Add create_genotype_dosage_dataset

sgkit/__init__.py

@@ -4,6 +4,7 @@
     DIM_SAMPLE,
     DIM_VARIANT,
     create_genotype_call_dataset,
+    create_genotype_dosage_dataset,
 )
 from .stats.association import gwas_linear_regression
 
@@ -13,5 +14,6 @@
     "DIM_SAMPLE",
     "DIM_VARIANT",
     "create_genotype_call_dataset",
+    "create_genotype_dosage_dataset",
     "gwas_linear_regression",
 ]

sgkit/api.py

@@ -1,5 +1,6 @@
 from typing import Any, Dict, Hashable, List
 
+import numpy as np
 import xarray as xr
 
 from .utils import check_array_like
@@ -79,3 +80,59 @@ def create_genotype_call_dataset(
         data_vars["variant/id"] = ([DIM_VARIANT], variant_id)
     attrs: Dict[Hashable, Any] = {"contigs": variant_contig_names}
     return xr.Dataset(data_vars=data_vars, attrs=attrs)
+
+
+def create_genotype_dosage_dataset(
+    *,
+    variant_contig_names: List[str],
+    variant_contig: Any,
+    variant_position: Any,
+    variant_alleles: Any,
+    sample_id: Any,
+    call_dosage: Any,
+    variant_id: Any = None,
+) -> xr.Dataset:
+    """Create a dataset of genotype calls.
+
+    Parameters
+    ----------
+    variant_contig_names : list of str
+        The contig names.
+    variant_contig : array_like, int
+        The (index of the) contig for each variant.
+    variant_position : array_like, int
+        The reference position of the variant.
+    variant_alleles : array_like, S1
+        The possible alleles for the variant.
+    sample_id : array_like, str
+        The unique identifier of the sample.
+    call_dosage : array_like, float
+        Dosages, encoded as floats, with NaN indicating a
+        missing value.
+    variant_id: array_like, str, optional
+        The unique identifier of the variant.
+
+    Returns
+    -------
+    xr.Dataset
+        The dataset of genotype calls.
+
+    """
+    check_array_like(variant_contig, kind="i", ndim=1)
+    check_array_like(variant_position, kind="i", ndim=1)
+    check_array_like(variant_alleles, kind="S", ndim=2)
+    check_array_like(sample_id, kind="U", ndim=1)
+    check_array_like(call_dosage, kind="f", ndim=2)
+    data_vars: Dict[Hashable, Any] = {
+        "variant/contig": ([DIM_VARIANT], variant_contig),
+        "variant/position": ([DIM_VARIANT], variant_position),
+        "variant/alleles": ([DIM_VARIANT, DIM_ALLELE], variant_alleles),
+        "sample/id": ([DIM_SAMPLE], sample_id),
+        "call/dosage": ([DIM_VARIANT, DIM_SAMPLE], call_dosage),
+        "call/dosage_mask": ([DIM_VARIANT, DIM_SAMPLE], np.isnan(call_dosage),),
+    }
+    if variant_id is not None:
+        check_array_like(variant_id, kind="U", ndim=1)
+        data_vars["variant/id"] = ([DIM_VARIANT], variant_id)
+    attrs: Dict[Hashable, Any] = {"contigs": variant_contig_names}
+    return xr.Dataset(data_vars=data_vars, attrs=attrs)

sgkit/tests/test_api.py

@@ -7,6 +7,7 @@
     DIM_SAMPLE,
     DIM_VARIANT,
     create_genotype_call_dataset,
+    create_genotype_dosage_dataset,
 )
 
 
@@ -48,3 +49,34 @@ def test_create_genotype_call_dataset():
     assert_array_equal(ds["call/genotype"], call_genotype)
     assert_array_equal(ds["call/genotype_mask"], call_genotype < 0)
     assert_array_equal(ds["call/genotype_phased"], call_genotype_phased)
+
+
+def test_create_genotype_dosage_dataset():
+    variant_contig_names = ["chr1"]
+    variant_contig = np.array([0, 0], dtype="i1")
+    variant_position = np.array([1000, 2000], dtype="i4")
+    variant_alleles = np.array([["A", "C"], ["G", "A"]], dtype="S1")
+    variant_id = np.array(["rs1", "rs2"], dtype=str)
+    sample_id = np.array(["sample_1", "sample_2", "sample_3"], dtype=str)
+    call_dosage = np.array([[0.8, 0.9, 1.0], [1.0, 1.1, 1.2]], dtype="f4")
+    ds = create_genotype_dosage_dataset(
+        variant_contig_names=variant_contig_names,
+        variant_contig=variant_contig,
+        variant_position=variant_position,
+        variant_alleles=variant_alleles,
+        sample_id=sample_id,
+        call_dosage=call_dosage,
+        variant_id=variant_id,
+    )
+
+    assert DIM_VARIANT in ds.dims
+    assert DIM_SAMPLE in ds.dims
+
+    assert ds.attrs["contigs"] == variant_contig_names
+    assert_array_equal(ds["variant/contig"], variant_contig)
+    assert_array_equal(ds["variant/position"], variant_position)
+    assert_array_equal(ds["variant/alleles"], variant_alleles)
+    assert_array_equal(ds["variant/id"], variant_id)
+    assert_array_equal(ds["sample/id"], sample_id)
+    assert_array_equal(ds["call/dosage"], call_dosage)
+    assert_array_equal(ds["call/dosage_mask"], np.isnan(call_dosage))