Adding median to summarize replicate z-scores in grit

cytominer_eval/evaluate.py

@@ -28,6 +28,7 @@ def evaluate(
     replicate_reproducibility_return_median_cor: bool = False,
     precision_recall_k: int = 10,
     grit_control_perts: List[str] = ["None"],
+    grit_replicate_summary_method: str = "mean",
     mp_value_params: dict = {},
 ):
     r"""Evaluate profile quality and strength.
@@ -86,10 +87,14 @@ def evaluate(
         Only used when `operation='grit'`. Specific profile identifiers used as a
         reference when calculating grit. The list entries must be found in the
         `replicate_groups[replicate_id]` column.
+    grit_replicate_summary_method : {"mean", "median"}, optional
+        Only used when `operation='grit'`. Defines how the replicate z scores are
+        summarized. see
+        :py:func:`cytominer_eval.operations.util.calculate_grit`
     mp_value_params : {{}, ...}, optional
         Only used when `operation='mp_value'`. A key, item pair of optional parameters
         for calculating mp value. See also
-        cytominer_eval.operations.util.default_mp_value_parameters
+        :py:func:`cytominer_eval.operations.util.default_mp_value_parameters`
     """
     # Check replicate groups input
     check_replicate_groups(eval_metric=operation, replicate_groups=replicate_groups)
@@ -124,6 +129,7 @@ def evaluate(
             control_perts=grit_control_perts,
             replicate_id=replicate_groups["replicate_id"],
             group_id=replicate_groups["group_id"],
+            replicate_summary_method=grit_replicate_summary_method,
         )
     elif operation == "mp_value":
         metric_result = mp_value(

cytominer_eval/operations/grit.py

@@ -7,7 +7,7 @@
 import pandas as pd
 from typing import List
 
-from .util import assign_replicates, calculate_grit
+from .util import assign_replicates, calculate_grit, check_grit_replicate_summary_method
 from cytominer_eval.transform.util import (
     set_pair_ids,
     set_grit_column_info,
@@ -20,6 +20,7 @@ def grit(
     control_perts: List[str],
     replicate_id: str,
     group_id: str,
+    replicate_summary_method: str = "mean",
 ) -> pd.DataFrame:
     r"""Calculate grit
 
@@ -30,16 +31,20 @@ def grit(
     control_perts : list
         a list of control perturbations to calculate a null distribution
     replicate_id : str
-        the metadata identifier marking which column tracks replicate perts
+        the metadata identifier marking which column tracks unique identifiers
     group_id : str
-        the metadata identifier marking which column tracks a higher order groups for
-        all perturbations
+        the metadata identifier marking which column defines how replicates are grouped
+    replicate_summary_method : {'mean', 'median'}, optional
+        how replicate z-scores to control perts are summarized. Defaults to "mean".
 
     Returns
     -------
     pandas.DataFrame
         A dataframe of grit measurements per perturbation
     """
+    # Check if we support the provided summary method
+    check_grit_replicate_summary_method(replicate_summary_method)
+
     # Determine pairwise replicates
     similarity_melted_df = assign_replicates(
         similarity_melted_df=similarity_melted_df,
@@ -61,7 +66,14 @@ def grit(
     # Calculate grit for each perturbation
     grit_df = (
         similarity_melted_df.groupby(replicate_col_name)
-        .apply(lambda x: calculate_grit(x, control_perts, column_id_info))
+        .apply(
+            lambda x: calculate_grit(
+                replicate_group_df=x,
+                control_perts=control_perts,
+                column_id_info=column_id_info,
+                replicate_summary_method=replicate_summary_method,
+            )
+        )
         .reset_index(drop=True)
     )
 

cytominer_eval/operations/util.py

@@ -7,7 +7,10 @@
 from sklearn.covariance import EmpiricalCovariance
 
 from cytominer_eval.transform import metric_melt
-from cytominer_eval.transform.util import set_pair_ids
+from cytominer_eval.transform.util import (
+    set_pair_ids,
+    check_grit_replicate_summary_method,
+)
 
 
 def assign_replicates(
@@ -88,11 +91,17 @@ def calculate_precision_recall(replicate_group_df: pd.DataFrame, k: int) -> pd.S
 
 
 def calculate_grit(
-    replicate_group_df: pd.DataFrame, control_perts: List[str], column_id_info: dict
+    replicate_group_df: pd.DataFrame,
+    control_perts: List[str],
+    column_id_info: dict,
+    replicate_summary_method: str = "mean",
 ) -> pd.Series:
     """
     Usage: Designed to be called within a pandas.DataFrame().groupby().apply()
     """
+    # Confirm that we support the provided summary method
+    check_grit_replicate_summary_method(replicate_summary_method)
+
     group_entry = get_grit_entry(replicate_group_df, column_id_info["group"]["id"])
     pert = get_grit_entry(replicate_group_df, column_id_info["replicate"]["id"])
 
@@ -125,7 +134,11 @@ def calculate_grit(
         scaler = StandardScaler()
         scaler.fit(control_distrib)
         grit_z_scores = scaler.transform(same_group_distrib)
-        grit = np.mean(grit_z_scores)
+
+        if replicate_summary_method == "mean":
+            grit = np.mean(grit_z_scores)
+        elif replicate_summary_method == "median":
+            grit = np.median(grit_z_scores)
 
         return_bundle = {"perturbation": pert, "group": group_entry, "grit": grit}
 

cytominer_eval/tests/test_evaluate.py

@@ -110,11 +110,13 @@ def test_evaluate_replicate_reprod_return_cor_true():
     assert top_genes == ["CDK2", "CCNE1", "ATF4", "KIF11", "CCND1"]
 
     assert np.round(med_cor_df.similarity_metric.max(), 3) == 0.949
-    assert sorted(med_cor_df.columns.tolist()) == sorted([
-        "Metadata_gene_name",
-        "Metadata_pert_name",
-        "similarity_metric",
-    ])
+    assert sorted(med_cor_df.columns.tolist()) == sorted(
+        [
+            "Metadata_gene_name",
+            "Metadata_pert_name",
+            "similarity_metric",
+        ]
+    )
 
 
 def test_evaluate_precision_recall():
@@ -197,6 +199,7 @@ def test_evaluate_grit():
         replicate_groups=grit_gene_replicate_groups,
         operation="grit",
         grit_control_perts=grit_gene_control_perts,
+        grit_replicate_summary_method="median",
     )
 
     top_result = (
@@ -206,7 +209,7 @@ def test_evaluate_grit():
             0,
         ]
     )
-    assert np.round(top_result.grit, 4) == 2.2597
+    assert np.round(top_result.grit, 4) == 2.3352
     assert top_result.group == "PTK2"
     assert top_result.perturbation == "PTK2-2"
 
@@ -224,6 +227,7 @@ def test_evaluate_grit():
         replicate_groups=grit_compound_replicate_groups,
         operation="grit",
         grit_control_perts=grit_compound_control_perts,
+        grit_replicate_summary_method="mean",
     )
 
     top_result = (

cytominer_eval/tests/test_operations/test_grit.py

@@ -91,7 +91,7 @@ def test_calculate_grit():
     expected_result = {"perturbation": "MTOR-2", "group": "MTOR", "grit": 1.55075}
     expected_result = pd.DataFrame(expected_result, index=["result"]).transpose()
 
-    assert_frame_equal(grit_result, expected_result, check_less_precise=True)
+    assert_frame_equal(grit_result, expected_result)
 
     # Calculate grit will not work with singleton perturbations
     # (no other perts in same group)
@@ -107,7 +107,7 @@ def test_calculate_grit():
     expected_result = {"perturbation": "AURKB-2", "group": "AURKB", "grit": np.nan}
     expected_result = pd.DataFrame(expected_result, index=["result"]).transpose()
 
-    assert_frame_equal(grit_result, expected_result, check_less_precise=True)
+    assert_frame_equal(grit_result, expected_result)
 
     # Calculate grit will not work with the full dataframe
     with pytest.raises(AssertionError) as ae:
@@ -147,7 +147,7 @@ def test_grit():
     expected_result = {"perturbation": "PTK2-2", "group": "PTK2", "grit": 4.61094}
     expected_result = pd.DataFrame(expected_result, index=[0]).transpose()
 
-    assert_frame_equal(top_result, expected_result, check_less_precise=True)
+    assert_frame_equal(top_result, expected_result)
 
     # There are six singletons in this dataset
     assert result.grit.isna().sum() == 6
@@ -157,3 +157,39 @@ def test_grit():
 
     # With this data, we do not expect the sum of grit to change
     assert np.round(result.grit.sum(), 0) == 152.0
+
+
+def test_grit_summary_metric():
+    result = grit(
+        similarity_melted_df=similarity_melted_df,
+        control_perts=control_perts,
+        replicate_id=replicate_id,
+        group_id=group_id,
+        replicate_summary_method="median",
+    ).sort_values(by="grit")
+
+    assert all([x in result.columns for x in ["perturbation", "group", "grit"]])
+
+    top_result = pd.DataFrame(
+        result.sort_values(by="grit", ascending=False)
+        .reset_index(drop=True)
+        .iloc[0, :],
+    )
+
+    expected_result = {"perturbation": "PTK2-2", "group": "PTK2", "grit": 4.715917}
+    expected_result = pd.DataFrame(expected_result, index=[0]).transpose()
+
+    assert_frame_equal(
+        top_result,
+        expected_result,
+    )
+
+    with pytest.raises(ValueError) as ve:
+        output = grit(
+            similarity_melted_df=similarity_melted_df,
+            control_perts=control_perts,
+            replicate_id=replicate_id,
+            group_id=group_id,
+            replicate_summary_method="fail",
+        )
+    assert "method not supported, use one of:" in str(ve.value)

cytominer_eval/tests/test_transform/test_util.py

@@ -9,6 +9,7 @@
 from cytominer_eval.transform.util import (
     get_available_eval_metrics,
     get_available_similarity_metrics,
+    get_available_grit_summary_methods,
     get_upper_matrix,
     convert_pandas_dtypes,
     assert_pandas_dtypes,
@@ -17,6 +18,7 @@
     assert_eval_metric,
     assert_melt,
     check_replicate_groups,
+    check_grit_replicate_summary_method,
 )
 
 random.seed(123)
@@ -48,6 +50,11 @@ def test_get_available_similarity_metrics():
     assert expected_result == get_available_similarity_metrics()
 
 
+def test_get_available_grit_summary_methods():
+    expected_result = ["mean", "median"]
+    assert expected_result == get_available_grit_summary_methods()
+
+
 def test_assert_eval_metric():
     with pytest.raises(AssertionError) as ae:
         output = assert_eval_metric(eval_metric="NOT SUPPORTED")
@@ -159,3 +166,14 @@ def test_check_replicate_groups():
             eval_metric="grit", replicate_groups=wrong_group_dict
         )
     assert "replicate_groups for grit not formed properly." in str(ae.value)
+
+
+def test_check_grit_replicate_summary_method():
+
+    # Pass
+    for metric in get_available_grit_summary_methods():
+        check_grit_replicate_summary_method(metric)
+
+    with pytest.raises(ValueError) as ve:
+        output = check_grit_replicate_summary_method("fail")
+    assert "method not supported, use one of:" in str(ve.value)

cytominer_eval/transform/util.py

@@ -13,6 +13,10 @@ def get_available_similarity_metrics():
     return ["pearson", "kendall", "spearman"]
 
 
+def get_available_grit_summary_methods():
+    return ["mean", "median"]
+
+
 def get_upper_matrix(df: pd.DataFrame) -> np.array:
     return np.triu(np.ones(df.shape), k=1).astype(bool)
 
@@ -148,3 +152,14 @@ def set_grit_column_info(replicate_id: str, group_id: str) -> dict:
 
     column_id_info = {"replicate": replicate_id_info, "group": group_id_info}
     return column_id_info
+
+
+def check_grit_replicate_summary_method(replicate_summary_method: str):
+    avail_methods = get_available_grit_summary_methods()
+
+    if replicate_summary_method not in avail_methods:
+        raise ValueError(
+            "{input} method not supported, use one of: {avail}".format(
+                input=replicate_summary_method, avail=avail_methods
+            )
+        )