def test_analyse_sample_zero(self):
''' test we raise an error if the de novo count is zero
'''
rates = WeightedChoice()
rates.add_choice(200, 1e-5, 'A', 'G')
rates.add_choice(201, 2e-5, 'C', 'T')
severity = [5, 10]
with self.assertRaises(ValueError):
analyse(rates, severity, 0, 0, iterations=10000)
def test_analyse_mismatch(self):
''' test for error when the rates and severity lengths are different
'''
rates = WeightedChoice()
rates.add_choice(200, 1e-5, 'A', 'G')
rates.add_choice(201, 2e-5, 'C', 'T')
severity = [5, 10, 5]
with self.assertRaises(ValueError):
analyse(rates, severity, 8, 1, iterations=100000)
def test_analyse(self):
''' test that we run the simulations correctly
'''
rates = WeightedChoice()
rates.add_choice(200, 1e-5, 'A', 'G')
rates.add_choice(201, 2e-5, 'C', 'T')
rates.add_choice(202, 1e-5, 'C', 'G')
severity = [5, 10, 5]
# define a test where the observed score will fall at the midpoint of
# the simulated null distribution
p = analyse(rates, severity, 8, 1, iterations=100000)
self.assertAlmostEqual(p, 0.5, places=2)
# now check when we sample two de novo mutations
p = analyse(rates, severity, 15, 2, iterations=100000)
self.assertAlmostEqual(p, 0.25, places=2)
def test_analyse_bigger(self):
''' test a more realistically sized data set
'''
seed(0)
rates = WeightedChoice()
pos = sorted(set([randint(1000, 3000) for x in range(2000)]))
for x in pos:
rates.add_choice(x, uniform(1e-10, 1e-7), 'A', 'G')
severity = [randint(0, 40) for x in pos]
p = analyse(rates, severity, 150, 4, iterations=10000)
self.assertAlmostEqual(p, 3e-4, places=2)
def analyse_gene(ensembl, mut_dict, cadd, symbol, de_novos, constraint,
weights):
''' analyse the severity of de novos found in a gene
Args:
ensembl: EnsemblRequest object, for transcript coordinates and sequence
mut_dict: list of sequence-context mutation probabilities.
cadd: pysam.TabixFile object for CADD scores (SNVs only)
symbol: HGNC symbol for current gene
de_novos: list of de novo mutations observed in current gene. Each entry
is a dict with 'position', 'ref', 'alt', and 'consequence' keys.
weights: dictionary of objects to weight CADD severity scores. We have
different weights for protein-truncating and protein-altering
variants, and within the protein-altering variants, different
weights for variants in constrained and unconstrained regions.
Returns:
p-value for the observed total severity with respect to a null
distribution of severities for the gene.
'''
sites = [x['pos'] for x in de_novos]
try:
# create gene/transcript for de novo mutations
transcripts = load_gene(ensembl, symbol, sites)
except IndexError:
return 'NA'
# get per site/allele mutation rates
rates_by_cq = get_site_sampler(transcripts, mut_dict)
chrom = transcripts[0].get_chrom()
# get per site/allele severity scores, weighted by enrichment of missense
# in known dominant at different severity thresholds
constrained = get_constrained_positions(ensembl, constraint, symbol)
severity = get_severity(cadd, chrom, rates_by_cq, weights, constrained)
# convert the rates per site per consequence to rates per site
rates = WeightedChoice()
for cq in sorted(rates_by_cq):
rates.append(rates_by_cq[cq])
# get summed score for observed de novos
observed = sum((get_severity(cadd, chrom, de_novos, weights, constrained)))
# simulate distribution of summed scores within transcript
return analyse(rates, severity, observed, len(de_novos), 1000000)
def test_analyse_extreme_p_value(self):
''' test when the observed severity score exceeds all possible values
'''
rates = WeightedChoice()
rates.add_choice(200, 1e-5, 'A', 'G')
rates.add_choice(201, 2e-5, 'C', 'T')
rates.add_choice(202, 1e-5, 'C', 'G')
severity = [5, 10, 5]
# now check when the observed severity score exceeds all possible
# values from the severity distribution. This test gives an absurd
# p-value at 1e-6, but that is because the observed value is
# unachievable given the existsing severity scores. In practice the
# observed score will always be theoretically achieveable in the null
# distribution, since the observed score is calculated from the
# existsing scores.
p = analyse(rates, severity, 20, 1, iterations=100000)
self.assertAlmostEqual(p, 1e-6, places=4)
def test_analyse_empty(self):
''' check we raise an error if the rates and severity are empty
'''
with self.assertRaises(ValueError):
analyse(WeightedChoice(), [], 8, 1, iterations=10000)