def interlocus_dependence(self, strain_names):
""" Performs a chi square test to find interval pairs whose origins are interdependent
:param strain_names: list of strain names to analyze
:return: elementary intervals, matrix of chi square values, matrix of p values (both upper triangular)
"""
combo_count_dict, intervals = self.pairwise_frequencies(strain_names)
# convert source_counts to matrix combo_counts
combo_counts = np.empty([len(intervals), len(intervals), subspecies.NUM_SUBSPECIES ** 2], dtype=np.uint16)
species_counts = np.zeros([len(intervals), subspecies.NUM_SUBSPECIES])
for i, prox_species in enumerate(subspecies.iter_subspecies()):
for j, dist_species in enumerate(subspecies.iter_subspecies()):
counts = combo_count_dict[subspecies.combine(prox_species, dist_species)]
species_counts[:, i] += np.diag(counts)
combo_counts[:, :, i * subspecies.NUM_SUBSPECIES + j] = counts
# compute expected combo frequencies from source frequencies
combo_expectations = np.zeros([len(intervals), len(intervals), subspecies.NUM_SUBSPECIES ** 2])
for i in xrange(subspecies.NUM_SUBSPECIES):
for j in xrange(subspecies.NUM_SUBSPECIES):
combo_expectations[:, :, i * subspecies.NUM_SUBSPECIES + j] += \
np.outer(species_counts[:, i], species_counts[:, j])
# normalize expectations using the actual total frequency
sums = np.sum(combo_counts, axis=2)
old_settings = np.seterr(invalid='ignore') # ignore division by 0 errors for intervals with no assigned origin
for i in xrange(subspecies.NUM_SUBSPECIES ** 2):
combo_expectations[:, :, i] = np.true_divide(combo_expectations[:, :, i], sums)
np.seterr(**old_settings)
combo_expectations = np.nan_to_num(combo_expectations)
# do chi-square test
output = []
for i in xrange(len(intervals)):
# only upper triangle is meaningful
for j in xrange(i + 1, len(intervals)):
nonzero_expectations = np.where(combo_expectations[i, j])
chi_sq, p_value = stats.chisquare(
combo_counts[i, j][nonzero_expectations], combo_expectations[i, j][nonzero_expectations])
output.append([
chi_sq,
p_value,
# proximal interval
intervals[i-1],
intervals[i],
# distal interval
intervals[j],
intervals[j-1]
])
return output
def sources_at_point_pair(self, chrom1, pos1, chrom2, pos2, strain_names):
""" Prints the range of the 2D interval and the counts of subspecific combos at 2 loci in the genome
:param chrom1: chromosome of one locus
:param pos1: position of one locus
:param chrom2: chromosome of another locus
:param pos2: position of another locus
:param strain_names: list of strain names to analyze
"""
coords = [self.genome_index(chrom1, pos1), self.genome_index(chrom2, pos2)]
mins = [0] * 2
maxes = [np.sum(self.sizes)] * 2
coords.sort()
output = {}
samples = [[[] for _ in subspecies.iter_subspecies(True)] for _ in subspecies.iter_subspecies(True)]
key = [subspecies.to_string(s) for s in subspecies.iter_subspecies(True)]
for strain_name in strain_names:
intervals = self.sample_dict[strain_name][0]
sources = self.sample_dict[strain_name][1]
# find interval containing each location
i = 0
interval_indices = [None, None]
for loc_num in xrange(2):
while intervals[i] < coords[loc_num]:
i += 1
if i > 0:
mins[loc_num] = max(mins[loc_num], intervals[i - 1])
maxes[loc_num] = min(maxes[loc_num], intervals[i])
interval_indices[loc_num] = i
samples[subspecies.to_ordinal(sources[interval_indices[0]])][
subspecies.to_ordinal(sources[interval_indices[1]])].append(strain_name)
output['Key'] = key
output['Samples'] = samples
output['Intervals'] = [
self.chrom_and_pos(mins[0], maxes[0]),
self.chrom_and_pos(mins[1], maxes[1])
]
return output
