def run_stats(ref_sim, ref_pair, data, data_freqs={}):
'''
co-factor function to md counter comparisons, deploy heatmap and calculate kmer proportion differences
between pairs of population.
- ref pair: list of tuples. can't be dictionary because of repeated pops / reference tags.
'''
batch = ref_sim.split('C')[0]
sizes = [data[x[0]]['sizes'][x[1]] for x in ref_pair]
#
chromosomes = [ref_sim.split('.')[0].split('C')[1]]
pop_counts = {g: data[g[0]]['counts'][g[1]] for g in ref_pair}
num_variants = {g: data[g[0]]['Nvars'][g[1]] for g in ref_pair}
ratio_grid, sig_cells = heatmap_v2(chromosomes,
pop_counts,
num_variants, {},
frequency_range,
exclude,
p_value,
muted_dir,
tag='',
test=test_m,
output='pval')
pop_counts = {z: s / np.sum(s) for z, s in pop_counts.items()}
grid_diffs = pop_counts[ref_pair[0]] - pop_counts[ref_pair[1]]
comb_stats = {
'grids': ratio_grid,
'sigs': sig_cells,
'sizes': sizes,
'batch': batch,
'diffs': grid_diffs
}
if data_freqs:
comb_stats['freqs'] = {
ref_pair.index(x): data_freqs[x[0]][x[1]]
for x in ref_pair
}
return comb_stats
def md_reference_comp(data,
p_value=1e-5,
test_m='fisher',
individually=False,
Nbins=10,
exclude=False,
frequency_range=[0, 1],
data_freqs={},
extract='pval',
muted_dir='',
tag_ref='_ss'):
'''
Parse data dictionary.
data: {sim: {counts:{pop:g}, Nvars:{pop:g}, sizes:{pop:g}}}
i: use sim and pop IDs to create dictionary connecting original populations to
subset populations created using ind_assignment_scatter_v1.
ii: for each pair of reference populations, launch heatmapv2. return grid pvals or proportions,
and proportion of mutations in subset population. allows for fisher or chi2 test for pval.
'''
bins = np.linspace(0, 1, Nbins)
bins = np.round(bins, 4)
bins = [(bins[x - 1], bins[x]) for x in range(1, len(bins))]
avail = list(data.keys())
ref_idx = [int(tag_ref in avail[x]) for x in range(len(avail))]
categ = {
z: [x for x in range(len(avail)) if ref_idx[x] == z]
for z in [0, 1]
}
print([len(categ[x]) for x in [0, 1]])
### possible combinations per simulation.
ref_combos = {}
for idx in categ[0]:
ref = avail[idx]
ref_combs = list(data[ref]['counts'].keys())
ref_combs = it.combinations(ref_combs, 2)
ref_combs = list(ref_combs)
comb_dict = {x: {} for x in ref_combs}
comb_stats = {}
for pair in ref_combs:
batch = ref.split('C')[0]
pop1, pop2 = pair
sizes = [data[ref]['sizes'][x] for x in pair]
#
chromosomes = [ref.split('.')[0].split('C')[1]]
pop_counts = {x: data[ref]['counts'][x] for x in pair}
num_variants = {z: data[ref]['Nvars'][z] for z in pair}
ratio_grid, sig_cells = heatmap_v2(chromosomes,
pop_counts,
num_variants, {},
frequency_range,
exclude,
p_value,
muted_dir,
tag='',
test=test_m,
output='pval')
pop_counts = {
z: pop_counts[z] / np.sum(pop_counts[z])
for z in pop_counts.keys()
}
dist_prop = pop_counts[pop1] / pop_counts[pop2]
dist_prop = np.nan_to_num(dist_prop)
grid_diffs = pop_counts[pop1] - pop_counts[pop2]
comb_stats[pair] = {
'grids': ratio_grid,
'sigs': sig_cells,
'sizes': sizes,
'batch': batch,
'diffs': grid_diffs
}
if data_freqs[ref]:
comb_stats[pair]['freqs'] = {
pop1: data_freqs[ref][pop1],
pop1: data_freqs[ref][pop2]
}
ref_combos[ref] = {
'combs': comb_dict,
'sizes': data[ref]['sizes'],
'stats': comb_stats
}
#### population size diffs per population per simulation
pop_asso = {avail[x]: recursively_default_dict() for x in categ[0]}
for av in categ[1]:
dat = [x for x in data[avail[av]]['counts'].keys() if tag_ref in x]
dat_size = [data[avail[av]]['sizes'][x] for x in dat]
ref_sim = avail[av].split(tag_ref)[0]
ref_pop = [x.split('.')[0].strip(tag_ref) for x in dat]
dat_size = [
dat_size[x] / data[ref_sim]['sizes'][ref_pop[x]]
for x in range(len(dat))
]
dat_size = [round(x, 3) for x in dat_size]
for p in range(len(dat)):
pop_asso[ref_sim][ref_pop[p]][dat_size[p]][avail[av]] = dat[p]
d = 0
### combine simulation combination and population size ranges.
for ref_sim in pop_asso.keys():
print(ref_sim)
batch = ref.split('C')[0]
for combo in ref_combos[ref_sim]['combs'].keys():
pop1, pop2 = combo
available_sizes = {
z: sorted(list(pop_asso[ref_sim][z].keys()))
for z in combo
}
#available_sizes= {
# z: [round(x / ref_combos[ref_sim]['sizes'][z], 3) for x in available_sizes[z]] for z in combo
#}
bins_dict = {
b: {
z:
[x for x in available_sizes[z] if x > b[0] and x <= b[1]]
for z in combo
}
for b in bins
}
print([len(bins_dict[b][pop1]) for b in bins])
print([len(bins_dict[b][pop2]) for b in bins])
bins_combs = {
b: [(x, y) for x in bins_dict[b][pop1]
for y in bins_dict[b][pop2]]
for b in bins
}
print([len(bins_combs[c]) for c in bins_combs.keys()])
for bend in bins_combs.keys():
ref_combos[ref_sim]['combs'][combo][bend] = []
for size_combo in bins_combs[bend]:
i, j = size_combo
for sub1 in pop_asso[ref_sim][pop1][i].keys():
for sub2 in pop_asso[ref_sim][pop2][j].keys():
ref_pair = {
sub1: pop_asso[ref_sim][pop1][i][sub1],
sub2: pop_asso[ref_sim][pop2][j][sub2]
}
sizes = [
data[x]['sizes'][g]
for x, g in ref_pair.items()
]
#
chromosomes = [ref_sim.split('.')[0].split('C')[1]]
pop_counts = {
x: data[g]['counts'][x]
for g, x in ref_pair.items()
}
num_variants = {
x: data[z]['Nvars'][x]
for z, x in ref_pair.items()
}
ratio_grid, sig_cells = heatmap_v2(chromosomes,
pop_counts,
num_variants,
{},
frequency_range,
exclude,
p_value,
muted_dir,
tag='',
test=test_m,
output='pval')
pop_counts = {
z: s / np.sum(s)
for z, s in pop_counts.items()
}
dist_prop = pop_counts[
ref_pair[sub1]] / pop_counts[ref_pair[sub2]]
dist_prop = np.nan_to_num(dist_prop)
grid_diffs = pop_counts[
ref_pair[sub1]] - pop_counts[ref_pair[sub2]]
comb_stats = {
'grids': ratio_grid,
'sigs': sig_cells,
'sizes': sizes,
'batch': batch,
'diffs': grid_diffs
}
if data_freqs:
comb_stats['freqs'] = {
pop1: data_freqs[sub1][ref_pair[sub1]],
pop2: data_freqs[sub2][ref_pair[sub2]]
}
ref_combos[ref_sim]['combs'][pair][bend].append(
comb_stats)
return pop_asso, ref_combos