def load_text_format_data(mapfn, pop_a_fn, pop_b_fn):
tbl = pd.read_csv(mapfn, sep="\t", header=None, engine="c")
try:
tbl.columns = ["ID", "CHROM", "GDist", "POS", "REF", "ALT"]
except ValueError:
logger.info("File not tab delimited as expected- trying with spaces")
tbl = pd.read_csv(mapfn,
sep=" ",
header=None,
engine="c",
names=["ID", "CHROM", "GDist", "POS", "REF", "ALT"])
try:
vartbl = allel.VariantChunkedTable(tbl.to_records(), index="POS")
except ValueError:
tbl = tbl.sort_values(["CHROM", "POS"])
logger.warning(
"Possible SNPs file is not sorted. Attempting to sort. This is likely to be inefficient"
)
vartbl = allel.VariantChunkedTable(tbl.to_records(), index="POS")
d1 = np.loadtxt(pop_a_fn, dtype="int8")
geno1 = allel.GenotypeChunkedArray(d1.reshape((d1.shape[0], -1, 2)))
d2 = np.loadtxt(pop_b_fn, dtype="int8")
geno2 = allel.GenotypeChunkedArray(d2.reshape((d2.shape[0], -1, 2)))
pos = allel.SortedIndex(vartbl.POS[:])
assert np.isnan(pos).sum() == 0, "nans values are not supported"
return geno1, geno2, allel.SortedIndex(vartbl.POS[:]), vartbl.GDist[:]
def load_arrays_noncoding_and_centromeres(local_path,
_set,
chrom,
coding_reg_df,
sitefilter='gamb_colu',
filter_centro=True):
"""
This function reads and filters a genotyping array to the noncoding, noncentromeric regions, and applys a filter depending on
whether the samples are arabiensis (arab) or gambiae/coluzzii (gamb_colu)
"""
Ag_array = zarr.open_array(
f"{local_path}/snp_genotypes/all/{_set}/{chrom}/calldata/GT/",
mode='r')
filters = zarr.open(
f"{local_path}/site_filters/dt_20200416/{sitefilter}/{chrom}/variants/filter_pass",
mode="r")
positions = zarr.open_array(
f"{local_path}/snp_genotypes/all/sites/{chrom}/variants/POS/",
mode='r')
positions = positions[:][filters[:]]
geno = allel.GenotypeDaskArray(Ag_array)
geno = geno[filters[:]]
if filter_centro is True:
if chrom == '2L':
centromere = (positions > 3000000)
elif chrom == '2R':
centromere = (positions < 57000000)
elif chrom == '3L':
centromere = (positions > 2000000)
elif chrom == '3R':
centromere = (positions < 50000000)
elif chrom == 'X':
centromere = (positions < 21000000)
positions = allel.SortedIndex(positions[centromere])
else:
positions = allel.SortedIndex(positions)
#get boolean array for positions that are coding - allel.locate_ranges so fast!
coding = positions.locate_ranges(coding_reg_df.start,
coding_reg_df.end,
strict=False)
#compress to get noncoding SNPs and remove centromeric regions of low recombination
#get non-centromeric regions. currently chosen by eye based on ag1000g phase1 paper fig1.
if filter_centro is True: geno = geno.compress(centromere, axis=0)
geno = geno.compress(
~coding,
axis=0) #we want noncoding regions so '~' to get inverse of boolean
positions = positions[~coding]
return (geno, positions)
def plotvars(chrm, callset, window_size=100000, title=None, saved=True):
"""
"""
try:
chrm = chrm.decode("utf-8")
except AttributeError:
chrm = chrm
chrom = callset['variants/CHROM']
chrom_mask = np.where(chrom[:] == chrm)
pos = callset['variants/POS']
p = pos[:][chrom_mask]
varpos = allel.SortedIndex(p)
# setup windows
bins = np.arange(0, varpos.max(), window_size)
# use window midpoints as x coordinate
x = (bins[1:] + bins[:-1]) / 2
# compute variant density in each window
h, _ = np.histogram(varpos, bins=bins)
y = h / window_size
# plot
fig, ax = plt.subplots(figsize=(12, 3))
sns.despine(ax=ax, offset=10)
ax.plot(x, y)
ax.set_xlabel('Chromosome position (bp)')
ax.set_ylabel('Variant density (bp$^{-1}$)')
if title:
ax.set_title(title)
else:
ax.set_title(chrm)
if saved:
fig.savefig("{}.vars.pdf".format(chrm), bbox_inches='tight')
def read_and_filter_genotypes(args, chromosome, window_pos_1, window_pos_2,
sites_list_chunk):
# a string representation of the target region of the current window
window_region = chromosome + ":" + str(window_pos_1) + "-" + str(
window_pos_2)
# read in data from the source VCF for the current window
callset = allel.read_vcf(args.vcf,
region=window_region,
fields=[
'CHROM', 'POS', 'calldata/GT',
'variants/is_snp', 'variants/numalt'
])
# keep track of whether the callset was empty (no sites for this range in the VCF)
# used by compute_summary_stats to add info about completely missing sites
if callset is None:
callset_is_none = True
gt_array = None
pos_array = None
else:
# if the callset is NOT empty (None), continue with pipeline
callset_is_none = False
# convert to a genotype array object
gt_array = allel.GenotypeArray(
allel.GenotypeDaskArray(callset['calldata/GT']))
# build an array of positions for the region
pos_array = allel.SortedIndex(callset['variants/POS'])
# create a mask for biallelic snps and invariant sites
snp_invar_mask = np.logical_or(
np.logical_and(callset['variants/is_snp'][:] == 1,
callset['variants/numalt'][:] == 1),
callset['variants/numalt'][:] == 0)
# remove rows that are NOT snps or invariant sites from the genotype array
gt_array = np.delete(gt_array,
np.where(np.invert(snp_invar_mask)),
axis=0)
gt_array = allel.GenotypeArray(gt_array)
# select rows that ARE snps or invariant sites in the position array
pos_array = pos_array[snp_invar_mask]
# if a list of target sites was specified, mask out all non-target sites
if sites_list_chunk is not None:
gt_array = mask_non_target_sites(gt_array, pos_array,
sites_list_chunk)
# extra 'none' check to catch cases where every site was removed by the mask
if len(gt_array) == 0:
callset_is_none = True
gt_array = None
pos_array = None
return callset_is_none, gt_array, pos_array
def misspos(chrm,
callset,
pc,
samples,
window_size=10000,
title=None,
saved=False):
"""
"""
# chrm = chrm.decode("utf-8")
chrom = callset['variants/CHROM']
chrom_mask = np.where(chrom[:] == chrm)
pos = callset['variants/POS']
p = pos[:][chrom_mask]
varpos = allel.SortedIndex(p)
bins = np.arange(0, varpos.max(), window_size)
# use window midpoints as x coordinate
x = bins
miss_site = pc[:][chrom_mask]
yy = []
for i, j in enumerate(x):
try:
left = bisect.bisect_left(varpos, j)
right = bisect.bisect_left(varpos, x[i + 1]) - 1
yy.append(np.mean(miss_site[left:right]))
except Exception:
yy.append(0)
y = np.array(yy)
ap.plotmiss(x, y / samples, title, chrm, saved)
def msp2sf2(tree_sequence, npops):
"""
"""
pix = [tree_sequence.get_samples(pop) for pop in range(npops)]
# get derived allele counts from allel
muts = tree_sequence.get_num_mutations()
sample_size = tree_sequence.get_sample_size()
V = np.zeros((muts, sample_size), dtype=np.int8)
for variant in tree_sequence.variants():
V[variant.index] = variant.genotypes
gt = allel.HaplotypeArray(V)
pos = allel.SortedIndex(
[int(variant.position) for variant in tree_sequence.variants()])
for i, p in enumerate(pix):
ac = gt[:, p].count_alleles()[:, 1]
d = open("{}.Neutral.sf2inrecomb".format(i), 'w')
d.write("position\trate\n")
with open("{}.Neutral.sf2in".format(i), 'w') as f:
f.write("position\tx\tn\tfolded\n")
for r, dac in enumerate(ac):
if dac > 0:
f.write("{}\t{}\t{}\t0\n".format(pos[r], dac, len(p)))
if r != 0:
d.write("{}\t{}\n".format(pos[r], pos[r] / 850000.0))
else:
d.write("{}\t{}\n".format(pos[r], 0))
d.close()
return (None)
def countPatternDFOIL(callset, sample_ix, outgroup):
"""Count patterns for all samples
"""
print("counting patterns in file...")
gt = allel.GenotypeArray(callset['calldata/GT'])
pos = allel.SortedIndex(callset['variants/POS'])
# remove any sites where outgroup is ./. or 0/1
keep = gt[:, outgroup].is_hom() & gt.count_alleles().is_biallelic()
gt = gt.compress(keep, axis=0)
pos = pos[keep]
windict = {}
permute = 1
g1, g2, g3, g4 = sample_ix
quartet = list(product(g1, g2, g3, g4))
print("total number of combinations: {}".format(len(quartet)))
for quart in quartet:
print("permutation number {}".format(permute))
i, j, k, m = quart
gt_sub = gt.take([i, j, k, m, outgroup], axis=1)
keep = gt_sub.is_hom().all(axis=1)
gt_sub = gt_sub.compress(keep, axis=0)
pos_sub = pos[keep]
count_array = gt_sub.is_hom_alt()
pattern_array = np.packbits(count_array, axis=1)
# windows
windict[permute] = (pos_sub, pattern_array)
permute += 1
return (windict)
def load_zarr_data(zarr_fn, chrom, s1, s2, gdistkey=None):
import zarr
samples1 = get_sample_ids(s1)
samples2 = get_sample_ids(s2)
zfh = zarr.open_group(zarr_fn, mode="r")[chrom]
samples_x = zfh["samples"][:]
sample_name = [sid.decode() for sid in samples_x.tolist()]
idx1 = np.array([sample_name.index(sid) for sid in samples1])
idx2 = np.array([sample_name.index(sid) for sid in samples2])
g = allel.GenotypeChunkedArray(zfh["calldata"]["genotype"])
pos = allel.SortedIndex(zfh["variants"]["POS"][:])
if gdistkey is not None:
gdist = h5fh["variants"][gdistkey][:]
else:
gdist = None
return g.take(idx1, axis=1), g.take(idx2, axis=1), pos, gdist
def plth12(chromlist):
"""
"""
for c in chromlist:
# callset = h5py.File("PNG.phased.autosomal.recode.{}.h5".format(c), mode='r')
callset = h5py.File("PNG.phased.X.recode.{}.h5".format(c), mode='r')
samples = callset['samples'][:]
sample_name = [sid.decode() for sid in samples.tolist()]
g = allel.GenotypeChunkedArray(callset["calldata/GT"])
h = g.to_haplotypes()
pos = allel.SortedIndex(callset["variants/POS"][:])
acc = h.count_alleles()[:, 1]
# H12
h12 = allel.moving_garud_h(h, window_size)[1] # set window size
h12_pos = []
p = 0
end = window_size
i = 0
while i < len(h12):
stop = pos[end]
while pos[p] < stop:
h12_pos.append(h12[i])
p += 1
i += 1
end += window_size
while len(h12_pos) < len(pos):
h12_pos.append(h12[-1])
plt.plot(pos, h12_pos)
plt.xlabel("{} genomic position".format(c))
plt.ylabel("H12")
plt.savefig("PNG.{}.H12.pdf".format(c))
plt.clf()
def calculate_overlap(chrom_overlap_regions, window, modern_haplotype_id,
informative_site_positions):
overlapping_bp = 0
overlapping_informative_sites = list()
if not chrom_overlap_regions.empty:
sample_chrom_overlap_regions = (chrom_overlap_regions[
chrom_overlap_regions['sample'] == modern_haplotype_id])
if not sample_chrom_overlap_regions.empty:
logging.debug(
"Overlap regions in chrom:\n{}".format(chrom_overlap_regions))
logging.debug("Window: {}".format(window.start))
overlapping_regions = sample_chrom_overlap_regions[
(chrom_overlap_regions['start'] <= window.end)
& (chrom_overlap_regions['end'] >= window.start)]
logging.debug("Overlapping regions for window:\n{}".format(
overlapping_regions))
overlapping_bp = 0
for index, region in overlapping_regions.iterrows():
logging.debug(region)
overlap = (min(region['end'], window.end) -
max(region['start'], window.start))
overlapping_bp += overlap
logging.debug("Informative site positions: {}".format(
informative_site_positions))
informative_site_index = allel.SortedIndex(
informative_site_positions)
overlapping_informative_sites = (
informative_site_index.intersect_ranges(
starts=overlapping_regions['start'],
stops=overlapping_regions['end']))
return (overlapping_bp, len(overlapping_informative_sites))
def ld_prune(gn, pos, size=500, step=200, threshold=.1, n_iter=5):
"""Remove sites in LD.
Parameters
----------
gn : TYPE
DESCRIPTION.
pos : TYPE
DESCRIPTION.
size : TYPE, optional
DESCRIPTION. The default is 500.
step : TYPE, optional
DESCRIPTION. The default is 200.
threshold : TYPE, optional
DESCRIPTION. The default is .1.
n_iter : TYPE, optional
DESCRIPTION. The default is 5.
Returns
-------
TYPE
DESCRIPTION.
gn : TYPE
DESCRIPTION.
"""
for i in range(n_iter):
loc_unlinked = allel.locate_unlinked(gn, size=size, step=step, threshold=threshold)
n = np.count_nonzero(loc_unlinked)
n_remove = gn.shape[0] - n
print(f"iteration {i+1} retaining {n} removing {n_remove} variants")
gn = gn.compress(loc_unlinked, axis=0)
pos = pos[loc_unlinked]
return allel.SortedIndex(pos), gn
def calculate_switch_distances(windows, switch_array, marker_pos, hz_pos,
rohz, gaps):
marker_pos = allel.SortedIndex(marker_pos)
gap_mp = np.mean(gaps, axis=1)
assert np.in1d(marker_pos, hz_pos).all(), "all markers are subset of hets"
marker_count = np.zeros(windows.shape[0], dtype="int")
marker_dist = np.zeros(windows.shape[0], dtype="float")
error_count = np.zeros(windows.shape[0], dtype="int")
hz_count = np.zeros(windows.shape[0], dtype="int")
pos_sw = ph.switch.derive_position_switch_array(switch_array)
pos_errors = np.take(marker_pos, pos_sw[:-1].cumsum())
for i, (start, stop) in enumerate(windows):
# this is the code I need to change
# A don't count error if immediately after GAP
# B don't count towards distance
try:
ix = marker_pos.locate_range(start, stop)
except KeyError:
marker_dist[i] = 0.0
marker_count[i] = 0
error_count[i] = 0
hz_count[i] = 0
continue
# how many separate gaps between first and last ix?
gap_ix = np.searchsorted(marker_pos[ix], gap_mp)
# interested in number of gaps
gap_pos = np.unique(
np.compress((gap_ix < marker_pos[ix].size) & (gap_ix > 0), gap_ix))
# now insert 0 and pos size at beginning and end
cuts = np.concatenate([[0], gap_pos, [marker_pos[ix].size]])
assert cuts.size >= 2
for p, q in zip(cuts[:-1], cuts[1:]):
first, last = marker_pos[ix][p], marker_pos[ix][q-1]
# how many hets between first an last?
counthets = np.searchsorted(hz_pos, last) - \
np.searchsorted(hz_pos, first)
error_count[i] += np.sum(evaluate_markers(marker_pos[ix][p:q],
pos_errors))
marker_dist[i] += calc_marker_dist(marker_pos[ix][p:q], rohz)
# just one marker is not informative.
marker_count[i] += (q - p - 1)
hz_count[i] += counthets
return np.vstack([marker_dist, marker_count, error_count, hz_count])
def load_hdf5_data(hdf5_fn, chrom, s1, s2):
callset = h5py.File(hdf5_fn, mode='r')
samples = callset['samples'][:]
sample_name = [sid.decode() for sid in samples.tolist()]
idx1 = np.array([sample_name.index(sid) for sid in s1])
idx2 = np.array([sample_name.index(sid) for sid in s2])
g = allel.GenotypeChunkedArray(callset["calldata/GT"])
pos = allel.SortedIndex(callset["variants/POS"][:])
return g.take(idx1, axis=1), g.take(idx2, axis=1), pos
def __main__():
parser = arg.ArgumentParser()
parser.add_argument('--chr', dest='chrom')
args = parser.parse_args()
# read in extra data
bed = pd.read_csv(
'/psych/ripke/vasa/reference_data/ldetect-data/EUR/fourier_ls-chr{}.bed'
.format(args.chrom),
sep='\s+')
eur_samples = pd.read_csv(
'/psych/ripke/1000Genomes_reference/1KG_Oct14/1000GP_Phase3_sr_0517d/integrated_call_samples_v3.20130502.ALL.panel.fam.EUR',
sep='\t',
names=['fid', 'iid', 'mid', 'pid', 'sex', 'pheno'],
header=None)
# read in genotype data
zarr_path = '/psych/ripke/vasa/reference_data/1000G/loc.ALL.chr{}.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.zarr'.format(
args.chrom)
callset = zarr.open_group(zarr_path, mode='r')
pos = ska.SortedIndex(callset['variants/POS'])
callset_samples = list(callset['samples'][:])
eur_samples['callset_index'] = [
callset_samples.index(s) for s in eur_samples['iid']
]
gt = callset['calldata/GT']
gt_da = ska.GenotypeDaskArray(gt)
print('Subsetting to europeans')
eur_da = gt_da.take(eur_samples['callset_index'].values, axis=1)
eur_ac = eur_da.count_alleles()
print('Filtering european singletons and invariants')
flt = (eur_ac.max_allele() == 1) & (eur_ac[:, :2].min(axis=1) > 1)
flt_mask = flt.compute()
flt_da = eur_da.compress(flt_mask, axis=0).compute()
# update variant index
pos = pos[flt_mask]
#import ipdb
#ipdb.set_trace()
print('Counting region window sizes: ')
bed['num_variants'] = np.nan
for i, region in bed.iterrows():
print('\t{} of {}'.format(i, bed.shape[0]))
loc_region = pos.locate_range(region['start'], region['stop'])
bed.loc[i, ['num_variants']] = flt_da[loc_region, :, :].n_variants
bed.to_csv('data/1000G_eur_chr{}_region_variant_counts.tsv'.format(
args.chrom),
sep='\t')
def jsfs(self, fold=False):
gt = allel.HaplotypeArray(self.haparr.T)
pos = allel.SortedIndex(self.pos)
stats_ls = []
for p1, p2 in combinations(self.stats["pop_config"], 2):
gtpops = gt.take(p1 + p2, axis=1)
props = afs.jsfs_stats(len(p1), gtpops, pos, fold)
stats_ls.extend(props)
return stats_ls
def sfs(self, fold=False):
fold = self.stats["sfs_fold"]
gt = allel.HaplotypeArray(self.haparr.T)
pos = allel.SortedIndex(self.pos)
stats_ls = []
for pop in self.stats["pop_config"]:
gtpop = gt.take(pop, axis=1)
sfs = afs.asfs_stats(gtpop, pos, fold)
stats_ls.extend(sfs)
return stats_ls
def filterGT(callset, outgroup):
"""Count patterns from VCF
"""
gt = allel.GenotypeArray(callset['calldata/GT'])
p = callset['variants/POS']
pos = allel.SortedIndex(p)
acs = gt[:, outgroup].count_alleles(max_allele=1)
flt = acs.is_segregating() # needs to be segregating in the outgroup
gt = gt.compress(flt, axis=0)
pos = pos[flt]
return (gt, pos)
def load_vcf_wrapper(path, seqid, samples):
callset = allel.read_vcf(path,
region=seqid,
fields=['variants/POS', 'calldata/GT', 'samples'],
tabix="tabix",
samples=samples)
p = allel.SortedIndex(callset["variants/POS"])
g = allel.GenotypeArray(callset['calldata/GT'])
return p, g
def tajd(self):
gt = allel.HaplotypeArray(self.haparr.T)
pos = allel.SortedIndex(self.pos)
win_size = self.stats["win_size1"]
length_bp = self.stats["length_bp"]
stats_ls = []
for pop in self.stats["pop_config"]:
gtpop = gt.take(pop, axis=1)
tajd_, tajd_std = popstats.tajimaD(pos, gtpop, win_size, length_bp)
stats_ls.extend([tajd_, tajd_std])
return stats_ls
def delta_tajD(self):
gt = allel.HaplotypeArray(self.haparr.T)
pos = allel.SortedIndex(self.pos)
win_size = self.stats["win_size1"]
length_bp = self.stats["length_bp"]
quants = self.stats["pw_quants"]
stats_ls = []
for p1, p2 in combinations(self.stats["pop_config"], 2):
gtpops = gt.take(p1 + p2, axis=1)
flt = pwpopstats.d_tajD(len(p1), pos, gtpops, win_size, length_bp,
quants)
stats_ls.extend(flt)
return stats_ls
def ddRank12(self):
gt = allel.HaplotypeArray(self.haparr.T)
pos = allel.SortedIndex(self.pos)
quants = self.stats["pw_quants"]
win_size = self.stats["win_size2"]
length_bp = self.stats["length_bp"]
stats_ls = []
for p1, p2 in combinations(self.stats["pop_config"], 2):
gtpops = gt.take(p1 + p2, axis=1)
flt = pwpopstats.ddRank1_2(len(p1), pos, gtpops, win_size,
length_bp, quants)
stats_ls.extend(flt) # 2 values returned as list [dd1, dd2]
return stats_ls
def getSNPHistogram(callset, winSize):
pos = allel.SortedIndex(callset['variants/POS'])
bins = np.arange(0, pos.max(), winSize)
# use window midpoints as x coordinate
x = (bins[1:] + bins[:-1]) / 2
# compute variant density in each window
y, _ = np.histogram(pos, bins=bins)
#y = y / windowSize
return [x, y]
def whatsnpisit(locs,
chrom,
inaccessible=False,
missense=True,
provide_region=False):
""" Given a list of locations+chrom, returns a table of those snps with their aa change
if a missense variant. Useful for RNA_seq variant calling pipeline"""
if inaccessible is False:
############ Read zarrs #############
Ag_store = zarr.open_array(
f"/home/sanj/ag1000g/data/ag1000g.phase2.ar1.pass/{chrom}/calldata/GT/",
mode='r')
positions = allel.SortedIndex(
zarr.open_array(
f"/home/sanj/ag1000g/data/ag1000g.phase2.ar1.pass/{chrom}/variants/POS",
mode='r')[:])
callset_fn = '/home/sanj/ag1000g/data/snp_eff/ag1000g.phase2.ar1.snpeff.AgamP4.2.pass.h5'
callset = h5py.File(callset_fn, mode='r')
snp_eff = callset[chrom]['variants']['ANN'][:]
else:
Ag_store = zarr.open_array(
f"/media/sanj/Sanj_HDD/Ag1000g/ag1000g.phase2.ar1/{chrom}/calldata/GT/",
mode='r')
positions = allel.SortedIndex(
zarr.open_array(
f"/media/sanj/Sanj_HDD/Ag1000g/ag1000g.phase2.ar1/{chrom}/variants/POS",
mode='r')[:])
callset_fn = '/home/sanj/ag1000g/data/all_snp_eff/ag1000g.phase2.ar1.snpeff.AgamP4.2.h5'
callset = h5py.File(callset_fn, mode='r')
snp_eff = callset[chrom]['variants']['ANN'][:]
positions_bool, pos_bool = positions.locate_intersection(locs)
snp_eff = snp_eff[positions_bool]
return (snp_eff)
def get_callables_sites(callset, chrom):
'''
Input:
- chrom : chromosome number
- callset : Zarr object which directs to all the arrays
Output:
- callable : np array boolean of shape (# SNPs, ) which encodes which positions are located in callalble regions
'''
callable_regions = get_callable(chrom)
return allel.SortedIndex(
callset['{}/variants/POS'.format(chrom)]).locate_ranges(
starts=callable_regions[:, 0],
stops=callable_regions[:, 1],
strict=False)
def FST(self):
gt = allel.HaplotypeArray(self.haparr.T)
pos = allel.SortedIndex(self.pos)
quants = self.stats["pw_quants"]
stats_ls = []
for p1, p2 in combinations(self.stats["pop_config"], 2):
gtpops = gt.take(p1 + p2, axis=1)
flt = pwpopstats.fst(len(p1), pos, gtpops, quants)
try:
stats_ls.extend(flt)
except TypeError:
flt = [np.nan] * len(quants)
stats_ls.extend(flt)
return stats_ls
def locate_intersection(positions_a, lengths_a, positions_b, lengths_b):
log("Computing position overlap")
loc_a = np.zeros(positions_a.shape, dtype=bool)
loc_b = np.zeros(positions_b.shape, dtype=bool)
ix_b, ix_a = 0, 0
for va, vb in zip(lengths_a, lengths_b):
positions_given_seq_a = allel.SortedIndex(positions_a[ix_a:(ix_a +
va)])
positions_given_seq_b = allel.SortedIndex(positions_b[ix_b:(ix_b +
vb)])
temp_loc_a, temp_loc_b = positions_given_seq_a.locate_intersection(
positions_given_seq_b)
loc_a[ix_a:(ix_a + va)] = temp_loc_a
loc_b[ix_b:(ix_b + vb)] = temp_loc_b
ix_a += va
ix_b += vb
return loc_a, loc_b
def load_text_format_data(mapfn, pop_a_fn, pop_b_fn):
tbl = pd.read_csv(mapfn,
sep=" ",
names=["ID", "CHROM", "GDist", "POS", "REF", "ALT"])
vartbl = allel.VariantChunkedTable(tbl.to_records(), index="POS")
d1 = np.loadtxt(pop_a_fn, dtype="int8")
geno1 = allel.GenotypeChunkedArray(d1.reshape((d1.shape[0], -1, 2)))
d2 = np.loadtxt(pop_b_fn, dtype="int8")
geno2 = allel.GenotypeChunkedArray(d2.reshape((d2.shape[0], -1, 2)))
return geno1, geno2, allel.SortedIndex(vartbl.POS[:]), vartbl.GDist[:]
def dmin(self):
gt = allel.HaplotypeArray(self.haparr.T)
pos = allel.SortedIndex(self.pos)
quants = self.stats["pw_quants"]
win_size = self.stats["win_size2"]
length_bp = self.stats["length_bp"]
stats_ls = []
for p1, p2 in combinations(self.stats["pop_config"], 2):
gtpops = gt.take(p1 + p2, axis=1)
flt = pwpopstats.dmin(len(p1), pos, gtpops, win_size, length_bp)
if quants[0] < 0:
dminq = [np.nanmean(flt)]
else:
dminq = np.nanquantile(flt, quants)
stats_ls.extend(dminq)
return stats_ls
def load_vcf_wrapper(path, seqid, samples, samples_path):
callset = allel.read_vcf(path,
region=seqid,
fields=['variants/POS', 'calldata/GT', 'samples'],
tabix="tabix",
samples=samples)
assert "samples" in callset.keys(
), "None of the samples provided in {0!r} are found in {1!r}".format(
samples_path, path)
p = allel.SortedIndex(callset["variants/POS"])
g = allel.GenotypeArray(callset['calldata/GT'])
return p, g
def countPattern(callset, sample_ix, outgroup):
"""Count patterns for all samples
"""
print("counting patterns in file...")
gt = allel.GenotypeArray(callset['calldata/GT'])
pos = allel.SortedIndex(callset['variants/POS'])
# remove any sites where outgroup is ./. or 0/1
keep = gt[:, outgroup].is_hom() & gt.count_alleles().is_biallelic()
gt = gt.compress(keep, axis=0)
pos = pos[keep]
# permute among all sample indexes, list of lists
# [[1,2,3,4,5],[6,7,8,9],[12,14,15,16]]
t1t2dict = defaultdict(list)
windict = {}
permute = 1
g1, g2, g3 = sample_ix
quartet = list(product(g1, g2, g3))
print("total number of combinations: {}".format(len(quartet)))
for quart in quartet:
print("permutation number {}".format(permute))
i, j, k = quart
gt_sub = gt.take([i, j, k, outgroup], axis=1)
keep = gt_sub.is_hom().all(axis=1)
gt_sub = gt_sub.compress(keep, axis=0)
pos_sub = pos[keep]
count_array = gt_sub.is_hom_alt()
pattern_array = np.packbits(count_array, axis=1)
calc_patterns = np.unique(pattern_array, return_counts=True)
d = {n: calc_patterns[1][i] for i, n in enumerate(calc_patterns[0])}
# total counts
AAAA = d.get(0, 0) + d.get(240, 0) # FFFF TTTT 240 and 0
BAAA = d.get(112, 0) + d.get(128, 0) # FTTT + TFFF 112 and 128
ABAA = d.get(176, 0) + d.get(64, 0) # TFTT + FTFF 176 and 64
AABA = d.get(208, 0) + d.get(32, 0) # TTFT + FFTF 208 and 32
BBAA = d.get(48, 0) + d.get(192, 0) # FFTT + TTFF 48 and 192
ABBA = d.get(144, 0) + d.get(96, 0) # TFFT + FTTF 144 and 96
BABA = d.get(80, 0) + d.get(160, 0) # FTFT + TFTF 80 and 160
BBBA = d.get(224, 0) + d.get(16, 0) # FFFT + TTTF 224 and 16
# t1t2 calc
t1, t2 = calct1t2(AAAA, BAAA, ABAA, AABA, BBAA, ABBA, BABA, BBBA)
t1t2dict["t1"].append(t1)
t1t2dict["t2"].append(t2)
# windows
windict[permute] = (pos_sub, pattern_array)
permute += 1
return (t1t2dict, windict)
