Changes related to sensitivity analysis

benchmark_utils/latent_signature_utils.py

@@ -124,9 +124,7 @@ def create_latent_signature(
                         adata_sampled = create_anndata_pseudobulk(
                             adata_sampled, pseudobulk
                         )
-                    result = model.get_latent_representation(
-                        adata_sampled, get_pseudobulk = False
-                    )
+                    result = model.get_latent_representation(adata_sampled)
                     if SIGNATURE_TYPE == "pre_encoded":
                         result = result.reshape(-1)
                     elif SIGNATURE_TYPE == "post_inference":

benchmark_utils/sanity_checks_utils.py

@@ -106,7 +106,7 @@ def run_sanity_check(
     # 2. TAPE
     if "TAPE" in baselines:
         _, deconv_results = \
-        Deconvolution(signature.T, pseudobulk_test_df,
+        Deconvolution(signature.loc[intersection].T, pseudobulk_test_df,
                   sep='\t', scaler='mms',
                   datatype='counts', genelenfile=None,
                   mode='overall', adaptive=True, variance_threshold=0.98,
@@ -118,7 +118,7 @@ def run_sanity_check(
         df_test_group_correlations.loc[:, "TAPE"] = group_correlations.values
     ## 3. Scaden
     if "Scaden" in baselines:
-        deconv_results = ScadenDeconvolution(signature.T,
+        deconv_results = ScadenDeconvolution(signature.loc[intersection].T,
                                             pseudobulk_test_df,
                                             sep='\t',
                                             batch_size=128, epochs=128)

scvi/model/_mixupvi.py

@@ -52,7 +52,7 @@ def get_latent_representation(
         self,
         adata: Optional[AnnData] = None,
         indices: Optional[Sequence[int]] = None,
-        get_pseudobulk: bool = True,
+        get_pseudobulk: bool = False,
         give_mean: bool = True,
         mc_samples: int = 5000,
         batch_size: Optional[int] = None,