Fix plot_multicomparison_fc with default figsize

pertpy/tools/_differential_gene_expression/_base.py

@@ -839,8 +839,13 @@ def _get_significance(p_val):
         sns.heatmap(df, **heatmap_kwargs, cmap="coolwarm", center=0, cbar_kws={"label": "Log2 fold change"})
 
         _size = {"< 0.001": marker_size, "< 0.01": math.floor(marker_size / 2), "< 0.1": math.floor(marker_size / 4)}
-        x_locs, x_labels = plt.xticks()[0], [label.get_text() for label in plt.xticks()[1]]
-        y_locs, y_labels = plt.yticks()[0], [label.get_text() for label in plt.yticks()[1]]
+        # Calculate locations directly from DataFrame instead of extracting from rendered plot (fixes #755)
+        # Seaborn places cell centers at 0.5, 1.5, 2.5, etc.
+        # NOTE: This assumes a non-clustered heatmap. If using clustermap, coordinates would need reordering.
+        x_locs = np.arange(len(df.columns)) + 0.5
+        x_labels = df.columns.tolist()
+        y_locs = np.arange(len(df.index)) + 0.5
+        y_labels = df.index.tolist()
 
         for _i, row in results_df.iterrows():
             if row["significance"] != "n.s.":

tests/tools/_differential_gene_expression/test_base.py

@@ -1,6 +1,7 @@
 from collections.abc import Sequence
 from importlib.util import find_spec
 
+import pandas as pd
 import pytest
 from pandas.core.api import DataFrame
 
@@ -89,3 +90,42 @@ def test_model_cond(test_adata_minimal, MockLinearModel, formula, cond_kwargs, e
         actual_contrast = mod.cond(**cond_kwargs)
         assert actual_contrast.tolist() == expected_contrast
         assert actual_contrast.index.tolist() == mod.design.columns.tolist()
+
+
+def test_plot_multicomparison_fc_many_genes(MockLinearModel, test_adata_minimal):
+    """Test that plot_multicomparison_fc works even when heatmap hides tick labels.
+
+    Regression test for issue #755.
+    When using small figsize or many genes, seaborn heatmap hides xticklabels.
+    The old code extracted labels from the rendered plot, causing ValueError.
+    The fix calculates positions directly from the DataFrame.
+    """
+    # Create mock results with many genes to force label hiding
+    results = []
+    genes = [f"GENE{i}" for i in range(50)]  # 50 genes will force label hiding
+    contrasts = ["contrast1", "contrast2"]
+
+    for contrast in contrasts:
+        for i, gene in enumerate(genes):
+            results.append(
+                {
+                    "contrast": contrast,
+                    "variable": gene,
+                    "log_fc": 1.5 + i * 0.05,
+                    "adj_p_value": 0.001 if i < 10 else 0.05,
+                }
+            )
+
+    results_df = pd.DataFrame(results)
+
+    # Create a mock model instance
+    mod = MockLinearModel(test_adata_minimal, "~condition")
+
+    # This should not raise ValueError even with small figsize
+    # that causes seaborn to hide tick labels
+    fig = mod.plot_multicomparison_fc(results_df, figsize=(6, 4), return_fig=True)
+    assert fig is not None
+
+    # Also test with heatmap_kwargs that explicitly hide labels
+    fig = mod.plot_multicomparison_fc(results_df, heatmap_kwargs={"xticklabels": False}, return_fig=True)
+    assert fig is not None