samples=metadata,
numbers=numbers,
ploidy=ploidy,
qualflt=qualflt,
missingfltprop=missingprop)
#### Genome-wide statistics (seqDiv, Wattersons Theta, LD, inbreeding coefficient) ####
seqdivdict = {}
thetadict = {}
coefdict = {}
allcoef = defaultdict(list)
for pop in metadata['treatment'].unique():
# Sequence diversity
seqdivdict[pop] = allel.sequence_diversity(pos, acsubpops[pop])
# Wattersons theta
thetadict[pop] = allel.watterson_theta(pos, acsubpops[pop])
# Inbreeding coefficient
if ploidy > 1:
gn = geno.take(subpops[pop], axis=1)
coef = allel.moving_statistic(
gn,
statistic=allel.inbreeding_coefficient,
size=1000,
step=100)
coef = np.nanmean(coef, axis=1)
coefdict[pop] = np.mean(coef)
allcoef[pop].append(np.array(coef))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
'1': [
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
38, 39
]
}
haps = np.array(ts1.genotype_matrix())
positions = np.array([s.position for s in ts1.sites()])
genotypes = allel.HaplotypeArray(haps).to_genotypes(ploidy=2)
allele_counts = genotypes.count_alleles()
subpop_allele_counts = genotypes.count_alleles_subpops(subpops=pops)
genotype_allele_counts = genotypes.to_allele_counts()
##SNP stats
segsites = np.shape(genotypes)[0]
pi = allel.sequence_diversity(positions, allele_counts, start=1, stop=1e7)
tajD = allel.tajima_d(ac=allele_counts, start=1, stop=1e7)
thetaW = allel.watterson_theta(pos=positions,
ac=allele_counts,
start=1,
stop=1e7)
het_o = np.mean(allel.heterozygosity_observed(genotypes))
fst = allel.stats.fst.average_weir_cockerham_fst(
genotypes,
[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
[
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
37, 38, 39
]], 100)[0]
dxy = allel.stats.diversity.sequence_divergence(positions,
subpop_allele_counts['0'],
# 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),
window_size=1,
normalise=True)
# store inner dict in outer dicts
fst_per_gene[ID] = dict(fst_per_comp)
se_per_gene[ID] = dict(se_per_comp)
if pbs is True : pbs_per_gene[ID] = dict(pbs_per_comp)
tajd_per_gene[ID] = dict(tajd_per_pop)
varbool_syn = np.asarray([oc_effvars_seg_typ[i] == "synonymous_variant" for i,_ in enumerate(oc_effvars_seg_typ)])
varbool_nosyn = np.logical_or(varbool_syn , np.asarray([oc_effvars_seg_typ[i] == "missense_variant" for i,_ in enumerate(oc_effvars_seg_typ)]))
for popi in ["cluster_103","cluster_231","cluster_233"]:
j_run = len(popdich_clu[popi])
j_pi_s = np.zeros(shape=j_run)
j_pi_n = np.zeros(shape=j_run)
j_pi_n_s = np.zeros(shape=j_run)
for i in range(j_run):
j_sel1 = popdich_clu[popi][0:i] + popdich_clu[popi][i+1:j_run]
j_dic = dict()
j_dic[popi] = j_sel1
j_dic_n_ac = oc_haploty_hap_seg.subset(sel0=np.logical_and(varbool_loc, varbool_nosyn)).count_alleles_subpops(subpops=j_dic)
j_dic_s_ac = oc_haploty_hap_seg.subset(sel0=np.logical_and(varbool_loc, varbool_syn)).count_alleles_subpops(subpops=j_dic)
j_pi_n[i] = allel.sequence_diversity(pos=oc_hapvars_seg["POS"].subset(sel0=np.logical_and(varbool_loc, varbool_nosyn)), ac=j_dic_n_ac[popi])
j_pi_s[i] = allel.sequence_diversity(pos=oc_hapvars_seg["POS"].subset(sel0=np.logical_and(varbool_loc, varbool_syn)), ac=j_dic_s_ac[popi])
j_pi_n_s[i] = j_pi_n[i] / j_pi_s[i]
j_av,j_se,j_cl,j_cu,j_nu = mean_se_ci_report(j_pi_n)
print("pi_n %s \t = %.3E +/- %.3E SE, %.3E-%.3E CI95, n=%i" % (popi, j_av, j_se, j_cl, j_cu, j_nu))
j_av,j_se,j_cl,j_cu,j_nu = mean_se_ci_report(j_pi_s)
print("pi_s %s \t = %.3E +/- %.3E SE, %.3E-%.3E CI95, n=%i" % (popi, j_av, j_se, j_cl, j_cu, j_nu))
j_av,j_se,j_cl,j_cu,j_nu = mean_se_ci_report(j_pi_n_s)
print("r_n_s %s\t = %.3E +/- %.3E SE, %.3E-%.3E CI95, n=%i" % (popi, j_av, j_se, j_cl, j_cu, j_nu))
# Can we phase additional mutations?
# If we find variants that are in high linkage disequilibrium with specimens that are 0 or 2 for the P4 mutation, we can use them to infer whether a particular haplotype has ZZB, dups, or indels.
# First, load info of extra mutations:
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