diff --git a/cytetype/main.py b/cytetype/main.py
index 55b577a..408f556 100644
--- a/cytetype/main.py
+++ b/cytetype/main.py
@@ -21,7 +21,10 @@
     aggregate_cluster_metadata,
     extract_visualization_coordinates,
 )
-from .preprocessing.validation import materialize_canonical_gene_symbols_column
+from .preprocessing.validation import (
+    materialize_canonical_gene_symbols_column,
+    _generate_unique_na_label,
+)
 from .core.payload import build_annotation_payload, save_query_to_file
 from .core.artifacts import (
     _is_integer_valued,
@@ -87,6 +90,7 @@ def __init__(
         max_metadata_categories: int = 500,
         api_url: str = "https://prod.cytetype.nygen.io",
         auth_token: str | None = None,
+        label_na: bool = False,
     ) -> None:
         """Initialize CyteType with AnnData object and perform data preparation.
 
@@ -125,6 +129,11 @@ def __init__(
                 deployment. Defaults to "https://prod.cytetype.nygen.io".
             auth_token (str | None, optional): Bearer token for API authentication. If provided,
                 will be included in the Authorization header as "Bearer {auth_token}". Defaults to None.
+            label_na (bool, optional): If True, cells with NaN values in the
+                ``group_key`` column are assigned an ``'Unknown'`` cluster label
+                (or ``'Unknown 2'``, etc. if that label already exists). The original
+                AnnData object is not modified. If False (default), a ``ValueError``
+                is raised instead.
 
         Raises:
             KeyError: If the required keys are missing in `adata.obs` or `adata.uns`
@@ -152,8 +161,40 @@ def __init__(
             self._original_gene_symbols_column = self.gene_symbols_column
 
             self.coordinates_key = validate_adata(
-                adata, group_key, rank_key, self.gene_symbols_column, coordinates_key
+                adata, group_key, rank_key, self.gene_symbols_column, coordinates_key,
+                label_na=label_na,
             )
+
+            if label_na:
+                nan_mask = adata.obs[group_key].isna()
+                if nan_mask.any():
+                    n_nan = int(nan_mask.sum())
+                    pct = round(100 * n_nan / adata.n_obs, 1)
+                    existing_labels = set(
+                        str(v) for v in adata.obs[group_key].dropna().unique()
+                    )
+                    na_label = _generate_unique_na_label(existing_labels)
+                    logger.warning(
+                        f"⚠️  Relabeling {n_nan} cells ({pct}%) with NaN values "
+                        f"in '{group_key}' as '{na_label}'."
+                    )
+                    adata = anndata.AnnData(
+                        X=adata.X,
+                        obs=adata.obs.copy(),
+                        var=adata.var,
+                        uns=adata.uns,
+                        obsm=adata.obsm,
+                        varm=adata.varm,
+                        layers=adata.layers,
+                        obsp=adata.obsp,
+                        varp=adata.varp,
+                    )
+                    col = adata.obs[group_key]
+                    if hasattr(col, "cat"):
+                        col = col.cat.add_categories(na_label)
+                    adata.obs[group_key] = col.fillna(na_label)
+                    self.adata = adata
+
             (
                 self.gene_symbols_column,
                 self._original_gene_symbols_column,
diff --git a/cytetype/preprocessing/validation.py b/cytetype/preprocessing/validation.py
index bbede86..c785daf 100644
--- a/cytetype/preprocessing/validation.py
+++ b/cytetype/preprocessing/validation.py
@@ -266,15 +266,43 @@ def _ur_sort_key(ur: float) -> float:
     return None
 
 
+def _generate_unique_na_label(existing_labels: set[str]) -> str:
+    label = "Unknown"
+    if label not in existing_labels:
+        return label
+    n = 2
+    while f"{label} {n}" in existing_labels:
+        n += 1
+    return f"{label} {n}"
+
+
 def validate_adata(
     adata: anndata.AnnData,
     cell_group_key: str,
     rank_genes_key: str,
     gene_symbols_col: str | None,
     coordinates_key: str,
+    label_na: bool = False,
 ) -> str | None:
     if cell_group_key not in adata.obs:
         raise KeyError(f"Cell group key '{cell_group_key}' not found in `adata.obs`.")
+
+    nan_mask = adata.obs[cell_group_key].isna()
+    n_nan = int(nan_mask.sum())
+    if n_nan > 0:
+        pct = round(100 * n_nan / adata.n_obs, 1)
+        if n_nan == adata.n_obs:
+            raise ValueError(
+                f"All {n_nan} cells have NaN values in '{cell_group_key}'. "
+                f"Cannot proceed with annotation."
+            )
+        if not label_na:
+            raise ValueError(
+                f"{n_nan} cells ({pct}%) have NaN values in '{cell_group_key}'. "
+                f"Either fix the data or set label_na=True to assign these cells "
+                f"an 'Unknown' cluster label."
+            )
+
     if adata.X is None:
         raise ValueError(
             "`adata.X` is required for ranking genes. Please ensure it contains log1p normalized data."