diff --git a/tools/cell_census_builder/__main__.py b/tools/cell_census_builder/__main__.py
index a81639b85..2056c36fe 100644
--- a/tools/cell_census_builder/__main__.py
+++ b/tools/cell_census_builder/__main__.py
@@ -295,7 +295,7 @@ def build_step4_populate_X_layers(
     populate_X_layers(assets_path, filtered_datasets, experiment_builders, args)
 
     for eb in reopen_experiment_builders(experiment_builders):
-        eb.populate_presence_matrix()
+        eb.populate_presence_matrix(filtered_datasets)
 
     logging.info("Build step 4 - Populate X layers - finished")
 
diff --git a/tools/cell_census_builder/experiment_builder.py b/tools/cell_census_builder/experiment_builder.py
index a3645f065..763920f24 100644
--- a/tools/cell_census_builder/experiment_builder.py
+++ b/tools/cell_census_builder/experiment_builder.py
@@ -254,7 +254,7 @@ def create_X_with_layers(self) -> None:
                     platform_config=CENSUS_X_LAYERS_PLATFORM_CONFIG[layer_name],
                 )
 
-    def populate_presence_matrix(self) -> None:
+    def populate_presence_matrix(self, datasets: List[Dataset]) -> None:
         """
         Save presence matrix per Experiment
         """
@@ -265,7 +265,7 @@ def populate_presence_matrix(self) -> None:
             # sanity check
             assert len(self.presence) == self.n_datasets
 
-            max_dataset_joinid = max(self.presence.keys())
+            max_dataset_joinid = max(d.soma_joinid for d in datasets)
 
             # LIL is fast way to create spmatrix
             pm = sparse.lil_array((max_dataset_joinid + 1, self.n_var), dtype=bool)
diff --git a/tools/cell_census_builder/tests/test_builder.py b/tools/cell_census_builder/tests/test_builder.py
index 27ec121c8..70fe53b7f 100644
--- a/tools/cell_census_builder/tests/test_builder.py
+++ b/tools/cell_census_builder/tests/test_builder.py
@@ -82,6 +82,22 @@ def test_base_builder_creation(
             # Presence matrix should exist with the correct dimensions
             for exp_name in ["homo_sapiens", "mus_musculus"]:
                 fdpm = census[CENSUS_DATA_NAME][exp_name].ms[MEASUREMENT_RNA_NAME][FEATURE_DATASET_PRESENCE_MATRIX_NAME]
+                fdpm_matrix = fdpm.read().coos().concat()
+
+                # The first dimension of the presence matrix should map to the soma_joinids of the returned datasets
+                dim_0 = fdpm_matrix.to_scipy().row
+                assert all(dim_0 >= 0)
+                assert all(dim_0 <= max(returned_datasets.soma_joinid))
+                assert fdpm_matrix.shape[0] == max(returned_datasets.soma_joinid) + 1
+
+                # All rows indexed by a Dataframe's soma_joinid that does not belong to the experiment contain all zeros
+                dense_pm = fdpm_matrix.to_scipy().todense()
+                for i, dataset in returned_datasets.iterrows():
+                    if dataset["dataset_id"].startswith(exp_name):
+                        assert np.count_nonzero(dense_pm[i]) > 0
+                    else:
+                        assert np.count_nonzero(dense_pm[i]) == 0
+
                 fdpm_df = fdpm.read().tables().concat().to_pandas()
                 n_datasets = fdpm_df["soma_dim_0"].nunique()
                 n_features = fdpm_df["soma_dim_1"].nunique()