Add census_data and census_spatial collections

tools/cellxgene_census_builder/src/cellxgene_census_builder/build_soma/build_soma.py

@@ -25,6 +25,7 @@
 from .globals import (
     CENSUS_DATA_NAME,
     CENSUS_INFO_NAME,
+    CENSUS_SPATIAL_NAME,
     SOMA_TileDB_Context,
 )
 from .manifest import load_manifest
@@ -153,7 +154,7 @@ def populate_root_collection(root_collection: soma.Collection) -> soma.Collectio
     root_collection.metadata["git_commit_sha"] = sha
 
     # Create sub-collections for experiments, etc.
-    for n in [CENSUS_INFO_NAME, CENSUS_DATA_NAME]:
+    for n in [CENSUS_INFO_NAME, CENSUS_DATA_NAME, CENSUS_SPATIAL_NAME]:
         root_collection.add_new_collection(n)
 
     return root_collection
@@ -198,7 +199,14 @@ def build_step2_create_root_collection(soma_path: str, experiment_builders: list
         populate_root_collection(root_collection)
 
         for e in experiment_builders:
-            e.create(census_data=root_collection[CENSUS_DATA_NAME])
+            # TODO (spatial): Confirm the decision that we are clearly separating
+            # experiments containing spatial assays from experiments not containing
+            # spatial assays. That is, an experiment should never contain assays from
+            # spatial and non-spatial modalities
+            if e.specification.is_exclusively_spatial():
+                e.create(census_data=root_collection[CENSUS_SPATIAL_NAME])
+            else:
+                e.create(census_data=root_collection[CENSUS_DATA_NAME])
 
         logger.info("Build step 2 - Create root collection - finished")
         return root_collection

tools/cellxgene_census_builder/src/cellxgene_census_builder/build_soma/experiment_builder.py

@@ -25,6 +25,7 @@
 from .anndata import AnnDataFilterSpec, AnnDataProxy, open_anndata
 from .datasets import Dataset
 from .globals import (
+    ALLOWED_SPATIAL_ASSAYS,
     CENSUS_OBS_PLATFORM_CONFIG,
     CENSUS_OBS_TABLE_SPEC,
     CENSUS_VAR_PLATFORM_CONFIG,
@@ -109,6 +110,10 @@ def create(
         """Factory method. Do not instantiate the class directly."""
         return cls(name, label, anndata_cell_filter_spec, organism_ontology_term_id)
 
+    def is_exclusively_spatial(self) -> bool:
+        """Returns True if the experiment specification EXCLUSIVELY involves spatial assays."""
+        return self.anndata_cell_filter_spec["assay_ontology_term_ids"] == ALLOWED_SPATIAL_ASSAYS
+
 
 class ExperimentBuilder:
     """Class that embodies the operators and state to build an Experiment.
@@ -143,7 +148,7 @@ def anndata_cell_filter_spec(self) -> AnnDataFilterSpec:
         return self.specification.anndata_cell_filter_spec
 
     def create(self, census_data: soma.Collection) -> None:
-        """Create experiment within the specified Collection with a single Measurement."""
+        """Create experiment within the specified Collection."""
         logger.info(f"{self.name}: create experiment at {urlcat(census_data.uri, self.name)}")
 
         self.experiment = census_data.add_new_collection(self.name, soma.Experiment)
@@ -155,6 +160,10 @@ def create(self, census_data: soma.Collection) -> None:
         # make measurement and add to ms collection
         ms.add_new_collection(MEASUREMENT_RNA_NAME, soma.Measurement)
 
+        # create `spatial`
+        if self.specification.is_exclusively_spatial():
+            self.experiment.add_new_collection("spatial")
+
     def write_obs_dataframe(self) -> None:
         logger.info(f"{self.name}: writing obs dataframe")
         assert self.experiment is not None
@@ -661,7 +670,7 @@ def read_and_dispatch_partial_h5ad(
             if d.dataset_id in eb.dataset_obs_joinid_start
             for chunk in range(0, eb.dataset_n_obs[d.dataset_id], REDUCE_X_MAJOR_ROW_STRIDE)
         ]
-        per_eb_results[eb.name] = (
+        per_eb_results[eb.experiment_uri] = (
             dask.bag.from_sequence(read_file_chunks)
             .starmap(read_and_dispatch_partial_h5ad, global_var_joinids=global_var_joinids)
             .foldby("dataset_id", reduce_X_stats_binop)
@@ -685,12 +694,12 @@ def populate_X_layers(
     per_eb_results = _reduce_X_matrices(assets_path, datasets, experiment_builders)
 
     for eb in experiment_builders:
-        if eb.name not in per_eb_results:
+        if eb.experiment_uri not in per_eb_results:
             continue
 
         # add per-dataset stats to each per-dataset XReduction
         eb_result: list[XReduction] = []
-        for dataset_id, xreduction in per_eb_results[eb.name]:
+        for dataset_id, xreduction in per_eb_results[eb.experiment_uri]:
             assert dataset_id == xreduction["dataset_id"]
             d = datasets_by_id[dataset_id]
             eb_result.extend(

tools/cellxgene_census_builder/src/cellxgene_census_builder/build_soma/experiment_specs.py

@@ -1,7 +1,7 @@
 import functools
 
 from .experiment_builder import ExperimentBuilder, ExperimentSpecification
-from .globals import RNA_SEQ
+from .globals import ALLOWED_SPATIAL_ASSAYS, RNA_SEQ
 
 
 @functools.cache
@@ -30,6 +30,25 @@ def make_experiment_specs() -> list[ExperimentSpecification]:
             },
             organism_ontology_term_id="NCBITaxon:10090",
         ),
+        # Experiments for spatial assays
+        ExperimentSpecification.create(
+            name="homo_sapiens",
+            label="Homo sapiens",
+            anndata_cell_filter_spec={
+                "organism_ontology_term_id": "NCBITaxon:9606",
+                "assay_ontology_term_ids": ALLOWED_SPATIAL_ASSAYS,
+            },
+            organism_ontology_term_id="NCBITaxon:9606",
+        ),
+        ExperimentSpecification.create(
+            name="mus_musculus",
+            label="Mus musculus",
+            anndata_cell_filter_spec={
+                "organism_ontology_term_id": "NCBITaxon:10090",
+                "assay_ontology_term_ids": ALLOWED_SPATIAL_ASSAYS,
+            },
+            organism_ontology_term_id="NCBITaxon:10090",
+        ),
     ]
 
 

tools/cellxgene_census_builder/src/cellxgene_census_builder/build_soma/globals.py

@@ -52,6 +52,9 @@
 # top-level SOMA collection
 CENSUS_DATA_NAME = "census_data"
 
+# top-level SOMA collection
+CENSUS_SPATIAL_NAME = "census_spatial"
+
 # "census_info"/"summary_cell_counts" SOMA Dataframe
 CENSUS_SUMMARY_CELL_COUNTS_NAME = "summary_cell_counts"  # object name
 
@@ -329,7 +332,6 @@
     "EFO:0010713",  # 10x immune profiling
     "EFO:0010714",  # 10x TCR enrichment
     "EFO:0010715",  # 10x Ig enrichment
-    "EFO:0010961",  # Visium Spatial Gene Expression
     "EFO:0010964",  # barcoded plate-based single cell RNA-seq
     "EFO:0011025",  # 10x 5' v1
     "EFO:0022396",  # TruSeq
@@ -353,6 +355,12 @@
     "EFO:0700016",  # Smart-seq v4
 ]
 
+# list of EFO terms that correspond to SPATIAL modality/measurement. These terms
+# define the inclusive filter applied to obs.assay_ontology_term_id. All other
+ALLOWED_SPATIAL_ASSAYS = [
+    "EFO:0010961",  # Visium Spatial Gene Expression
+]
+
 # Full-gene assays have special handling in the "normalized" X layers
 FULL_GENE_ASSAY = [
     "EFO:0003755",  # FL-cDNA

tools/cellxgene_census_builder/src/cellxgene_census_builder/build_soma/validate_soma.py

@@ -38,6 +38,7 @@
     CENSUS_OBS_STATS_COLUMNS,
     CENSUS_OBS_TABLE_SPEC,
     CENSUS_SCHEMA_VERSION,
+    CENSUS_SPATIAL_NAME,
     CENSUS_SUMMARY_CELL_COUNTS_NAME,
     CENSUS_SUMMARY_CELL_COUNTS_TABLE_SPEC,
     CENSUS_SUMMARY_NAME,
@@ -82,9 +83,18 @@ def assert_all(__iterable: Iterable[object]) -> bool:
     return r
 
 
+def get_census_data_collection_name(eb: ExperimentSpecification) -> str:
+    return CENSUS_SPATIAL_NAME if eb.is_exclusively_spatial() else CENSUS_DATA_NAME
+
+
+def get_experiment_uri(base_uri: str, eb: ExperimentSpecification) -> str:
+    census_data_collection_name = get_census_data_collection_name(eb)
+    return urlcat(base_uri, census_data_collection_name, eb.name)
+
+
 def open_experiment(base_uri: str, eb: ExperimentSpecification) -> soma.Experiment:
     """Helper function that knows the Census schema path conventions."""
-    return soma.Experiment.open(urlcat(base_uri, CENSUS_DATA_NAME, eb.name), mode="r")
+    return soma.Experiment.open(get_experiment_uri(base_uri, eb), mode="r")
 
 
 def get_experiment_shape(base_uri: str, specs: list[ExperimentSpecification]) -> dict[str, tuple[int, int]]:
@@ -230,9 +240,10 @@ def validate_axis_dataframes_global_ids(
                 .concat()
                 .to_pandas()
             )
-            assert eb_info[eb.name].n_obs == len(census_obs_df) == exp.obs.count
-            assert (len(census_obs_df) == 0) or (census_obs_df.soma_joinid.max() + 1 == eb_info[eb.name].n_obs)
-            assert eb_info[eb.name].dataset_ids == set(census_obs_df.dataset_id.unique())
+            eb_info_key = get_experiment_uri(soma_path, eb)
+            assert eb_info[eb_info_key].n_obs == len(census_obs_df) == exp.obs.count
+            assert (len(census_obs_df) == 0) or (census_obs_df.soma_joinid.max() + 1 == eb_info[eb_info_key].n_obs)
+            assert eb_info[eb_info_key].dataset_ids == set(census_obs_df.dataset_id.unique())
 
             # Validate that all obs soma_joinids are unique and in the range [0, n).
             obs_unique_joinids = np.unique(census_obs_df.soma_joinid.to_numpy())
@@ -254,13 +265,13 @@ def validate_axis_dataframes_global_ids(
             del census_obs_df, obs_unique_joinids
 
             # var
-            n_vars = len(eb_info[eb.name].vars)
+            n_vars = len(eb_info[eb_info_key].vars)
 
             census_var_df = (
                 exp.ms[MEASUREMENT_RNA_NAME].var.read(column_names=["feature_id", "soma_joinid"]).concat().to_pandas()
             )
             assert n_vars == len(census_var_df) == exp.ms[MEASUREMENT_RNA_NAME].var.count
-            assert eb_info[eb.name].vars == set(census_var_df.feature_id.array)
+            assert eb_info[eb_info_key].vars == set(census_var_df.feature_id.array)
             assert (len(census_var_df) == 0) or (census_var_df.soma_joinid.max() + 1 == n_vars)
 
             # Validate that all var soma_joinids are unique and in the range [0, n).
@@ -289,7 +300,7 @@ def _validate_axis_dataframes(
         eb_info: dict[str, EbInfo] = {}
         for eb in experiment_specifications:
             with soma.Collection.open(soma_path, context=SOMA_TileDB_Context()) as census:
-                census_data = census[CENSUS_DATA_NAME]
+                census_data_collection = census[get_census_data_collection_name(eb)]
                 dataset_id = dataset.dataset_id
                 ad = open_anndata(
                     dataset,
@@ -298,8 +309,16 @@ def _validate_axis_dataframes(
                     var_column_names=CXG_VAR_COLUMNS_READ,
                     filter_spec=eb.anndata_cell_filter_spec,
                 )
-                eb_info[eb.name] = EbInfo()
-                se = census_data[eb.name]
+                se = census_data_collection[eb.name]
+
+                # NOTE: Since we are validating data for each experiment, we
+                # use the experiment uri as the key for the data that must be validated.
+                # Using just the experiment spec name would cause collisions as in the case
+                # of spatial and non-spatial experiments with the same name (experiment spec name)
+                # but stored under different census root collections
+                eb_info_key = get_experiment_uri(soma_path, eb)
+                eb_info[eb_info_key] = EbInfo()
+
                 dataset_obs = (
                     se.obs.read(
                         column_names=list(CENSUS_OBS_TABLE_SPEC.field_names()),
@@ -326,11 +345,13 @@ def _validate_axis_dataframes(
                     if isinstance(dataset_obs[key].dtype, pd.CategoricalDtype):
                         dataset_obs[key] = dataset_obs[key].astype(dataset_obs[key].cat.categories.dtype)
 
-                assert len(dataset_obs) == len(ad.obs), f"{dataset.dataset_id}/{eb.name} obs length mismatch"
+                assert (
+                    len(dataset_obs) == len(ad.obs)
+                ), f"{dataset.dataset_id}/{eb.name} obs length mismatch soma experiment obs len: {len(dataset_obs)} != anndata obs len: {len(ad.obs)}"
                 if ad.n_obs > 0:
-                    eb_info[eb.name].n_obs += ad.n_obs
-                    eb_info[eb.name].dataset_ids.add(dataset_id)
-                    eb_info[eb.name].vars |= set(ad.var.index.array)
+                    eb_info[eb_info_key].n_obs += ad.n_obs
+                    eb_info[eb_info_key].dataset_ids.add(dataset_id)
+                    eb_info[eb_info_key].vars |= set(ad.var.index.array)
                     ad_obs = ad.obs[list(set(CXG_OBS_TERM_COLUMNS) - set(CENSUS_OBS_STATS_COLUMNS))].reset_index(
                         drop=True
                     )
@@ -343,11 +364,11 @@ def _validate_axis_dataframes(
     def reduce_eb_info(results: Sequence[dict[str, EbInfo]]) -> dict[str, EbInfo]:
         eb_info = {}
         for res in results:
-            for name, info in res.items():
-                if name not in eb_info:
-                    eb_info[name] = copy.copy(info)
+            for eb_info_key, info in res.items():
+                if eb_info_key not in eb_info:
+                    eb_info[eb_info_key] = copy.copy(info)
                 else:
-                    eb_info[name].update(info)
+                    eb_info[eb_info_key].update(info)
         return eb_info
 
     eb_info = (
@@ -815,8 +836,9 @@ def validate_X_layers_schema(
         with open_experiment(soma_path, eb) as exp:
             assert soma.Collection.exists(exp.ms[MEASUREMENT_RNA_NAME].X.uri)
 
-            n_obs = eb_info[eb.name].n_obs
-            n_vars = eb_info[eb.name].n_vars
+            eb_info_key = get_experiment_uri(soma_path, eb)
+            n_obs = eb_info[eb_info_key].n_obs
+            n_vars = eb_info[eb_info_key].n_vars
             assert n_obs == exp.obs.count
             assert n_vars == exp.ms[MEASUREMENT_RNA_NAME].var.count
 
@@ -1011,8 +1033,9 @@ def get_sparse_arrays(C: soma.Collection) -> list[soma.SparseNDArray]:
     # first, confirm we set shape correctly, as the code uses it as the max bounding box
     for eb in experiment_specifications:
         with open_experiment(soma_path, eb) as exp:
-            n_obs = eb_info[eb.name].n_obs
-            n_vars = eb_info[eb.name].n_vars
+            eb_info_key = get_experiment_uri(soma_path, eb)
+            n_obs = eb_info[eb_info_key].n_obs
+            n_vars = eb_info[eb_info_key].n_vars
             for layer_name in exp.ms[MEASUREMENT_RNA_NAME].X:
                 assert exp.ms[MEASUREMENT_RNA_NAME].X[layer_name].shape == (n_obs, n_vars)
             if "feature_dataset_presence_matrix" in exp.ms[MEASUREMENT_RNA_NAME]: