def flip_measure_regression(self, i, snp, min_clique_size=3, small_clique=10):
'''Suggested by Oren.
Flip measure for sample i at a snp. This is the separation angle between the regression slopes of
the kinship coefficient of clique-father vs. the kinship coefficient of clique-mother for each
of the two cliques. The angle is scaled to [-1,1].'''
c0, c1 = self.ibd_clique_sample_ids(i, snp, 0), self.ibd_clique_sample_ids(i, snp, 1)
if min(c0.shape[0], c1.shape[0]) < min_clique_size:
sep, k, line, c = None, None, None, None
else:
c = [c0, c1]
parents = [self.ped.sample_id[self.ped.graph.predecessors(i)[a]] for a in im.constants.ALLELES]
K = lambda k, l: np.array([self.params.kinship(parents[k], x) for x in c[l]])
# Perform least-squares fit of both cliques
# k_threshold = Aligner._K_THRESHOLD
prob = [0.25, 0.75] # Quantile region to include in fit
k00 = K(0, 0)
k10 = K(1, 0)
# good = (k00 <= k_threshold) | (k10 <= k_threshold)
m0, m1 = mquantiles(k00, prob=prob), mquantiles(k10, prob=prob)
good = (k00 >= m0[0]) & (k00 <= m0[1]) & (k10 >= m1[0]) & (k10 <= m1[1]) if len(c0) >= small_clique else np.arange(len(c0))
a0, b0 = linefit(k00[good], k10[good], 1)
# print len(c0), np.where(good)[0]
k01 = K(0, 1)
k11 = K(1, 1)
# good = (k01 <= k_threshold) | (k11 <= k_threshold)
m0, m1 = mquantiles(k01, prob=prob), mquantiles(k11, prob=prob)
good = (k01 >= m0[0]) & (k01 <= m0[1]) & (k11 >= m1[0]) & (k11 <= m1[1]) if len(c1) >= small_clique else np.arange(len(c1))
a1, b1 = linefit(k01[good], k11[good], 1)
# print len(c1), np.where(good)[0]
k = (k00, k10, k01, k11)
line = (a0, b0, a1, b1)
# Calculate the separation angle
if np.abs(a0 - a1) < 1e-15:
sep = 0
else:
e = (b1 - b0) / (a0 - a1)
p, q = e, a0 * e + b0
x0 = np.sign(a0 + b0)
sep = max(-1, min(1, ANGLE_SCALING_FACTOR * (np.arctan2(a0 * x0 + b0 - q, x0 - p) - np.arctan2(a1 + b1 - q, 1 - p))))
return sep, (len(c0), len(c1)), (k, line, c)
def test_linefit(self): """Test the outlier-resistant line fitter.""" x = numpy.linspace(-10, 10, 256) y = 1.34*x + 0.56 cc = robust.linefit(x, y) self.assertAlmostEqual(cc[0], 1.34, 2) self.assertAlmostEqual(cc[1], 0.56, 2) cc = robust.linefit(x, y, Bisector=True) self.assertAlmostEqual(cc[0], 1.34, 2) self.assertAlmostEqual(cc[1], 0.56, 2) x = numpy.linspace(-10, 10, 2048) y = 2.86*x - 0.56 cc = robust.linefit(x, y) self.assertAlmostEqual(cc[0], 2.86, 2) self.assertAlmostEqual(cc[1], -0.56, 2) cc = robust.linefit(x, y, Bisector=True) self.assertAlmostEqual(cc[0], 2.86, 2) self.assertAlmostEqual(cc[1], -0.56, 2)
def flip_measure_regression(self,
i,
snp,
min_clique_size=3,
small_clique=10):
'''Suggested by Oren.
Flip measure for sample i at a snp. This is the separation angle between the regression slopes of
the kinship coefficient of clique-father vs. the kinship coefficient of clique-mother for each
of the two cliques. The angle is scaled to [-1,1].'''
c0, c1 = self.ibd_clique_sample_ids(i, snp,
0), self.ibd_clique_sample_ids(
i, snp, 1)
if min(c0.shape[0], c1.shape[0]) < min_clique_size:
sep, k, line, c = None, None, None, None
else:
c = [c0, c1]
parents = [
self.ped.sample_id[self.ped.graph.predecessors(i)[a]]
for a in im.constants.ALLELES
]
K = lambda k, l: np.array(
[self.params.kinship(parents[k], x) for x in c[l]])
# Perform least-squares fit of both cliques
# k_threshold = Aligner._K_THRESHOLD
prob = [0.25, 0.75] # Quantile region to include in fit
k00 = K(0, 0)
k10 = K(1, 0)
# good = (k00 <= k_threshold) | (k10 <= k_threshold)
m0, m1 = mquantiles(k00, prob=prob), mquantiles(k10, prob=prob)
good = (k00 >= m0[0]) & (k00 <= m0[1]) & (k10 >= m1[0]) & (
k10 <= m1[1]) if len(c0) >= small_clique else np.arange(
len(c0))
a0, b0 = linefit(k00[good], k10[good], 1)
# print len(c0), np.where(good)[0]
k01 = K(0, 1)
k11 = K(1, 1)
# good = (k01 <= k_threshold) | (k11 <= k_threshold)
m0, m1 = mquantiles(k01, prob=prob), mquantiles(k11, prob=prob)
good = (k01 >= m0[0]) & (k01 <= m0[1]) & (k11 >= m1[0]) & (
k11 <= m1[1]) if len(c1) >= small_clique else np.arange(
len(c1))
a1, b1 = linefit(k01[good], k11[good], 1)
# print len(c1), np.where(good)[0]
k = (k00, k10, k01, k11)
line = (a0, b0, a1, b1)
# Calculate the separation angle
if np.abs(a0 - a1) < 1e-15:
sep = 0
else:
e = (b1 - b0) / (a0 - a1)
p, q = e, a0 * e + b0
x0 = np.sign(a0 + b0)
sep = max(
-1,
min(
1,
ANGLE_SCALING_FACTOR *
(np.arctan2(a0 * x0 + b0 - q, x0 - p) -
np.arctan2(a1 + b1 - q, 1 - p))))
return sep, (len(c0), len(c1)), (k, line, c)
