def sample_variants(c, args):
idx_to_sample = util.map_indicies_to_samples(c)
query = "SELECT variant_id, gt_types, gts, gene, impact, biotype, \
in_dbsnp, clinvar_sig, clinvar_disease_name, aaf_1kg_all, aaf_esp_all, chrom, \
start, end \
FROM variants"
c.execute(query)
if args.command == 'interactions':
#header
if args.var_mode:
print "\t".join(['sample','gene','order_of_interaction', \
'interacting_gene', 'var_id', 'chrom', 'start', \
'end', 'impact', 'biotype', 'in_dbsnp', \
'clinvar_sig', 'clinvar_disease_name', 'aaf_1kg_all', \
'aaf_esp_all'])
if (not args.var_mode):
print "\t".join(['sample','gene','order_of_interaction', \
'interacting_gene'])
sample_gene_interactions(c, args, idx_to_sample)
elif args.command == 'lof_interactions':
samples = get_variant_genes(c, args, idx_to_sample)
return samples
def get_genotypes(c, args):
"""For each variant, report each sample's genotype
on a separate line.
"""
idx_to_sample = util.map_indicies_to_samples(c)
query = "SELECT v.chrom, v.start, v.end, \
v.ref, v.alt, \
v.type, v.sub_type, \
v.aaf, v.in_dbsnp, v.gene, \
v.gts \
FROM variants v \
ORDER BY chrom, start"
c.execute(query)
# build a list of all the column indices that are NOT
# gt_* columns. These will be the columns reported
(col_names, non_gt_idxs) = \
util.get_col_names_and_indices(c.description, ignore_gt_cols=True)
col_names.append('sample')
col_names.append('genotype')
if args.use_header:
print args.separator.join(col for col in col_names)
for row in c:
gts = np.array(cPickle.loads(zlib.decompress(row['gts'])))
for idx, gt in enumerate(gts):
# xrange(len(row)-1) to avoid printing v.gts
print args.separator.join(str(row[i]) for i in xrange(len(row)-1)),
print args.separator.join([idx_to_sample[idx], gt])
def get_gtcounts_by_sample(c, args):
"""
Report the count of each genotype class
observed for each sample.
"""
idx_to_sample = util.map_indicies_to_samples(c)
# report.
print '\t'.join([
'sample', 'num_hom_ref', 'num_het', 'num_hom_alt', 'num_unknown',
'total'
])
query = "SELECT *, \
(num_hom_ref + num_het + num_hom_alt + num_unknown) as total \
FROM sample_genotype_counts"
c.execute(query)
# count the number of each genotype type obs. for each sample.
for row in c:
sample = idx_to_sample[row['sample_id']]
print "\t".join(
str(s) for s in [
sample, row['num_hom_ref'], row['num_het'], row['num_hom_alt'],
row['num_unknown'], row['total']
])
def get_genotypes(c, args):
"""For each variant, report each sample's genotype
on a separate line.
"""
idx_to_sample = util.map_indicies_to_samples(c)
query = "SELECT v.chrom, v.start, v.end, \
v.ref, v.alt, \
v.type, v.sub_type, \
v.aaf, v.in_dbsnp, v.gene, \
v.gts \
FROM variants v \
ORDER BY chrom, start"
c.execute(query)
# build a list of all the column indices that are NOT
# gt_* columns. These will be the columns reported
(col_names, non_gt_idxs) = \
util.get_col_names_and_indices(c.description, ignore_gt_cols=True)
col_names.append('sample')
col_names.append('genotype')
if args.use_header:
print args.separator.join(col for col in col_names)
for row in c:
gts = np.array(cPickle.loads(zlib.decompress(row['gts'])))
for idx, gt in enumerate(gts):
# xrange(len(row)-1) to avoid printing v.gts
print args.separator.join(
str(row[i]) for i in xrange(len(row) - 1)),
print args.separator.join([idx_to_sample[idx], gt])
def sample_lof_variants(c, args):
idx_to_sample = util.map_indicies_to_samples(c)
query = "SELECT chrom, start, end, \
gt_types, gts, gene \
FROM variants \
WHERE is_lof='1'"
c.execute(query)
sample_lof_interactions(c, args, idx_to_sample)
def __init__(self, db):
self.db = db
self.query_executed = False
self.for_browser = False
self._connect_to_database()
# map sample names to indices. e.g. self.sample_to_idx[NA20814] -> 323
self.sample_to_idx = util.map_samples_to_indicies(self.c)
# and vice versa. e.g., self.idx_to_sample[323] -> NA20814
self.idx_to_sample = util.map_indicies_to_samples(self.c)
def sample_lof_variants(c, args):
idx_to_sample = util.map_indicies_to_samples(c)
query = "SELECT chrom, start, end, \
gt_types, gts, gene \
FROM variants \
WHERE is_lof='1'"
c.execute(query)
sample_lof_interactions(c, args, idx_to_sample)
def __init__(self, db):
self.db = db
self.query_executed = False
self.for_browser = False
self._connect_to_database()
# map sample names to indices. e.g. self.sample_to_idx[NA20814] -> 323
self.sample_to_idx = util.map_samples_to_indicies(self.c)
# and vice versa. e.g., self.idx_to_sample[323] -> NA20814
self.idx_to_sample = util.map_indicies_to_samples(self.c)
def __init__(self, db, include_gt_cols=False):
assert os.path.exists(db), "%s does not exist." % db
self.db = db
self.query_executed = False
self.for_browser = False
self.include_gt_cols = include_gt_cols
self._connect_to_database()
# map sample names to indices. e.g. self.sample_to_idx[NA20814] -> 323
self.sample_to_idx = util.map_samples_to_indicies(self.c)
# and vice versa. e.g., self.idx_to_sample[323] -> NA20814
self.idx_to_sample = util.map_indicies_to_samples(self.c)
def sample_variants(c, args):
idx_to_sample = util.map_indicies_to_samples(c)
query = "SELECT variant_id, gt_types, gts, gene, impact, biotype \
FROM variants"
c.execute(query)
if args.var_mode:
print "\t".join(['sample','gene','order_of_interaction', \
'interacting_gene', 'var_id','impact','biotype'])
elif (not args.var_mode):
print "\t".join(['sample','gene','order_of_interaction', \
'interacting_gene'])
sample_gene_interactions(c, args, idx_to_sample)
def sample_variants(c, args):
idx_to_sample = util.map_indicies_to_samples(c)
query = "SELECT variant_id, gt_types, gts, gene, impact, biotype \
FROM variants"
c.execute(query)
if args.var_mode:
print "\t".join(['sample','gene','order_of_interaction', \
'interacting_gene', 'var_id','impact','biotype'])
elif (not args.var_mode):
print "\t".join(['sample','gene','order_of_interaction', \
'interacting_gene'])
sample_gene_interactions(c, args, idx_to_sample)
def get_ind_lof(c, args):
idx_to_sample = util.map_indicies_to_samples(c)
query = "SELECT v.chrom, v.start, v.end, v.ref, v.alt, \
v.impact, v.aa_change, v.aa_length, \
v.gt_types, v.gts, i.gene, \
i.transcript, i.biotype\
FROM variants v, variant_impacts i \
WHERE v.variant_id = i.variant_id \
AND i.is_lof='1' \
AND v.type = 'snp'"
c.execute(query)
# header
print '\t'.join([
'chrom', 'start', 'end', 'ref', 'alt', 'highest_impact', 'aa_change',
'var_trans_pos', 'trans_aa_length', 'var_trans_pct', 'sample',
'genotype', 'gene', 'transcript', 'trans_type'
])
for r in c:
gt_types = np.array(cPickle.loads(zlib.decompress(r['gt_types'])))
gts = np.array(cPickle.loads(zlib.decompress(r['gts'])))
gene = str(r['gene'])
trans = str(r['transcript'])
aa_change = str(r['aa_change'])
aa_length = str(r['aa_length'])
transcript_pos = None
transcript_pct = None
if aa_change != 'None':
transcript_pos = re.findall('\S(\d+)\S', aa_change)[0]
if aa_length != 'None':
transcript_pct = float(transcript_pos) / float(aa_length)
for idx, gt_type in enumerate(gt_types):
if gt_type == HET or gt_type == HOM_ALT:
print "\t".join([
r['chrom'],
str(r['start']),
str(r['end']), r['ref'], r['alt'], r['impact'],
r['aa_change'] or 'None', transcript_pos or 'None',
r['aa_length'] or 'None',
str(transcript_pct) or 'None', idx_to_sample[idx],
gts[idx], gene, trans, r['biotype']
])
def get_ind_lof(c, args):
idx_to_sample = util.map_indicies_to_samples(c)
query = "SELECT v.chrom, v.start, v.end, v.ref, v.alt, \
v.impact, v.aa_change, v.aa_length, \
v.gt_types, v.gts, i.gene, \
i.transcript, i.biotype\
FROM variants v, variant_impacts i \
WHERE v.variant_id = i.variant_id \
AND i.is_lof='1' \
AND v.type = 'snp'"
c.execute(query)
# header
print '\t'.join(['chrom', 'start', 'end', 'ref', 'alt', \
'highest_impact', 'aa_change', 'var_trans_pos',
'trans_aa_length', 'var_trans_pct', \
'sample', 'genotype', 'gene', 'transcript', 'trans_type'])
for r in c:
gt_types = np.array(cPickle.loads(zlib.decompress(r['gt_types'])))
gts = np.array(cPickle.loads(zlib.decompress(r['gts'])))
gene = str(r['gene'])
trans = str(r['transcript'])
aa_change = str(r['aa_change'])
aa_length = str(r['aa_length'])
transcript_pos = None
transcript_pct = None
if aa_change != 'None':
transcript_pos = re.findall('\S(\d+)\S', aa_change)[0]
if aa_length != 'None':
transcript_pct = float(transcript_pos) / float(aa_length)
for idx, gt_type in enumerate(gt_types):
if gt_type == GT_HET or gt_type == GT_HOM_ALT:
print "\t".join([r['chrom'], str(r['start']), \
str(r['end']), r['ref'], r['alt'], \
r['impact'], \
r['aa_change'] or 'None', \
transcript_pos or 'None', \
r['aa_length'] or 'None', \
str(transcript_pct) or 'None', \
idx_to_sample[idx], \
gts[idx], gene, trans, r['biotype']])
def get_ind_pathways(c, args):
idx_to_sample = util.map_indicies_to_samples(c)
query = "SELECT v.chrom, v.start, v.end, v.ref, v.alt, \
i.impact, v.gt_types, v.gts, i.gene, \
i.transcript \
FROM variants v, variant_impacts i \
WHERE v.variant_id = i.variant_id"
c.execute(query)
# header
print '\t'.join(['chrom', 'start', 'end', 'ref', 'alt', \
'highest_impact', 'sample', 'genotype', \
'gene', 'transcript', 'pathway'])
_report_variant_pathways(c, args, idx_to_sample)
def get_variants_by_sample(c, args):
"""
Report the number of variants observed for each sample
where the sample had a non-ref genotype
"""
idx_to_sample = util.map_indicies_to_samples(c)
# report.
print '\t'.join(['sample', 'total'])
query = "SELECT sample_id, \
(num_het + num_hom_alt) as total \
FROM sample_genotype_counts"
c.execute(query)
for row in c:
sample = idx_to_sample[row['sample_id']]
print "\t".join(str(s) for s in [sample,
row['total']])
def get_ind_pathways(c, args):
idx_to_sample = util.map_indicies_to_samples(c)
query = "SELECT v.chrom, v.start, v.end, v.ref, v.alt, \
i.impact, v.gt_types, v.gts, i.gene, \
i.transcript \
FROM variants v, variant_impacts i \
WHERE v.variant_id = i.variant_id"
c.execute(query)
# header
print "\t".join(
["chrom", "start", "end", "ref", "alt", "impact", "sample", "genotype", "gene", "transcript", "pathway"]
)
_report_variant_pathways(c, args, idx_to_sample)
def get_ind_pathways(c, args):
idx_to_sample = util.map_indicies_to_samples(c)
query = "SELECT v.chrom, v.start, v.end, v.ref, v.alt, \
i.impact, v.gt_types, v.gts, i.gene, \
i.transcript \
FROM variants v, variant_impacts i \
WHERE v.variant_id = i.variant_id"
c.execute(query)
# header
print '\t'.join(['chrom', 'start', 'end', 'ref', 'alt', \
'impact', 'sample', 'genotype', \
'gene', 'transcript', 'pathway'])
_report_variant_pathways(c, args, idx_to_sample)
def sample_lof_variants(c, args, samples):
idx_to_sample = util.map_indicies_to_samples(c)
query = "SELECT chrom, start, end, \
gt_types, gts, gene \
FROM variants \
WHERE is_lof='1'"
c.execute(query)
#header
if args.var_mode:
print "\t".join(['sample','lof_gene','order_of_interaction', \
'interacting_gene', 'var_id', 'chrom', 'start', \
'end', 'impact','biotype','in_dbsnp', 'clin_sigs', \
'aaf_1kg_all','aaf_esp_all'])
elif (not args.var_mode):
print "\t".join(['sample','lof_gene','order_of_interaction', \
'interacting_gene'])
sample_lof_interactions(c, args, idx_to_sample, samples)
def sample_lof_variants(c, args, samples):
idx_to_sample = util.map_indicies_to_samples(c)
query = "SELECT chrom, start, end, \
gt_types, gts, gene \
FROM variants \
WHERE is_lof='1'"
c.execute(query)
#header
if args.var_mode:
print "\t".join(['sample','lof_gene','order_of_interaction', \
'interacting_gene', 'var_id', 'chrom', 'start', \
'end', 'impact','biotype','in_dbsnp', 'clinvar_sig', \
'clinvar_disease_name', 'aaf_1kg_all','aaf_esp_all'])
elif (not args.var_mode):
print "\t".join(['sample','lof_gene','order_of_interaction', \
'interacting_gene'])
sample_lof_interactions(c, args, idx_to_sample, samples)
def get_gtcounts_by_sample(c, args):
"""
Report the count of each genotype class
observed for each sample.
"""
idx_to_sample = util.map_indicies_to_samples(c)
# report.
print '\t'.join(['sample', 'num_hom_ref', 'num_het',
'num_hom_alt', 'num_unknown', 'total'])
query = "SELECT *, \
(num_hom_ref + num_het + num_hom_alt + num_unknown) as total \
FROM sample_genotype_counts"
c.execute(query)
# count the number of each genotype type obs. for each sample.
for row in c:
sample = idx_to_sample[row['sample_id']]
print "\t".join(str(s) for s in [sample,
row['num_hom_ref'],
row['num_het'],
row['num_hom_alt'],
row['num_unknown'],
row['total']])
def get_compound_hets(c, args):
"""
Report candidate compound heterozygous mutations.
"""
# build a mapping of the numpy array index to the appropriate sample name
# e.g. 0 == 109400005
# 37 == 147800025
idx_to_sample = util.map_indicies_to_samples(c)
comp_hets = collections.defaultdict(lambda: collections.defaultdict(list))
query = "SELECT * FROM variants \
WHERE is_coding = 1" # is_exonic - what about splice?
c.execute(query)
# step 1. collect all candidate heterozygptes for all
# genes and samples. the list will be refined in step 2.
for row in c:
gt_types = np.array(cPickle.loads(zlib.decompress(row['gt_types'])))
gt_phases = np.array(cPickle.loads(zlib.decompress(row['gt_phases'])))
gt_bases = np.array(cPickle.loads(zlib.decompress(row['gts'])))
site = Site(row)
# filter putative sites that the user doesn't care about
if site.num_hets > 1 and not args.allow_other_hets:
continue
if not site.is_lof and args.only_lof:
continue
# track each sample that is heteroyzgous at this site.
for idx, gt_type in enumerate(gt_types):
if gt_type == GT_HET:
sample = idx_to_sample[idx]
# (testing)
# sample = "NA19002"
sample_site = copy(site)
sample_site.phased = gt_phases[idx]
# require phased genotypes
if not sample_site.phased:
continue
sample_site.gt = gt_bases[idx]
# add the site to the list of candidates
# for this sample/gene
comp_hets[sample][site.gene].append(sample_site)
# header
print "sample\tgene\thet1\thet2"
# step 2. now, cull the list of candidate heterozygotes for each
# gene/sample to those het pairs where the alternate alleles
# were inherited on opposite haplotypes.
for sample in comp_hets:
for gene in comp_hets[sample]:
for site1 in comp_hets[sample][gene]:
for site2 in comp_hets[sample][gene]:
if site1 == site2:
continue
# expand the genotypes for this sample
# at each site into it's composite
# alleles. e.g. A|G -> ['A', 'G']
alleles_site1 = site1.gt.split('|')
alleles_site2 = site2.gt.split('|')
# return the haplotype on which the alternate
# allele was observed for this sample at each
# candidate het. site.
# e.g., if ALT=G and alleles_site1=['A', 'G']
# then alt_hap_1 = 1. if ALT=A, then alt_hap_1 = 0
alt_hap_1 = alleles_site1.index(site1.alt)
alt_hap_2 = alleles_site2.index(site2.alt)
# it is only a true compound heterozygote iff
# the alternates are on opposite haplotypes.
if alt_hap_1 != alt_hap_2:
print "\t".join([sample, gene, str(site1), str(site2)])
def get_compound_hets(c, args):
"""
Report candidate compound heterozygous mutations.
"""
# build a mapping of the numpy array index to the appropriate sample name
# e.g. 0 == 109400005
# 37 == 147800025
idx_to_sample = util.map_indicies_to_samples(c)
comp_hets = collections.defaultdict(lambda: collections.defaultdict(list))
query = "SELECT * FROM variants \
WHERE impact_severity != 'LOW'" # is_exonic - what about splice?
c.execute(query)
# step 1. collect all candidate heterozygptes for all
# genes and samples. the list will be refined in step 2.
for row in c:
gt_types = compression.unpack_genotype_blob(row['gt_types'])
gt_phases = compression.unpack_genotype_blob(row['gt_phases'])
gt_bases = compression.unpack_genotype_blob(row['gts'])
site = Site(row)
# filter putative sites that the user doesn't care about
if site.num_hets > 1 and not args.allow_other_hets:
continue
if not site.is_lof and args.only_lof:
continue
# track each sample that is heteroyzgous at this site.
for idx, gt_type in enumerate(gt_types):
if gt_type == HET:
sample = idx_to_sample[idx]
# (testing)
# sample = "NA19002"
sample_site = copy(site)
sample_site.phased = gt_phases[idx]
# require phased genotypes
if not sample_site.phased and not args.ignore_phasing:
continue
sample_site.gt = gt_bases[idx]
# add the site to the list of candidates
# for this sample/gene
comp_hets[sample][site.gene].append(sample_site)
# header
print "sample\tgene\thet1\thet2"
# step 2. now, cull the list of candidate heterozygotes for each
# gene/sample to those het pairs where the alternate alleles
# were inherited on opposite haplotypes.
for sample in comp_hets:
for gene in comp_hets[sample]:
for site1 in comp_hets[sample][gene]:
for site2 in comp_hets[sample][gene]:
if site1 == site2:
continue
# expand the genotypes for this sample
# at each site into it's composite
# alleles. e.g. A|G -> ['A', 'G']
alleles_site1 = []
alleles_site2 = []
if not args.ignore_phasing:
alleles_site1 = site1.gt.split('|')
alleles_site2 = site2.gt.split('|')
else:
# split on phased (|) or unphased (/) genotypes
alleles_site1 = re.split('\||/', site1.gt)
alleles_site2 = re.split('\||/', site2.gt)
# it is only a true compound heterozygote iff
# the alternates are on opposite haplotypes.
if not args.ignore_phasing:
# return the haplotype on which the alternate
# allele was observed for this sample at each
# candidate het. site.
# e.g., if ALT=G and alleles_site1=['A', 'G']
# then alt_hap_1 = 1. if ALT=A, then alt_hap_1 = 0
alt_hap_1 = alleles_site1.index(site1.alt)
alt_hap_2 = alleles_site2.index(site2.alt)
if alt_hap_1 != alt_hap_2:
print "\t".join([sample,
gene,
str(site1),
str(site2)])
else:
# user has asked us to not care about phasing
print "\t".join([sample,
gene,
str(site1),
str(site2)])
def get_ind_lof(c, args):
idx_to_sample = util.map_indicies_to_samples(c)
query = "SELECT v.chrom, v.start, v.end, v.ref, v.alt, \
v.impact, v.aa_change, v.aa_length, \
v.gt_types, v.gts, i.gene, \
i.transcript, i.biotype\
FROM variants v, variant_impacts i \
WHERE v.variant_id = i.variant_id \
AND i.is_lof='1' \
AND v.type = 'snp'"
c.execute(query)
# header
print "\t".join(
[
"chrom",
"start",
"end",
"ref",
"alt",
"highest_impact",
"aa_change",
"var_trans_pos",
"trans_aa_length",
"var_trans_pct",
"sample",
"genotype",
"gene",
"transcript",
"trans_type",
]
)
for r in c:
gt_types = np.array(cPickle.loads(zlib.decompress(r["gt_types"])))
gts = np.array(cPickle.loads(zlib.decompress(r["gts"])))
gene = str(r["gene"])
trans = str(r["transcript"])
aa_change = str(r["aa_change"])
aa_length = str(r["aa_length"])
transcript_pos = None
transcript_pct = None
if aa_change != "None":
transcript_pos = re.findall("\S(\d+)\S", aa_change)[0]
if aa_length != "None":
transcript_pct = float(transcript_pos) / float(aa_length)
for idx, gt_type in enumerate(gt_types):
if gt_type == HET or gt_type == HOM_ALT:
print "\t".join(
[
r["chrom"],
str(r["start"]),
str(r["end"]),
r["ref"],
r["alt"],
r["impact"],
r["aa_change"] or "None",
transcript_pos or "None",
r["aa_length"] or "None",
str(transcript_pct) or "None",
idx_to_sample[idx],
gts[idx],
gene,
trans,
r["biotype"],
]
)
