def write_imputed(t, out, debug=False, poo_phase=None):
'''Write imputed genotypes to the stream out in CGI format.'''
data, metadata, hap_type = im.recode.recode_cgi(t.imputed_data), t.genotype.metadata, t.imputed_hap_type
if poo_phase is not None:
aligned_samples = np.where(poo_phase)[0]
t.imputed_hap_type[:, aligned_samples] = im.constants.PHASED_WITH_ORIGIN
# Flip haplotypes of samples with flipped POO phase
flipped_samples = np.where(poo_phase < 0)[0]
orig = flattened_meshgrid(flipped_samples, im.constants.ALLELES)
flipped = flattened_meshgrid(flipped_samples, list(reversed(im.constants.ALLELES)))
data[:, orig[0], orig[1]] = data[:, flipped[0], flipped[1]]
if debug: np.set_printoptions(threshold=np.nan)
for snp in t.genotype.snp_range:
# Ensure that all fields are non-empty - easier to parse by subsequent processes
if metadata:
np.savetxt(out, np.array(map(lambda x: x if x else '-', metadata[snp])), fmt='%s', newline='\011', delimiter='')
# Remove trailing tab at the end of the line produced by the numpy savetxt call
out_str = StringIO.StringIO()
np.savetxt(out_str, [x[0] + x[1] for x in it.izip(it.imap(str, hap_type[snp]), (g[0] + g[1] for g in data[snp]))], fmt='%s', newline='\011', delimiter='')
out.write(out_str.getvalue()[:-1])
out.write('\n')
out.flush()
if debug: print np.concatenate((np.arange(data.shape[1])[np.newaxis].transpose(), hap_type[snp][np.newaxis].transpose(), t.imputed_data[snp]), axis=1)
if debug: np.set_printoptions(threshold=1000)
def __init__(self, problem, fraction=None, test_index=None):
'''Initialize an experiment to be run on a problem, clearing out 'fraction' of the data. If test_index
is specified, these specific test indices are used; otherwise a random fraction is generated.
If test_index = 'hap', data is read from problem.h (haplotype array). The entire array
is considered as a test array, but nothing is zeroed out. Useful for phasing result stats.'''
# Create a working copy of the problem. Only the data is copied.
if not (fraction is not None) ^ (test_index is not None):
raise ValueError('Must specify fraction or test_index')
self.problem = Problem(problem.pedigree, problem.genotype.copy())
self.h = self.problem.h
# Create test set; save original genotypes in g_orig
if test_index is None:
self.fraction = fraction
self.g_orig, i = clear_random_portion(self.problem.genotype.data, fraction)
elif test_index == 'hap':
# Don't clear anything; call everything a test index.
h = problem.h
i = tuple(util.flattened_meshgrid(range(h.shape[0]), range(h.shape[1])))
self.g_orig = problem.g
self.h = h
self.fraction = 1.0
else:
self.g_orig, i = clear_index(self.problem.g, test_index)
self.fraction = (1.0 * i[0].size) / (self.h.shape[0] * self.h.shape[1])
self.num_tests = i[0].size
self.test_index = i
self.r_orig = recode.recode_single_genotype(self.g_orig)
self.fill = self.problem.fill_fraction()[:, SAMPLE]
self.__recode_single_genotype = None
def __init__(self, problem, fraction=None, test_index=None):
'''Initialize an experiment to be run on a problem, clearing out 'fraction' of the data. If test_index
is specified, these specific test indices are used; otherwise a random fraction is generated.
If test_index = 'hap', data is read from problem.h (haplotype array). The entire array
is considered as a test array, but nothing is zeroed out. Useful for phasing result stats.'''
# Create a working copy of the problem. Only the data is copied.
if not (fraction is not None) ^ (test_index is not None):
raise ValueError('Must specify fraction or test_index')
self.problem = Problem(problem.pedigree, problem.genotype.copy())
self.h = self.problem.h
# Create test set; save original genotypes in g_orig
if test_index is None:
self.fraction = fraction
self.g_orig, i = clear_random_portion(self.problem.genotype.data,
fraction)
elif test_index == 'hap':
# Don't clear anything; call everything a test index.
h = problem.h
i = tuple(
util.flattened_meshgrid(range(h.shape[0]), range(h.shape[1])))
self.g_orig = problem.g
self.h = h
self.fraction = 1.0
else:
self.g_orig, i = clear_index(self.problem.g, test_index)
self.fraction = (1.0 * i[0].size) / (self.h.shape[0] *
self.h.shape[1])
self.num_tests = i[0].size
self.test_index = i
self.r_orig = recode.recode_single_genotype(self.g_orig)
self.fill = self.problem.fill_fraction()[:, SAMPLE]
self.__recode_single_genotype = None
def genotype_ibs_segments(genotype,
id1,
id2,
snps,
error_filter='median',
error_filter_length=5,
margin=0.0,
min_ibs_len_snp=400,
debug=False):
'''Return Identical-by-State (IBS >= 1) segments between two genoypes of samples id1 and id2
in the SNP range [snp[0],snp[1]) (if snp is a tuple) or the subset of SNPs, if snps is an array.
See ibs_segments() for a description of optional parameters.'''
num_snps = genotype.num_snps
g = genotype.data
g1 = recode.recode_single_genotype(g[snps, id1, :])
g2 = recode.recode_single_genotype(g[snps, id2, :])
d = (recode.ibs_state(g1, g2) == 0).astype(np.byte)
# Consider informative or the specified SNPs only
filtered_diff = filter_diff(d, error_filter, error_filter_length)
error_snps = snps[np.nonzero(d - filtered_diff)[0]]
# Detect edges as non-zero gradient points; output sufficiently long segments
if np.size(filtered_diff) == 0:
# No data to consider ==> no IBD intervals can be identified
segments = []
else:
# Convert recombination locations to segments of no recombination; filter short segments
bp = genotype.snp['base_pair']
#print segment.segments_with_value(filtered_diff, 0, min_ibs_len_snp)
segments = [
Segment(((x[0], x[1])), [id1, id2],
(bp[x[0]], segment.stop_bp(bp, x[1], num_snps)),
error_snps=segment.in_segment(error_snps, x),
collapse_to_set=False) for x in
segment.segments_with_value(filtered_diff, 0, min_ibs_len_snp)
]
# Cut segment margins
if margin >= constants.SMALL_FLOAT:
segments = [
s for s in (s.middle_part(
genotype.nearest_snp, bp, margin, collapse_to_set=False)
for s in segments) if s
]
# Restrict errors to those inside segments
segment_set = SegmentSet(segments,
np.array(util.flattened_meshgrid(reduce(list.__add__, (s.error_snps.tolist() for s in segments)),
np.array([id1, id2])), dtype=int) \
if segments else gt.empty_errors_array())
if debug:
print 'ibs_segments()', segment_set
print 'errors', segment_set.errors
return segment_set
def write_imputed(t, out, debug=False, poo_phase=None):
'''Write imputed genotypes to the stream out in CGI format.'''
data, metadata, hap_type = im.recode.recode_cgi(
t.imputed_data), t.genotype.metadata, t.imputed_hap_type
if poo_phase is not None:
aligned_samples = np.where(poo_phase)[0]
t.imputed_hap_type[:,
aligned_samples] = im.constants.PHASED_WITH_ORIGIN
# Flip haplotypes of samples with flipped POO phase
flipped_samples = np.where(poo_phase < 0)[0]
orig = flattened_meshgrid(flipped_samples, im.constants.ALLELES)
flipped = flattened_meshgrid(flipped_samples,
list(reversed(im.constants.ALLELES)))
data[:, orig[0], orig[1]] = data[:, flipped[0], flipped[1]]
if debug: np.set_printoptions(threshold=np.nan)
for snp in t.genotype.snp_range:
# Ensure that all fields are non-empty - easier to parse by subsequent processes
if metadata:
np.savetxt(out,
np.array(map(lambda x: x if x else '-', metadata[snp])),
fmt='%s',
newline='\011',
delimiter='')
# Remove trailing tab at the end of the line produced by the numpy savetxt call
out_str = StringIO.StringIO()
np.savetxt(out_str, [
x[0] + x[1]
for x in it.izip(it.imap(str, hap_type[snp]), (g[0] + g[1]
for g in data[snp]))
],
fmt='%s',
newline='\011',
delimiter='')
out.write(out_str.getvalue()[:-1])
out.write('\n')
out.flush()
if debug:
print np.concatenate(
(np.arange(data.shape[1])[np.newaxis].transpose(),
hap_type[snp][np.newaxis].transpose(), t.imputed_data[snp]),
axis=1)
if debug: np.set_printoptions(threshold=1000)
def __error_index(self, diff, errors, num_errors, error_type):
'''Return the corresponding snp and child indices of genotype errors. These are rows
in the errors array that have error_type non-zeros.'''
if error_type == FamilyIbdComputer.NON_TEMPLATE:
# Non-template children errors
snp_index = errors[np.where(num_errors == 1)[0]]
return (self.snps[snp_index], self.children[np.where(diff[snp_index, :] != 0)[1]])
if error_type == FamilyIbdComputer.TEMPLATE:
# Template child errors
snp_index = errors[np.where(num_errors == self.num_children - 1)[0]]
return (self.snps[snp_index], np.tile(self.children[self.template], (len(snp_index),)))
else:
# Indecisive cases: flag parent+all children as errors -- the best we can do for now.
snp_index = errors[np.where(np.logical_and(num_errors != 1, num_errors != self.num_children - 1))[0]]
a = util.flattened_meshgrid(np.concatenate((self.children, [self.parent])), self.snps[snp_index])
return (a[1], a[0])
def genotype_ibs_segments(genotype, id1, id2, snps,
error_filter='median', error_filter_length=5, margin=0.0,
min_ibs_len_snp=400, debug=False):
'''Return Identical-by-State (IBS >= 1) segments between two genoypes of samples id1 and id2
in the SNP range [snp[0],snp[1]) (if snp is a tuple) or the subset of SNPs, if snps is an array.
See ibs_segments() for a description of optional parameters.'''
num_snps = genotype.num_snps
g = genotype.data
g1 = recode.recode_single_genotype(g[snps, id1, :])
g2 = recode.recode_single_genotype(g[snps, id2, :])
d = (recode.ibs_state(g1, g2) == 0).astype(np.byte)
# Consider informative or the specified SNPs only
filtered_diff = filter_diff(d, error_filter, error_filter_length)
error_snps = snps[np.nonzero(d - filtered_diff)[0]]
# Detect edges as non-zero gradient points; output sufficiently long segments
if np.size(filtered_diff) == 0:
# No data to consider ==> no IBD intervals can be identified
segments = []
else:
# Convert recombination locations to segments of no recombination; filter short segments
bp = genotype.snp['base_pair']
#print segment.segments_with_value(filtered_diff, 0, min_ibs_len_snp)
segments = [Segment(((x[0], x[1])), [id1, id2], (bp[x[0]], segment.stop_bp(bp, x[1], num_snps)),
error_snps=segment.in_segment(error_snps, x), collapse_to_set=False)
for x in segment.segments_with_value(filtered_diff, 0, min_ibs_len_snp)]
# Cut segment margins
if margin >= constants.SMALL_FLOAT:
segments = [s for s in (s.middle_part(genotype.nearest_snp, bp, margin, collapse_to_set=False)
for s in segments) if s]
# Restrict errors to those inside segments
segment_set = SegmentSet(segments,
np.array(util.flattened_meshgrid(reduce(list.__add__, (s.error_snps.tolist() for s in segments)),
np.array([id1, id2])), dtype=int) \
if segments else gt.empty_errors_array())
if debug:
print 'ibs_segments()', segment_set
print 'errors', segment_set.errors
return segment_set
def impute(self, samples=None):
"""Infer imputed genotypes at all samples of h from the samples of g, at the location
between k-1 and k."""
# Phase all hom training samples
self.__phase_hom()
# Phase as much as possible non-hom training samples
# Bootstrap: impute R -> T; pass imputed samples from T to R; repeat
R = azip(*util.flattened_meshgrid(self.hom, ALLELES))
T = set(self.non_hom)
R, _ = self._impute_bootstrap(R, T, self.num_passes_training, True, "non-hom typed")
# Impute target samples from all available training haps
T = set(range(self.h.num_samples)) - set(self.training_sample_index)
if samples is not None:
T &= set(samples)
R, T = self._impute_bootstrap(R, T, self.num_passes_training, False, "non-typed")
return self.result
def impute(self):
'''Infer imputed genotypes at all samples of h from the samples of g, at the location
between k-1 and k.'''
# Phase all hom training samples
# print '#' * 80
# print '#' * 80
self.__phase_hom()
print 'Imputing genotyped hom', '|hom|', len(
self.hom), '|non_hom|', len(self.non_hom)
# Phase as much as possible non-hom training samples
R = util.flattened_meshgrid(self.hom, ALLELES)
# set_printoptions(threshold=np.nan)
# R = [np.array([1053]), np.array([0])]
if self.debug:
print '#' * 80
print 'Imputing genotyped non-hom', '|R|', len(R[0]), '|T|', len(
self.non_hom)
if self.debug:
print '#' * 80
print 'R', R
print 'T', self.non_hom
for sample in self.non_hom:
self._impute_sample(sample, R, True)
# Impute non-training samples from all available training haps
# R = [np.array([1053, 0]), np.array([0])]
R = self.result.nonzero()
T = set(range(self.h.num_samples)) - set(self.training_sample_index)
if self.debug:
print '#' * 80
print 'Imputing non-genotyped', '|R|', len(R[0]), '|T|', len(T)
if self.debug:
print '#' * 80
print 'R', R
print 'T', self.non_hom
for sample in T:
self._impute_sample(sample, R, False)
return self.result
def impute(self):
'''Infer imputed genotypes at all samples of h from the samples of g, at the location
between k-1 and k.'''
# Phase all hom training samples
# print '#' * 80
# print '#' * 80
self.__phase_hom()
print 'Imputing genotyped hom', '|hom|', len(self.hom), '|non_hom|', len(self.non_hom)
# Phase as much as possible non-hom training samples
R = util.flattened_meshgrid(self.hom, ALLELES)
# set_printoptions(threshold=np.nan)
# R = [np.array([1053]), np.array([0])]
if self.debug:
print '#' * 80
print 'Imputing genotyped non-hom', '|R|', len(R[0]), '|T|', len(self.non_hom)
if self.debug:
print '#' * 80
print 'R', R
print 'T', self.non_hom
for sample in self.non_hom:
self._impute_sample(sample, R, True)
# Impute non-training samples from all available training haps
# R = [np.array([1053, 0]), np.array([0])]
R = self.result.nonzero()
T = set(range(self.h.num_samples)) - set(self.training_sample_index)
if self.debug:
print '#' * 80
print 'Imputing non-genotyped', '|R|', len(R[0]), '|T|', len(T)
if self.debug:
print '#' * 80
print 'R', R
print 'T', self.non_hom
for sample in T:
self._impute_sample(sample, R, False)
return self.result
def impute(self, samples=None):
'''Infer imputed genotypes at all samples of h from the samples of g, at the location
between k-1 and k.'''
# Phase all hom training samples
self.__phase_hom()
# Phase as much as possible non-hom training samples
# Bootstrap: impute R -> T; pass imputed samples from T to R; repeat
R = azip(*util.flattened_meshgrid(self.hom, ALLELES))
T = set(self.non_hom)
R, _ = self._impute_bootstrap(R, T, self.num_passes_training, True,
'non-hom typed')
# Impute target samples from all available training haps
T = set(range(self.h.num_samples)) - set(self.training_sample_index)
if samples is not None:
T &= set(samples)
R, T = self._impute_bootstrap(R, T, self.num_passes_training, False,
'non-typed')
return self.result
def __error_index(self, diff, errors, num_errors, error_type):
'''Return the corresponding snp and child indices of genotype errors. These are rows
in the errors array that have error_type non-zeros.'''
if error_type == FamilyIbdComputer.NON_TEMPLATE:
# Non-template children errors
snp_index = errors[np.where(num_errors == 1)[0]]
return (self.snps[snp_index],
self.children[np.where(diff[snp_index, :] != 0)[1]])
if error_type == FamilyIbdComputer.TEMPLATE:
# Template child errors
snp_index = errors[np.where(num_errors == self.num_children -
1)[0]]
return (self.snps[snp_index],
np.tile(self.children[self.template], (len(snp_index), )))
else:
# Indecisive cases: flag parent+all children as errors -- the best we can do for now.
snp_index = errors[np.where(
np.logical_and(num_errors != 1,
num_errors != self.num_children - 1))[0]]
a = util.flattened_meshgrid(
np.concatenate((self.children, [self.parent])),
self.snps[snp_index])
return (a[1], a[0])
def ibs_segments(haplotype,
id1,
id2,
hap1_type,
hap2_type,
snps=None,
include_alt_phase=False,
error_filter='median',
error_filter_length=5,
length_bound=None,
min_segment_length=INDETERMINATE,
margin=0.0,
debug=False):
'''Return 1) Identical-by-State (IBS) segments separated by recombination events between two
sample haplotypes (id1, hap1_type) and (id2, hap2_type). The 2-D output array's ith row format is
(segment_start, segment_stop),
(id1, hap1), (id2, hap2),
(segment_start_bp, segment_stop_bp, segment_length_in_bp, num_errors_in_segment)
The SNP range is [segment_start, segment_stop) where start=inclusive and stop is exclusive.
2) List of het_snp indices at which there are likely genotype errors.
Options:
snps - list of SNPs to base the comparison on. For parent-child comparisons, these should
be heterozygous SNPs in the parent's genotype, distinguishing its haplotypes
and used to locate segments. For unphased-phased individuals, these should be the list of
homozygous SNPs at the unphased individual (those that have data).
If not specified, all SNPs are used.
length_bound - minimum segment length bound type:
None: no lower bound enforced
'base_pair': output segments of at least min_segment_length [base pair]
'snp': output segments of at least min_segment_length consecutive SNPs out of the snps list.
This is useful only if snps includes all SNPs (or is None)
*NOTE*: min_segment_length''s units are interpreted differently depending on length_bound.
margin = fraction of segment to discard near the endpoints (margin/2 is removed from each side).'''
if debug:
print 'Computing IBD segments between haplotypes (%d,%d), (%d,%d); filter %s length %d' % \
(id1, hap1_type, id2, hap2_type, error_filter, error_filter_length)
d = diff.all_diffs(haplotype.data,
id1,
id2,
hap1_type=hap1_type,
hap2_type=hap2_type)[0]
# Segment length, as defined by the input parameters
segment_length = lambda f: np.inf if not length_bound else (
f.length
if length_bound == 'base_pair' else f.num_snps) # @UnusedVariable
# Consider informative or the specified SNPs only
snps = snps if snps is not None else haplotype.snp_range
snps = np.intersect1d(snps, np.where(d != INDETERMINATE)[0])
d_snps = d[snps]
filtered_diff = filter_diff(d_snps, error_filter, error_filter_length)
error_snps = snps[np.nonzero(d_snps - filtered_diff)[0]]
# Detect edges as non-zero gradient points; output sufficiently long segments
bp = haplotype.snp['base_pair']
num_snps = haplotype.num_snps
if np.size(filtered_diff) == 0:
# No data to consider ==> no IBD intervals can be identified
segments = []
else:
deriv = ndimage.convolve(filtered_diff, [1, -1])
edge = np.where(deriv != 0)[0]
initial_phase = hap1_type if filtered_diff[0] == 0 else 1 - hap1_type
if debug:
print 'initial_phase', initial_phase # , 'edge', edge
# Convert recombination locations to segments of no recombination; filter short segments
segments = [
f for f in (
Segment(((x[0], x[1])),
set(((id1, x[2]), (id2, hap2_type))), (
bp[x[0]], segment.stop_bp(bp, x[1], num_snps)),
error_snps=segment.in_segment(error_snps, x))
for x in segment.edges_to_segments(
snps, edge, initial_phase, haplotype.num_snps, hap1_type))
if segment_length(f) >= min_segment_length
]
# Cut segment margins
if margin >= constants.SMALL_FLOAT:
segments = [
s for s in (s.middle_part(haplotype.nearest_snp, bp, margin)
for s in segments) if s
]
# Restrict errors to those inside segments
segment_set = SegmentSet(segments,
np.array(util.flattened_meshgrid(reduce(list.__add__, (s.error_snps.tolist() for s in segments)),
np.array([id1, id2])), dtype=int) \
if segments else gt.empty_errors_array())
if debug:
print 'ibs_segments()', segment_set
print 'errors', segment_set.errors
return segment_set
def ibs_segments(haplotype, id1, id2, hap1_type, hap2_type, snps=None, include_alt_phase=False,
error_filter='median', error_filter_length=5,
length_bound=None, min_segment_length=INDETERMINATE, margin=0.0, debug=False):
'''Return 1) Identical-by-State (IBS) segments separated by recombination events between two
sample haplotypes (id1, hap1_type) and (id2, hap2_type). The 2-D output array's ith row format is
(segment_start, segment_stop),
(id1, hap1), (id2, hap2),
(segment_start_bp, segment_stop_bp, segment_length_in_bp, num_errors_in_segment)
The SNP range is [segment_start, segment_stop) where start=inclusive and stop is exclusive.
2) List of het_snp indices at which there are likely genotype errors.
Options:
snps - list of SNPs to base the comparison on. For parent-child comparisons, these should
be heterozygous SNPs in the parent's genotype, distinguishing its haplotypes
and used to locate segments. For unphased-phased individuals, these should be the list of
homozygous SNPs at the unphased individual (those that have data).
If not specified, all SNPs are used.
length_bound - minimum segment length bound type:
None: no lower bound enforced
'base_pair': output segments of at least min_segment_length [base pair]
'snp': output segments of at least min_segment_length consecutive SNPs out of the snps list.
This is useful only if snps includes all SNPs (or is None)
*NOTE*: min_segment_length''s units are interpreted differently depending on length_bound.
margin = fraction of segment to discard near the endpoints (margin/2 is removed from each side).'''
if debug:
print 'Computing IBD segments between haplotypes (%d,%d), (%d,%d); filter %s length %d' % \
(id1, hap1_type, id2, hap2_type, error_filter, error_filter_length)
d = diff.all_diffs(haplotype.data, id1, id2, hap1_type=hap1_type, hap2_type=hap2_type)[0]
# Segment length, as defined by the input parameters
segment_length = lambda f: np.inf if not length_bound else (f.length if length_bound == 'base_pair' else f.num_snps) # @UnusedVariable
# Consider informative or the specified SNPs only
snps = snps if snps is not None else haplotype.snp_range
snps = np.intersect1d(snps, np.where(d != INDETERMINATE)[0])
d_snps = d[snps]
filtered_diff = filter_diff(d_snps, error_filter, error_filter_length)
error_snps = snps[np.nonzero(d_snps - filtered_diff)[0]]
# Detect edges as non-zero gradient points; output sufficiently long segments
bp = haplotype.snp['base_pair']
num_snps = haplotype.num_snps
if np.size(filtered_diff) == 0:
# No data to consider ==> no IBD intervals can be identified
segments = []
else:
deriv = ndimage.convolve(filtered_diff, [1, -1])
edge = np.where(deriv != 0)[0]
initial_phase = hap1_type if filtered_diff[0] == 0 else 1 - hap1_type
if debug:
print 'initial_phase', initial_phase # , 'edge', edge
# Convert recombination locations to segments of no recombination; filter short segments
segments = [f for f in (Segment(((x[0], x[1])), set(((id1, x[2]), (id2, hap2_type))),
(bp[x[0]], segment.stop_bp(bp, x[1], num_snps)),
error_snps=segment.in_segment(error_snps, x))
for x in segment.edges_to_segments(snps, edge, initial_phase,
haplotype.num_snps, hap1_type))
if segment_length(f) >= min_segment_length]
# Cut segment margins
if margin >= constants.SMALL_FLOAT:
segments = [s for s in (s.middle_part(haplotype.nearest_snp, bp, margin) for s in segments) if s]
# Restrict errors to those inside segments
segment_set = SegmentSet(segments,
np.array(util.flattened_meshgrid(reduce(list.__add__, (s.error_snps.tolist() for s in segments)),
np.array([id1, id2])), dtype=int) \
if segments else gt.empty_errors_array())
if debug:
print 'ibs_segments()', segment_set
print 'errors', segment_set.errors
return segment_set