Implement mutation rate scaling based on the number of alleles

python/tests/beagle_numba.py

@@ -324,8 +324,14 @@ def get_transition_probs(cm, h, ne):
 
 def compute_emission_probability(mismatch_prob, is_match, *, num_alleles=2):
     """
+    Compute emission probability at a site based on whether the alleles
+    carried by a query haplotype and a reference haplotype match at the site.
+
+    The emission probability may be scaled according to the number of distinct
+    segregating alleles. By default, it is assumeed the site is biallelic.
+
     :param float mismatch_prob: Mismatch probability.
-    :param bool is_match: True if match, otherwise False.
+    :param bool is_match: True if matched, otherwise False.
     :param int num_alleles: Number of distinct alleles (default = 2).
     :return: Emission probability
     :rtype: float
@@ -337,7 +343,9 @@ def compute_emission_probability(mismatch_prob, is_match, *, num_alleles=2):
 
 
 @njit
-def compute_forward_matrix(ref_h, query_h, trans_probs, mismatch_probs):
+def compute_forward_matrix(
+    ref_h, query_h, trans_probs, mismatch_probs, *, num_alleles=2
+):
     """
     Implement Li and Stephens forward algorithm.
 
@@ -350,6 +358,7 @@ def compute_forward_matrix(ref_h, query_h, trans_probs, mismatch_probs):
     :param numpy.ndarray query_h: One query haplotype.
     :param numpy.ndarray trans_probs: Transition probabilities.
     :param numpy.ndarray mismatch_probs: Mismatch probabilities.
+    :param int num_alleles: Number of distinct alleles (default = 2).
     :return: Forward probability matrix.
     :rtype: numpy.ndarray
     """
@@ -367,7 +376,9 @@ def compute_forward_matrix(ref_h, query_h, trans_probs, mismatch_probs):
             # Get allele at genotyped position i on reference haplotype j.
             ref_a = ref_h[i, j]
             # Get emission probability.
-            em_prob = compute_emission_probability(mismatch_probs[i], query_a == ref_a)
+            em_prob = compute_emission_probability(
+                mismatch_probs[i], query_a == ref_a, num_alleles=num_alleles
+            )
             fwd_mat[i, j] = em_prob
             if i > 0:
                 fwd_mat[i, j] *= scale * fwd_mat[i - 1, j] + shift
@@ -378,7 +389,9 @@ def compute_forward_matrix(ref_h, query_h, trans_probs, mismatch_probs):
 
 
 @njit
-def compute_backward_matrix(ref_h, query_h, trans_probs, mismatch_probs):
+def compute_backward_matrix(
+    ref_h, query_h, trans_probs, mismatch_probs, *, num_alleles=2
+):
     """
     Implement Li and Stephens backward algorithm.
 
@@ -394,6 +407,7 @@ def compute_backward_matrix(ref_h, query_h, trans_probs, mismatch_probs):
     :param numpy.ndarray query_h: One query haplotype.
     :param numpy.ndarray trans_probs: Transition probabilities.
     :param numpy.ndarray mismatch_probs: Mismatch probabilities.
+    :param int num_alleles: Number of distinct alleles (default = 2).
     :return: Backward probability matrix.
     :rtype: numpy.ndarray
     """
@@ -407,7 +421,9 @@ def compute_backward_matrix(ref_h, query_h, trans_probs, mismatch_probs):
         for j in range(h):
             ref_a = ref_h[iP1, j]
             em_prob = compute_emission_probability(
-                mismatch_probs[iP1], query_a == ref_a
+                mismatch_probs[iP1],
+                query_a == ref_a,
+                num_alleles=num_alleles,
             )
             bwd_mat[iP1, j] *= em_prob
         site_sum = np.sum(bwd_mat[iP1, :])
@@ -635,6 +651,7 @@ def run_interpolation_beagle(
         "Check the reference and query haplotypes use the same allele encoding.",
         stacklevel=1,
     )
+    num_alleles = len(tskit.ALLELES_ACGT)
     h = ref_h.shape[1]  # Number of reference haplotypes.
     # Separate indices of genotyped and ungenotyped positions.
     idx_typed = np.where(query_h != tskit.MISSING_DATA)[0]
@@ -654,10 +671,18 @@ def run_interpolation_beagle(
     mismatch_probs = get_mismatch_probs(pos_typed, error_rate=error_rate)
     # Compute matrices at genotyped positions.
     fwd_mat = compute_forward_matrix(
-        ref_h_typed, query_h_typed, trans_probs, mismatch_probs
+        ref_h_typed,
+        query_h_typed,
+        trans_probs,
+        mismatch_probs,
+        num_alleles=num_alleles,
     )
     bwd_mat = compute_backward_matrix(
-        ref_h_typed, query_h_typed, trans_probs, mismatch_probs
+        ref_h_typed,
+        query_h_typed,
+        trans_probs,
+        mismatch_probs,
+        num_alleles=num_alleles,
     )
     state_mat = compute_state_prob_matrix(fwd_mat, bwd_mat)
     # Interpolate allele probabilities.