Change API functions to take query sequences of unphased genotypes

lshmm/api.py

@@ -41,16 +41,21 @@ def check_inputs(
     The reference panel and query are arrays of size (m, n) and (k, m), respectively,
     where:
         m = number of sites.
-        n = number of samples in the reference panel (haplotypes, not individuals).
-        k = number of samples in the query (haplotypes, not individuals).
+        n = number of haplotypes (not individuals) in the reference panel.
+        k = number of haploid or diploid individuals in the query.
 
-    TODO: Support running on multiple queries. Currently, only k = 1 or 2 is supported.
+    In the haploid case, queries are (phased) haplotypes and can have multiallelic sites.
+
+    In the diploid case queries are unphased genotypes (encoded as allele dosages).
+    Currently, only biallelic sites are supported.
+
+    TODO: Support running on multiple queries. Currently, only k = 1 is supported.
 
     The mutation rate can be scaled according to the set of alleles
     that can be mutated to based on the number of distinct alleles at each site.
 
-    :param numpy.ndarray reference_panel: A panel of reference sequences.
-    :param numpy.ndarray query: A query sequence.
+    :param numpy.ndarray reference_panel: A panel of reference haplotypes.
+    :param numpy.ndarray query: A query (a haplotype or a sequence of allelic dosages).
     :param numpy.ndarray ploidy: Ploidy (only 1 or 2 are supported).
     :param numpy.ndarray prob_recombination: Recombination probability.
     :param numpy.ndarray prob_mutation: Mutation probability.
@@ -74,14 +79,14 @@ def check_inputs(
 
     if ploidy == 2:
         if not np.all(np.isin(reference_panel, [0, 1, core.NONCOPY])):
-            err_msg = "Reference panel has illegal alleles. "
-            err_msg += "Only 0/1 encoding is supported in diploid mode."
+            err_msg = "Reference panel has not allowed in diploid mode. "
+            err_msg += "Only 0/1 biallelic encoding is supported."
             raise ValueError(err_msg)
 
     num_sites, num_ref_haps = reference_panel.shape
 
     # Check the queries.
-    if query.shape[0] != ploidy:
+    if query.shape[0] != 1:
         err_msg = "Query array has incorrect dimensions."
         raise ValueError(err_msg)
 
@@ -94,9 +99,9 @@ def check_inputs(
         raise ValueError(err_msg)
 
     if ploidy == 2:
-        if not np.all(np.isin(query, [0, 1, core.MISSING])):
-            err_msg = "Query has illegal alleles. "
-            err_msg += "Only 0/1 encoding is supported in diploid mode."
+        if not np.all(np.isin(query, [0, 1, 2, core.MISSING])):
+            err_msg = "Query has states not allowed in diploid mode. "
+            err_msg += "Only 0/1/2 allele dosage encoding is supported."
             raise ValueError(err_msg)
 
     # Check the recombination probability.
@@ -139,8 +144,30 @@ def check_inputs(
         err_msg = "Mutation probability is not a scalar or an array of expected length."
         raise ValueError(err_msg)
 
+    # Get the number of distinct alleles per site.
+    if ploidy == 1:
+        num_alleles = core.get_num_alleles(reference_panel, query)
+    else:
+        # TODO: This is a hack, because the ref. panel and query have different encodings.
+        # This needs to be overhauled when or before we deal with multiallelic sites.
+        # Also, this only works if we work with only biallelic sites.
+        query_unraveled = np.zeros((2, num_sites), dtype=np.int8) - np.inf
+        for i in range(num_sites):
+            if query[0, i] == 0:
+                query_unraveled[:, i] = np.array([0, 0])
+            elif query[0, i] == 1:
+                query_unraveled[:, i] = np.array([0, 1])
+            elif query[0, i] == 2:
+                query_unraveled[:, i] = np.array([1, 1])
+            else:
+                query_unraveled[:, i] = np.array([core.MISSING, core.MISSING])
+        num_alleles = core.get_num_alleles(reference_panel, query_unraveled)
+
+    if not np.all(num_alleles > 1):
+        err_msg = "Some sites have less than two distinct alleles."
+        raise ValueError(err_msg)
+
     # Calculate the emission probability matrix.
-    num_alleles = core.get_num_alleles(reference_panel, query)
     if ploidy == 1:
         emission_matrix = core.get_emission_matrix_haploid(
             mu=prob_mutation,
@@ -168,12 +195,11 @@ def check_inputs(
         ref_panel_genotypes = core.convert_haplotypes_to_phased_genotypes(
             reference_panel
         )
-        query_genotypes = core.convert_haplotypes_to_unphased_genotypes(query)
         return (
             num_ref_haps,
             num_sites,
-            ref_panel_genotypes,
-            query_genotypes,
+            ref_panel_genotypes,    # Only ref. panel is converted
+            query,
             emission_matrix,
         )
 

tests/lsbase.py

@@ -173,11 +173,13 @@ def get_examples_pars(
             # because we can now get back mutations that
             # result in the number of alleles being higher
             # than the number of alleles in the reference panel.
-            num_alleles = core.get_num_alleles(H, query)
             prob_mutation = mu
             if prob_mutation is None:
                 # Note that n is the number of haplotypes, including ancestors.
                 prob_mutation = np.zeros(m) + core.estimate_mutation_probability(n)
+
+            num_alleles = core.get_num_alleles(H, query)
+
             if ploidy == 1:
                 e = core.get_emission_matrix_haploid(
                     mu=prob_mutation,
@@ -192,6 +194,9 @@ def get_examples_pars(
                     num_alleles=num_alleles,
                     scale_mutation_rate=scale_mutation_rate,
                 )
+                # In the diploid case, query is converted to unphased genotypes.
+                query = core.convert_haplotypes_to_unphased_genotypes(query)
+
             yield n, m, H, query, e, r, mu
 
     # Prepare simple example datasets.
@@ -204,11 +209,13 @@ def get_ts_simple_n10_no_recomb(self, seed=42):
                 recombination_rate=0.0,
                 random_seed=seed,
             ),
+            rate=0.3,
             model=msprime.BinaryMutationModel(),
-            rate=0.5,
+            discrete_genome=False,
             random_seed=seed,
         )
-        assert ts.num_sites > 3
+        assert ts.num_sites > 5
+        assert ts.num_sites < 25
         return ts
 
     def get_ts_simple(self, num_samples, seed=42):
@@ -220,11 +227,13 @@ def get_ts_simple(self, num_samples, seed=42):
                 recombination_rate=2.0,
                 random_seed=seed,
             ),
-            rate=5.0,
+            rate=0.2,
             model=msprime.BinaryMutationModel(),
+            discrete_genome=False,
             random_seed=seed,
         )
         assert ts.num_sites > 5
+        assert ts.num_sites < 25
         return ts
 
     def get_ts_simple_n8_high_recomb(self, seed=42):
@@ -236,12 +245,14 @@ def get_ts_simple_n8_high_recomb(self, seed=42):
                 recombination_rate=20.0,
                 random_seed=seed,
             ),
-            rate=5.0,
+            rate=0.2,
             model=msprime.BinaryMutationModel(),
+            discrete_genome=False,
             random_seed=seed,
         )
         assert ts.num_trees > 15
         assert ts.num_sites > 5
+        assert ts.num_sites < 25
         return ts
 
     def get_ts_custom_pars(self, num_samples, seq_length, mean_r, mean_mu, seed=42):
@@ -255,6 +266,7 @@ def get_ts_custom_pars(self, num_samples, seq_length, mean_r, mean_mu, seed=42):
             ),
             rate=mean_mu,
             model=msprime.BinaryMutationModel(),
+            discrete_genome=False,
             random_seed=seed,
         )
         return ts
@@ -302,8 +314,8 @@ def get_ts_multiallelic_n8(self, seed=42):
             rate=1e-4,
             random_seed=seed,
         )
-        assert ts.num_sites > 5
         assert ts.num_trees > 15
+        assert ts.num_sites > 5
         return ts
 
     def get_ts_multiallelic_n16(self, seed=42):

tests/test_api_fb_diploid.py

@@ -17,13 +17,12 @@ def verify(self, ts, scale_mutation_rate, include_ancestors):
             include_extreme_rates=True,
         ):
             G_vs = core.convert_haplotypes_to_phased_genotypes(H_vs)
-            s = core.convert_haplotypes_to_unphased_genotypes(query)
 
             F_vs, c_vs, ll_vs = fbd.forward_ls_dip_loop(
                 n=n,
                 m=m,
                 G=G_vs,
-                s=s,
+                s=query,
                 e=e_vs,
                 r=r,
                 norm=True,
@@ -32,7 +31,7 @@ def verify(self, ts, scale_mutation_rate, include_ancestors):
                 n=n,
                 m=m,
                 G=G_vs,
-                s=s,
+                s=query,
                 e=e_vs,
                 c=c_vs,
                 r=r,
@@ -66,7 +65,7 @@ def test_ts_simple_n10_no_recomb(self, scale_mutation_rate, include_ancestors):
         ts = self.get_ts_simple_n10_no_recomb()
         self.verify(ts, scale_mutation_rate, include_ancestors)
 
-    @pytest.mark.parametrize("num_samples", [4, 8, 16])
+    @pytest.mark.parametrize("num_samples", [8, 16])
     @pytest.mark.parametrize("scale_mutation_rate", [True, False])
     @pytest.mark.parametrize("include_ancestors", [True, False])
     def test_ts_simple(self, num_samples, scale_mutation_rate, include_ancestors):
@@ -75,13 +74,13 @@ def test_ts_simple(self, num_samples, scale_mutation_rate, include_ancestors):
 
     @pytest.mark.parametrize("scale_mutation_rate", [True, False])
     @pytest.mark.parametrize("include_ancestors", [True, False])
-    def test_ts_simple_n8_high_recomb(self, scale_mutation_rate, include_ancestors):
+    def ts_simple_n8_high_recomb(self, scale_mutation_rate, include_ancestors):
         ts = self.get_ts_simple_n8_high_recomb()
         self.verify(ts, scale_mutation_rate, include_ancestors)
 
     @pytest.mark.parametrize("scale_mutation_rate", [True, False])
     @pytest.mark.parametrize("include_ancestors", [True, False])
-    def test_ts_larger(self, scale_mutation_rate, include_ancestors):
+    def ts_larger(self, scale_mutation_rate, include_ancestors):
         ts = self.get_ts_custom_pars(
             num_samples=30, seq_length=1e5, mean_r=1e-5, mean_mu=1e-5
         )

tests/test_api_vit_diploid.py

@@ -17,13 +17,12 @@ def verify(self, ts, scale_mutation_rate, include_ancestors):
             include_extreme_rates=True,
         ):
             G_vs = core.convert_haplotypes_to_phased_genotypes(H_vs)
-            s = core.convert_haplotypes_to_unphased_genotypes(query)
 
             V_vs, P_vs, ll_vs = vd.forwards_viterbi_dip_low_mem(
                 n=n,
                 m=m,
                 G=G_vs,
-                s=s,
+                s=query,
                 e=e_vs,
                 r=r,
             )
@@ -47,7 +46,7 @@ def test_ts_simple_n10_no_recomb(self, scale_mutation_rate, include_ancestors):
         ts = self.get_ts_simple_n10_no_recomb()
         self.verify(ts, scale_mutation_rate, include_ancestors)
 
-    @pytest.mark.parametrize("num_samples", [4, 8, 16])
+    @pytest.mark.parametrize("num_samples", [8, 16])
     @pytest.mark.parametrize("scale_mutation_rate", [True, False])
     @pytest.mark.parametrize("include_ancestors", [True, False])
     def test_ts_simple(self, num_samples, scale_mutation_rate, include_ancestors):

tests/test_nontree_fb_diploid.py

@@ -25,45 +25,44 @@ def verify(
             include_extreme_rates=include_extreme_rates,
         ):
             G_vs = core.convert_haplotypes_to_phased_genotypes(H_vs)
-            s = core.convert_haplotypes_to_unphased_genotypes(query)
 
-            F_vs, c_vs, ll_vs = fbd.forwards_ls_dip(n, m, G_vs, s, e_vs, r, norm=True)
-            B_vs = fbd.backwards_ls_dip(n, m, G_vs, s, e_vs, c_vs, r)
+            F_vs, c_vs, ll_vs = fbd.forwards_ls_dip(n, m, G_vs, query, e_vs, r, norm=True)
+            B_vs = fbd.backwards_ls_dip(n, m, G_vs, query, e_vs, c_vs, r)
             self.assertAllClose(np.sum(F_vs * B_vs, (1, 2)), np.ones(m))
 
             F_tmp, c_tmp, ll_tmp = fbd.forward_ls_dip_loop(
-                n, m, G_vs, s, e_vs, r, norm=True
+                n, m, G_vs, query, e_vs, r, norm=True
             )
-            B_tmp = fbd.backward_ls_dip_loop(n, m, G_vs, s, e_vs, c_tmp, r)
+            B_tmp = fbd.backward_ls_dip_loop(n, m, G_vs, query, e_vs, c_tmp, r)
             self.assertAllClose(np.sum(F_tmp * B_tmp, (1, 2)), np.ones(m))
             self.assertAllClose(ll_vs, ll_tmp)
 
             if not normalise:
                 F_tmp, c_tmp, ll_tmp = fbd.forwards_ls_dip(
-                    n, m, G_vs, s, e_vs, r, norm=False
+                    n, m, G_vs, query, e_vs, r, norm=False
                 )
                 if ll_tmp != -np.inf:
-                    B_tmp = fbd.backwards_ls_dip(n, m, G_vs, s, e_vs, c_tmp, r)
+                    B_tmp = fbd.backwards_ls_dip(n, m, G_vs, query, e_vs, c_tmp, r)
                     self.assertAllClose(
                         np.log10(np.sum(F_tmp * B_tmp, (1, 2))), ll_tmp * np.ones(m)
                     )
                     self.assertAllClose(ll_vs, ll_tmp)
 
                 F_tmp, c_tmp, ll_tmp = fbd.forward_ls_dip_loop(
-                    n, m, G_vs, s, e_vs, r, norm=False
+                    n, m, G_vs, query, e_vs, r, norm=False
                 )
                 if ll_tmp != -np.inf:
-                    B_tmp = fbd.backward_ls_dip_loop(n, m, G_vs, s, e_vs, c_tmp, r)
+                    B_tmp = fbd.backward_ls_dip_loop(n, m, G_vs, query, e_vs, c_tmp, r)
                     self.assertAllClose(
                         np.log10(np.sum(F_tmp * B_tmp, (1, 2))), ll_tmp * np.ones(m)
                     )
                     self.assertAllClose(ll_vs, ll_tmp)
 
                 F_tmp, ll_tmp = fbd.forward_ls_dip_starting_point(
-                    n, m, G_vs, s, e_vs, r
+                    n, m, G_vs, query, e_vs, r
                 )
                 if ll_tmp != -np.inf:
-                    B_tmp = fbd.backward_ls_dip_starting_point(n, m, G_vs, s, e_vs, r)
+                    B_tmp = fbd.backward_ls_dip_starting_point(n, m, G_vs, query, e_vs, r)
                     self.assertAllClose(
                         np.log10(np.sum(F_tmp * B_tmp, (1, 2))), ll_tmp * np.ones(m)
                     )
@@ -87,7 +86,7 @@ def test_ts_simple_n10_no_recomb(
             include_extreme_rates=include_extreme_rates,
         )
 
-    @pytest.mark.parametrize("num_samples", [4, 8, 16])
+    @pytest.mark.parametrize("num_samples", [8, 16])
     @pytest.mark.parametrize("scale_mutation_rate", [True, False])
     @pytest.mark.parametrize("include_ancestors", [True, False])
     @pytest.mark.parametrize("normalise", [True, False])