diff --git a/ehrapy/tools/feature_ranking/_rank_features_groups.py b/ehrapy/tools/feature_ranking/_rank_features_groups.py
index 4fe8d52e..5625d95f 100644
--- a/ehrapy/tools/feature_ranking/_rank_features_groups.py
+++ b/ehrapy/tools/feature_ranking/_rank_features_groups.py
@@ -257,7 +257,7 @@ def _check_no_datetime_columns(df):
 def _get_intersection(adata_uns, key, selection):
     """Get intersection of adata_uns[key] and selection"""
     if key in adata_uns:
-        uns_enc_to_keep = list(set(adata_uns["encoded_non_numerical_columns"]) & set(selection))
+        uns_enc_to_keep = list(set(adata_uns[key]) & set(selection))
     else:
         uns_enc_to_keep = []
     return uns_enc_to_keep
@@ -351,31 +351,48 @@ def rank_features_groups(
                 minimal set of genes that are good predictors (sparse solution meaning few non-zero fitted coefficients).
 
     Returns:
-        *names*: structured `np.ndarray` (`.uns['rank_features_groups']`)
+        *names* structured `np.ndarray` (`.uns['rank_features_groups']`)
                   Structured array to be indexed by group id storing the gene
                   names. Ordered according to scores.
-        *scores*: structured `np.ndarray` (`.uns['rank_features_groups']`)
+        *scores* structured `np.ndarray` (`.uns['rank_features_groups']`)
                   Structured array to be indexed by group id storing the z-score
                   underlying the computation of a p-value for each gene for each group.
                   Ordered according to scores.
-        *logfoldchanges*: structured `np.ndarray` (`.uns['rank_features_groups']`)
+        *logfoldchanges* structured `np.ndarray` (`.uns['rank_features_groups']`)
                           Structured array to be indexed by group id storing the log2
                           fold change for each gene for each group. Ordered according to scores.
                           Only provided if method is 't-test' like.
                           Note: this is an approximation calculated from mean-log values.
-        *pvals*: structured `np.ndarray` (`.uns['rank_features_groups']`) p-values.
-        *pvals_adj* : structured `np.ndarray` (`.uns['rank_features_groups']`) Corrected p-values.
+        *pvals* structured `np.ndarray` (`.uns['rank_features_groups']`) p-values.
+        *pvals_adj* structured `np.ndarray` (`.uns['rank_features_groups']`) Corrected p-values.
         *pts*: `pandas.DataFrame` (`.uns['rank_features_groups']`)
                Fraction of cells expressing the genes for each group.
-        *pts_rest*: `pandas.DataFrame` (`.uns['rank_features_groups']`)
+        *pts_rest* `pandas.DataFrame` (`.uns['rank_features_groups']`)
                     Only if `reference` is set to `'rest'`.
                     Fraction of observations from the union of the rest of each group containing the features.
 
      Examples:
          >>> import ehrapy as ep
-         >>> adata = ep.dt.mimic_2(encoded=True)
+         >>> adata = ep.dt.mimic_2(encoded=False)
+         >>> # want to move some metadata to the obs field
+         >>> ep.anndata.move_to_obs(adata, to_obs=["service_unit", "service_num", "age", "mort_day_censored"])
          >>> ep.tl.rank_features_groups(adata, "service_unit")
          >>> ep.pl.rank_features_groups(adata)
+
+         >>> import ehrapy as ep
+         >>> adata = ep.dt.mimic_2(encoded=False)
+         >>> # want to move some metadata to the obs field
+         >>> ep.anndata.move_to_obs(adata, to_obs=["service_unit", "service_num", "age", "mort_day_censored"])
+         >>> ep.tl.rank_features_groups(adata, "service_unit", field_to_rank="obs", columns_to_rank={"obs_names": ["age", "mort_day_censored"]})
+         >>> ep.pl.rank_features_groups(adata)
+
+         >>> import ehrapy as ep
+         >>> adata = ep.dt.mimic_2(encoded=False)
+         >>> # want to move some metadata to the obs field
+         >>> ep.anndata.move_to_obs(adata, to_obs=["service_unit", "service_num", "age", "mort_day_censored"])
+         >>> ep.tl.rank_features_groups(adata, "service_unit", field_to_rank="layer_and_obs", columns_to_rank={"var_names": ['copd_flg', 'renal_flg'], "obs_names": ["age", "mort_day_censored"]})
+         >>> ep.pl.rank_features_groups(adata)
+
     """
     if layer is not None and field_to_rank == "obs":
         raise ValueError("If 'layer' is not None, 'field_to_rank' cannot be 'obs'.")
@@ -452,6 +469,9 @@ def rank_features_groups(
             adata_minimal = adata_minimal[:, 1:]
 
         adata_minimal = encode(adata_minimal, autodetect=True, encodings="label")
+        # this is needed because encode() doesn't add this key if there are no categorical columns to encode
+        if "encoded_non_numerical_columns" not in adata_minimal.uns:
+            adata_minimal.uns["encoded_non_numerical_columns"] = []
 
     if layer is not None:
         adata_minimal.layers[layer] = adata_minimal.X
diff --git a/tests/tools/test_features_ranking.py b/tests/tools/test_features_ranking.py
index 8e10ead9..cc9c3c22 100644
--- a/tests/tools/test_features_ranking.py
+++ b/tests/tools/test_features_ranking.py
@@ -384,3 +384,46 @@ def test_rank_features_groups_consistent_results(self):
                 np.array(adata_features_in_x.uns["rank_features_groups"]["names"][record]),
                 np.array(adata_features_in_x_and_obs.uns["rank_features_groups"]["names"][record]),
             )
+
+    def test_rank_features_group_column_to_rank(self):
+        adata = read_csv(
+            dataset_path=f"{_TEST_PATH}/dataset1.csv",
+            columns_obs_only=["disease", "station", "sys_bp_entry", "dia_bp_entry"],
+            index_column="idx",
+        )
+
+        # get a fresh adata for every test to not have any side effects
+        adata_copy = adata.copy()
+
+        ep.tl.rank_features_groups(adata, groupby="disease", columns_to_rank="all")
+        assert len(adata.uns["rank_features_groups"]["names"]) == 2
+
+        # want to check a "complete selection" works
+        adata = adata_copy.copy()
+        ep.tl.rank_features_groups(adata, groupby="disease", columns_to_rank={"var_names": ["glucose", "weight"]})
+        assert len(adata.uns["rank_features_groups"]["names"]) == 2
+
+        # want to check a "sub-selection" works
+        adata = adata_copy.copy()
+        ep.tl.rank_features_groups(adata, groupby="disease", columns_to_rank={"var_names": ["glucose"]})
+        assert len(adata.uns["rank_features_groups"]["names"]) == 1
+
+        # want to check a "complete" selection works
+        adata = adata_copy.copy()
+        ep.tl.rank_features_groups(
+            adata,
+            groupby="disease",
+            field_to_rank="obs",
+            columns_to_rank={"obs_names": ["station", "sys_bp_entry", "dia_bp_entry"]},
+        )
+        assert len(adata.uns["rank_features_groups"]["names"]) == 3
+
+        # want to check a "sub-selection" selection works
+        adata = adata_copy.copy()
+        ep.tl.rank_features_groups(
+            adata,
+            groupby="disease",
+            field_to_rank="obs",
+            columns_to_rank={"obs_names": ["sys_bp_entry", "dia_bp_entry"]},
+        )
+        assert len(adata.uns["rank_features_groups"]["names"]) == 2