv'),
index=False)
writeVariants(vn[c_all_denovo], cols_to_output + extra_cols, var_type,
prefix, 'ALL_DENOVO', outp_dir)
writeVariants(vn[vn.c_biotype], cols_to_output + extra_cols, var_type,
prefix, 'ALL_DENOVO_CODING', outp_dir)
writeVariants(vn_FN, cols_to_output + extra_cols, var_type, prefix,
'FN', outp_dir)
writeVariants(vn_TP, cols_to_output + extra_cols, var_type, prefix,
'TP', outp_dir)
writeVariants(vn_mis, cols_to_output + extra_cols, var_type, prefix,
'MIS', outp_dir)
writeVariants(vn_lof, cols_to_output + extra_cols, var_type, prefix,
'LOF', outp_dir)
writeVariants(vn_syn, cols_to_output + extra_cols, var_type, prefix,
'SYN', outp_dir)
writeVariants(vn_other, cols_to_output + extra_cols, var_type, prefix,
'OTHER', outp_dir)
writeVariants(vn_mis_clinical, cols_to_output + extra_cols, var_type,
prefix + '_MIS', 'clinical', outp_dir)
writeVariants(vn_lof_clinical, cols_to_output + extra_cols, var_type,
prefix + '_LOF', 'clinical', outp_dir)
writeVariants(vn_lof_dmis_clinical, cols_to_output + extra_cols, var_type,
prefix + '_LOF_DMIS', 'clinical', outp_dir)
writeVariants(vn_syn_clinical, cols_to_output + extra_cols, var_type,
prefix + '_SYN', 'clinical', outp_dir)
writeVariants(vn_other_clinical, cols_to_output + extra_cols,
var_type, prefix + '_OTHER', 'clinical', outp_dir)
# writeVariants(vn_diff, cols_to_output[:-2]+['step'], var_type,
# prefix + '_DIFF', outp_suffix, outp_dir)
# vn_TP[cols_to_output[:-2]].to_csv(
# os.path.join(outp_dir, 'true_pos_snp' + outp_suffix + '.csv'), index=False)
# vn_mis[cols_to_output[:-2]][c_new].to_csv(
# os.path.join(outp_dir, 'dmg_missense' + outp_suffix + '.csv'), index=False)
# vn_lof[cols_to_output[:-2]][c_new].to_csv(
# os.path.join(outp_dir, 'lof' + outp_suffix + '.csv'), index=False)
# vn_syn[cols_to_output[:-2]][c_new].to_csv(
# os.path.join(outp_dir, 'syn' + outp_suffix + '.csv'), index=False)
# vn_diff[cols_to_output[:-2] + ['step']].to_csv(
# os.path.join(outp_dir, 'lostTP' + outp_suffix + '.csv'), index=False)
cfg['predictions_file'] = infile
with open(os.path.join(outp_dir, 'cfg' + outp_suffix + '.yml'), 'w') as f:
yaml.dump(cfg, f, default_flow_style=False)
return vn[c_all_denovo]
lambda i: get_spID(i, labID2spID))
for clr in callers:
ped_by_btch = {}
for batch_dir in batch_dirs:
if sf_seq_center == 'bay':
if batch_dir == 'b1-2':
path_toPed = '/mnt/xfs1/scratch/asalomatov/data/SPARK/ped/spark_%s.ped' % clr
else:
path_toPed = '/mnt/xfs1/scratch/asalomatov/data/SPARK/ped/spark_spID_%s_ext_%s.ped' % (
batch_dir, clr)
elif sf_seq_center == 'reg':
path_toPed = '/mnt/ceph/users/asalomatov/regeneron_spark_pilot/ped/spark_%s_ext_%s.ped' % (
batch_dir, clr)
else:
sys.exit('unknown seq_center')
myped = ped.Ped(path_toPed, ['bam', 'vcf'])
ped_by_btch[batch_dir] = myped
# check if variants are present in vcf files
other_calls['in_vcf_' + clr] = other_calls.apply(
lambda row: isInVcf(row, ped_by_btch[row['batch']]), axis=1)
other_calls['is_dn_' + clr] = other_calls.apply(
lambda row: isDeNovo(row, clr), axis=1)
other_calls['inVCF'] = isInVcfSNP(other_calls)
other_calls['isDeNovo'] = isDeNovoSNP(other_calls)
other_calls.to_csv(os.path.join(
sf_calls_dir, 'other_calls_' + '_'.join(batch_dirs) + '.csv'),
index=False)
sys.exit('done, annotate other calls')
genome_b = 'hg19'
elif args.genome_build in [38]:
genome_b = 'genome.fa'
else:
sys.exit('Unknown genome build, use 19, 37, or 38')
func.runInShell('mkdir -p ' + output_dir)
igv_inp = pandas.read_csv(input_file, dtype=str)
#igv_inp['centers'] = 'SF'
#igv_inp = pandas.read_table(input_file, dtype=str)
#for reg
if args.genome_build in [38]:
igv_inp.SP_id = igv_inp.ind_id
igv_inp.lab_id = igv_inp.ind_id
myped = ped.Ped(args.ped_file, ['bam', 'vcf'])
#myped = ped.Ped(args.ped_file, ['batch', 'bam'])
tmpl1 = """
new
genome %(genome_b)s
snapshotDirectory %(output_dir)s
load %(sample_bam)s
goto %(chr_pos)s
snapshot %(sample_snapshot_name)s
"""
tmpl3 = """
builds 19, 37, 38 are supported.')
sys.exit('Only builds 19, 37, 38 are supported')
logging.info('Using %s as pipeline config file' % incl_make)
# create output dirs
func.makeDir(output_dir)
# create temp dir
tmp_dir = tempfile.mkdtemp()
logging.info('working dir is %s' % tmp_dir)
# get path to script
script_name = os.path.abspath(
pkg_resources.resource_filename('variants', 'vep_snpeff_for_vcf.sh'))
# populate ped DF
myped = ped.Ped(ped_file_extended, cfg['ped_extra_clmns'])
f = features.Features(myped, None)
# trio has to be complete with no file missing
if not f.initTrioFor(child_id):
logging.critical('\nfailed to initialize trio for ' + child_id)
sys.exit(1)
sys.stdout.write('\ninitialized trio for ' + child_id)
sys.stdout.write('\n')
logging.info('father and mother: ' +
' '.join([f.father_id, f.mother_id]))
# extract just the trio, drop non variant loci
# annotate, and filter based on AF
vrs = variants.Variants(f.sample_vcf, f.family_id)
vrs.readVcfToDF(sample_list=[f.sample_id, f.father_id, f.mother_id])
import variants
import ped
import features_vcf as fv
import pandas as pd
import features_vcf
import numpy as np
import os
import sklearn
variants = reload(variants)
func = reload(func)
ped = reload(ped)
###SSC
infile_ped = '/mnt/scratch/asalomatov/data/SSC/SSCped/SSC.ped'
myped = ped.Ped(infile_ped, ['collection'])
myped.getParents(11006)
#myped.addVcf()
myped.addVcf(file_pat = '/mnt/scratch/asalomatov/data/SSC/vcf/raw/%s.family.vqsr.sorted.vcf.gz')
myped.ped.head()
###Columbia
infile_ped = '/mnt/scratch/asalomatov/data/columbia/pcgc_ped.txt'
myped = ped.Ped(infile_ped, [])
myped.getParents('1-00034')
myped.getFather('1-00034')
myped.
myped.ped.head()
myped.ped.shape
myped.addVcf(file_pat = '/mnt/scratch/asalomatov/data/columbia/vcf/deco/%s_%s-02_%s-01.annotated-deco.vcf.gz')
sum(myped.ped.vcf.notnull())
