@@ -611,7 +611,7 @@ def parallel_differential_expression_vec(
611611
612612 # Get unique targets efficiently
613613 obs_values = adata .obs [groupby_key ].values
614- unique_targets = np .unique (obs_values )
614+ unique_targets = np .unique (obs_values ) # type: ignore
615615
616616 if groups is not None :
617617 mask = np .isin (unique_targets , groups + [reference ])
@@ -626,13 +626,13 @@ def parallel_differential_expression_vec(
626626
627627 # Convert to dense matrix for fastest access
628628 if hasattr (adata .X , "toarray" ):
629- X = adata .X .toarray ().astype (np .float32 )
629+ X = adata .X .toarray ().astype (np .float32 ) # type: ignore
630630 else :
631631 X = np .asarray (adata .X , dtype = np .float32 )
632632
633633 # Get reference data once
634634 reference_mask = obs_values == reference
635- X_ref = X [reference_mask , :]
635+ X_ref = X [reference_mask , :] # type: ignore
636636
637637 # Compute reference means once for all genes
638638 if is_log1p :
@@ -654,11 +654,11 @@ def parallel_differential_expression_vec(
654654 target_results = _process_single_target_vectorized (
655655 target = target ,
656656 reference = reference ,
657- obs_values = obs_values ,
657+ obs_values = obs_values , # type: ignore
658658 X = X ,
659659 X_ref = X_ref ,
660660 means_ref = means_ref ,
661- gene_names = gene_names ,
661+ gene_names = gene_names , # type: ignore
662662 is_log1p = is_log1p ,
663663 exp_post_agg = exp_post_agg ,
664664 clip_value = clip_value ,
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