def test_sequence_divergence(self):
from allel import sequence_divergence
pos = [2, 4, 8]
ac1 = AlleleCountsArray([[2, 0], [2, 0], [2, 0]])
ac2 = AlleleCountsArray([[0, 2], [0, 2], [0, 2]])
# all variants
e = 3 / 7
a = sequence_divergence(pos, ac1, ac2)
assert e == a
# start/stop
e = 2 / 6
a = sequence_divergence(pos, ac1, ac2, start=0, stop=5)
assert e == a
# start/stop, an provided
an1 = ac1.sum(axis=1)
an2 = ac2.sum(axis=1)
e = 2 / 6
a = sequence_divergence(pos,
ac1,
ac2,
start=0,
stop=5,
an1=an1,
an2=an2)
assert e == a
continue
# store number of snps per gene
n_dict[ID] = nsnps
# store midpoint positions of gene
pos_dict[ID] = (gene['start'] + gene['end'])/2
# fst and dxy per gene between each comparison
for comp1,comp2 in comparisons:
name = comp1 + "_" + comp2
ac1 = acsubpops[comp1].compress(gene_bool, axis=0)
ac2 = acsubpops[comp2].compress(gene_bool, axis=0)
fst_per_comp[name], se_per_comp[name],_,_= allel.average_hudson_fst(ac1, ac2, blen=1)
dxy_per_comp[name] = allel.sequence_divergence(pos[gene_bool], ac1, ac2)
# tajimas d and sequence diversity per gene for each subpop(i.e treatment)
for subpop in subpops:
ac = acsubpops[subpop].compress(gene_bool)
genepos = pos[gene_bool]
tajd_per_pop[subpop] = allel.tajima_d(ac=ac, pos=genepos)
gdiv_per_pop[subpop] = allel.sequence_diversity(ac=ac, pos=genepos)
# pbs for each gene for each pbc comparison as defined in config.yaml
if pbs is True:
for pbscomp in pbscomps:
pop1, pop2, outpop = pbscomp.split("_")
pbs_per_comp[pbscomp],se,_,_ = rnaseqpop.meanPBS(acsubpops[pop1].compress(gene_bool, axis=0),
acsubpops[pop2].compress(gene_bool, axis=0),
acsubpops[outpop].compress(gene_bool, axis=0),
def traditional_stats(data):
"""
Caclulates lots of (mostly) traditional statistics,
that are summaries of the site frequency spectrum.
Arguments
---------
data: Named tuple of results (made by collate_results function)
Returns
---------
Nested dictionary of statistics
"""
pop_names = ["domestic", "wild", "captive", "all_pops"]
stats = {
"sfs_mean": {},
"diversity": {},
"wattersons_theta": {},
"tajimas_d": {},
"observed_heterozygosity": {},
"expected_heterozygosity": {},
"segregating_sites": {},
"monomorphic_sites": {},
"roh_mean": {},
"roh_iqr": {},
"r2": {},
"f3": {},
"divergence": {},
"fst": {},
"f2": {},
}
for pop in pop_names:
# One way statistics
stats["sfs_mean"][pop] = binned_sfs_mean(data.allele_counts[pop])
stats["diversity"][pop] = allel.sequence_diversity(
data.positions, data.allele_counts[pop])
stats["wattersons_theta"][pop] = allel.watterson_theta(
data.positions, data.allele_counts[pop])
stats["tajimas_d"][pop] = allel.tajima_d(data.allele_counts[pop],
data.positions)
stats["observed_heterozygosity"][pop] = allel.heterozygosity_observed(
data.genotypes[pop]).mean()
stats["expected_heterozygosity"][pop] = allel.heterozygosity_expected(
data.allele_counts[pop].to_frequencies(), ploidy=2).mean()
stats["segregating_sites"] = data.allele_counts[pop].count_segregating(
)
if pop != "all_pops": # all_pops has no monomorphic sites
stats["monomorphic_sites"][pop] = data.allele_counts[
pop].count_non_segregating()
# Three way statistics
other_pops = [
pop_name for pop_name in pop_names
if pop_name not in ["all_pops", pop]
]
t, b = allel.patterson_f3(data.allele_counts[pop],
data.allele_counts[other_pops[0]],
data.allele_counts[other_pops[1]])
stats["f3"][pop] = np.sum(t) / np.sum(b)
# Two way statistics
for comparison in ["domestic_wild", "domestic_captive", "wild_captive"]:
p = comparison.split("_")
stats["divergence"][comparison] = allel.sequence_divergence(
data.positions, data.allele_counts[p[0]], data.allele_counts[p[1]])
num, den = allel.hudson_fst(data.allele_counts[p[0]],
data.allele_counts[p[1]])
stats["fst"][comparison] = np.sum(num) / np.sum(den)
stats["f2"][comparison] = allel.patterson_f2(
data.allele_counts[p[0]], data.allele_counts[p[1]]).mean()
return stats
