diff --git a/setup.cfg b/setup.cfg
index de5b72d4..5d7d6c19 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,6 +1,6 @@
 [metadata]
 name = cell2location
-version = 0.1.4
+version = 0.1.5
 description = cell2location: High-throughput spatial mapping of cell types
 long_description = file: README.md
 long_description_content_type = text/markdown
diff --git a/tests/test_cell2location.py b/tests/test_cell2location.py
index f762c3f7..bd2c9a1c 100644
--- a/tests/test_cell2location.py
+++ b/tests/test_cell2location.py
@@ -1,4 +1,5 @@
 import numpy as np
+import pytest
 import torch
 from pyro.infer.autoguide import AutoHierarchicalNormalMessenger
 from scvi.data import synthetic_iid
@@ -319,3 +320,85 @@ def test_cell2location():
             sample_key="batch",
         )
     melt_signal_target_data_frame(weighted_avg_dict, distance_bins)
+
+
+@pytest.mark.parametrize("sliding_window_size", [0, 4])
+@pytest.mark.parametrize("use_distance_function_prior_on_w_sf", [True, False])
+@pytest.mark.parametrize("use_distance_function_effect_on_w_sf", [True, False])
+@pytest.mark.parametrize("use_aggregated_w_sf", [False])
+@pytest.mark.parametrize("amortised", [True, False])
+@pytest.mark.parametrize("amortised_sliding_window_size", [0, 4])
+def test_cell2location_with_aggregation(
+    sliding_window_size,
+    use_distance_function_prior_on_w_sf,
+    use_distance_function_effect_on_w_sf,
+    use_aggregated_w_sf,
+    amortised,
+    amortised_sliding_window_size,
+):
+    save_path = "./cell2location_model_test"
+    if torch.cuda.is_available():
+        accelerator = "gpu"
+    else:
+        accelerator = "cpu"
+    dataset = synthetic_iid(n_labels=5)
+    dataset.obsm["X_spatial"] = np.random.normal(0, 1, [dataset.n_obs, 2])
+    RegressionModel.setup_anndata(dataset, labels_key="labels", batch_key="batch")
+
+    # train regression model to get signatures of cell types
+    sc_model = RegressionModel(dataset)
+    # test minibatch training
+    sc_model.train(max_epochs=1, batch_size=100, accelerator=accelerator)
+    # export the estimated cell abundance (summary of the posterior distribution)
+    dataset = sc_model.export_posterior(dataset, sample_kwargs={"num_samples": 10})
+    # test quantile export
+    export_posterior_sc(sc_model, dataset)
+    sc_model.plot_QC(summary_name="q05")
+    # export estimated expression in each cluster
+    if "means_per_cluster_mu_fg" in dataset.varm.keys():
+        inf_aver = dataset.varm["means_per_cluster_mu_fg"][
+            [f"means_per_cluster_mu_fg_{i}" for i in dataset.uns["mod"]["factor_names"]]
+        ].copy()
+    else:
+        inf_aver = dataset.var[[f"means_per_cluster_mu_fg_{i}" for i in dataset.uns["mod"]["factor_names"]]].copy()
+    inf_aver.columns = dataset.uns["mod"]["factor_names"]
+    ### test cell2location model with convolutions ###
+    use_distance_fun = use_distance_function_prior_on_w_sf or use_distance_function_effect_on_w_sf
+    Cell2location.setup_anndata(
+        dataset,
+        batch_key="batch",
+        position_key=None if not use_distance_fun else "X_spatial",
+    )
+    ##  full data  ##
+    st_model = Cell2location(
+        dataset,
+        cell_state_df=inf_aver,
+        N_cells_per_location=30,
+        detection_alpha=200,
+        average_distance_prior=5.0,
+        sliding_window_size=sliding_window_size,
+        image_size=[20, 20],
+        use_distance_function_prior_on_w_sf=use_distance_function_prior_on_w_sf,
+        use_distance_function_effect_on_w_sf=use_distance_function_effect_on_w_sf,
+        use_aggregated_w_sf=use_aggregated_w_sf,
+        amortised=amortised,
+        encoder_mode="multiple",
+        amortised_sliding_window_size=amortised_sliding_window_size,
+    )
+    shuffle = False if (sliding_window_size > 0) or (amortised_sliding_window_size > 0) else True
+    # test full data training
+    st_model.train(
+        max_epochs=1,
+        accelerator=accelerator,
+        shuffle_set_split=shuffle,
+        # datasplitter_kwargs={"shuffle": shuffle, "shuffle_set_split": shuffle},
+    )
+    # test save/load
+    st_model.save(save_path, overwrite=True, save_anndata=True)
+    st_model = Cell2location.load(save_path)
+    # export the estimated cell abundance (summary of the posterior distribution)
+    # full data
+    if not use_distance_fun:
+        dataset = st_model.export_posterior(
+            dataset, sample_kwargs={"num_samples": 10, "batch_size": st_model.adata.n_obs}
+        )