From 17896a7c29b3ad46a5becc482e0c8f6985a513bc Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 1 Apr 2026 15:06:43 +0000
Subject: [PATCH 1/3] Initial plan: add comprehensive tests to improve coverage

Agent-Logs-Url: https://github.com/CostaLab/pyCrossTalkeR/sessions/5897d241-aa30-411e-9f49-eef0fafe5988

Co-authored-by: jsnagai <8943000+jsnagai@users.noreply.github.com>
---
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diff --git a/pycrosstalker/tools/__pycache__/Comparative_condition.cpython-312.pyc b/pycrosstalker/tools/__pycache__/Comparative_condition.cpython-312.pyc
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From acfecbde6fe5adf4b3893fd963661b40acaf842f Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 1 Apr 2026 15:20:20 +0000
Subject: [PATCH 2/3] Add comprehensive tests: coverage from 54% to 97%

Agent-Logs-Url: https://github.com/CostaLab/pyCrossTalkeR/sessions/5897d241-aa30-411e-9f49-eef0fafe5988

Co-authored-by: jsnagai <8943000+jsnagai@users.noreply.github.com>
---
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diff --git a/pycrosstalker/plots/plot.py b/pycrosstalker/plots/plot.py
index 0b64342..aa1ae9f 100644
--- a/pycrosstalker/plots/plot.py
+++ b/pycrosstalker/plots/plot.py
@@ -192,7 +192,7 @@ def plot_pca_LR_comparative(lrobj_tblPCA, pca_table, dims=(1, 2), ret=False, ggi
 
         # Mapping Table
         if include_tf:
-            map_df = pd.DataFrame(pca_df.index, columns=["gene"])
+            map_df = pd.DataFrame({"gene": pca_df.index})
             map_df["mapping"] = map_df["gene"].apply(lambda gene: "Receptor" if "|R" in gene else ("Ligand" if "|L" in gene else "Transcription Factor"))
             color_groups = ["#f8756b", "#00b835", "#619cff"]
         else:
@@ -334,9 +334,9 @@ def plot_bar_rankings(annData, table_name, ranking, type = None, filter_sign = N
             pass
 
         if filter_sign == 'pos':
-            rankings_table = rankings_table[rankings_table['ranking'] > 0]
+            rankings_table = rankings_table[rankings_table[ranking] > 0]
         elif filter_sign == 'neg':
-            rankings_table = rankings_table[rankings_table['ranking'] < 0]
+            rankings_table = rankings_table[rankings_table[ranking] < 0]
 
 
         if rankings_table.empty:
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HcmV?d00001

diff --git a/test/conftest.py b/test/conftest.py
new file mode 100644
index 0000000..7de0ef7
--- /dev/null
+++ b/test/conftest.py
@@ -0,0 +1,48 @@
+"""
+Shared pytest fixtures for pyCrossTalkeR tests.
+"""
+import numpy as np
+import pandas as pd
+import pytest
+from anndata import AnnData
+from pycrosstalker import tools as cttl
+
+SEL_COLUMNS = ['source', 'target', 'gene_A', 'gene_B', 'type_gene_A', 'type_gene_B', 'MeanLR']
+
+
+def make_lr_df(seed=0):
+    """Create a minimal synthetic LR interaction DataFrame.
+
+    Uses 3 cell types and 3 LR pairs so that PCA(n_components=2) always has
+    enough samples (3 nodes) and features (Listener, Influencer, Mediator).
+    """
+    np.random.seed(seed)
+    cell_types = ['CellA', 'CellB', 'CellC']
+    lr_pairs = [('GENE_L1', 'GENE_R1'), ('GENE_L2', 'GENE_R2'), ('GENE_L3', 'GENE_R3')]
+    rows = []
+    for src in cell_types:
+        for tgt in cell_types:
+            for gl, gr in lr_pairs:
+                rows.append({
+                    'source': src,
+                    'target': tgt,
+                    'gene_A': gl,
+                    'gene_B': gr,
+                    'type_gene_A': 'Ligand',
+                    'type_gene_B': 'Receptor',
+                    'MeanLR': abs(float(np.random.normal(1.5, 0.5))) + 0.1,
+                })
+    return pd.DataFrame(rows)
+
+
+@pytest.fixture(scope="session")
+def analysed_adata():
+    """Fully analysed AnnData built from synthetic data (runs once per session)."""
+    adata = AnnData()
+    adata.uns['pycrosstalker'] = {
+        'path': {
+            'CTR': make_lr_df(0),
+            'EXP': make_lr_df(1),
+        }
+    }
+    return cttl.analise_LR(adata, save=False)
diff --git a/test/test_coverage.py b/test/test_coverage.py
new file mode 100644
index 0000000..a2a4adf
--- /dev/null
+++ b/test/test_coverage.py
@@ -0,0 +1,828 @@
+"""
+Comprehensive tests to improve coverage for pyCrossTalkeR.
+
+Covers uncovered paths in:
+  - pycrosstalker/tools/utils.py
+  - pycrosstalker/tools/Single_Condition.py
+  - pycrosstalker/tools/Comparative_condition.py
+  - pycrosstalker/plots/plot.py
+"""
+import json
+import os
+
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import networkx as nx
+import numpy as np
+import pandas as pd
+import pytest
+from anndata import AnnData
+
+from pycrosstalker import plots as ctpl
+from pycrosstalker import tools as cttl
+from pycrosstalker.tools.utils import (
+    add_node_type,
+    comparative_med,
+    comparative_pagerank,
+    create_ordered_circular_layout,
+    fisher_test_cci,
+    from_liana,
+    get_clustered_node_order,
+    graph_from_storable_json,
+    graph_to_storable_json,
+    mannwhitneyu_test_cci,
+    ranking_net,
+)
+
+# ---------------------------------------------------------------------------
+# Shared constants and helpers (also defined in conftest.py for fixtures)
+# ---------------------------------------------------------------------------
+
+SEL_COLUMNS = ['source', 'target', 'gene_A', 'gene_B', 'type_gene_A', 'type_gene_B', 'MeanLR']
+
+
+def make_lr_df(seed=0):
+    """Create a minimal synthetic LR interaction DataFrame.
+
+    Uses 3 cell types and 3 LR pairs so PCA(n_components=2) always succeeds
+    (3 nodes >= 2 required components).
+    """
+    np.random.seed(seed)
+    cell_types = ['CellA', 'CellB', 'CellC']
+    lr_pairs = [('GENE_L1', 'GENE_R1'), ('GENE_L2', 'GENE_R2'), ('GENE_L3', 'GENE_R3')]
+    rows = []
+    for src in cell_types:
+        for tgt in cell_types:
+            for gl, gr in lr_pairs:
+                rows.append({
+                    'source': src,
+                    'target': tgt,
+                    'gene_A': gl,
+                    'gene_B': gr,
+                    'type_gene_A': 'Ligand',
+                    'type_gene_B': 'Receptor',
+                    'MeanLR': abs(float(np.random.normal(1.5, 0.5))) + 0.1,
+                })
+    return pd.DataFrame(rows)
+
+
+# ---------------------------------------------------------------------------
+# Local helpers
+# ---------------------------------------------------------------------------
+
+def _make_analysed_pair():
+    """Return an AnnData after read_lr_single_condition + create_diff_table."""
+    adata = AnnData()
+    adata.uns['pycrosstalker'] = {
+        'path': {'CTR': make_lr_df(0), 'EXP': make_lr_df(1)}
+    }
+    adata = cttl.read_lr_single_condition(adata, sel_columns=SEL_COLUMNS)
+    adata = cttl.create_diff_table(adata, "/tmp/", comparison=None)
+    return adata
+
+
+def _close():
+    plt.close('all')
+
+
+# ===========================================================================
+# utils.py – add_node_type
+# ===========================================================================
+
+class TestAddNodeType:
+    def test_ligand_receptor(self):
+        df = pd.DataFrame({
+            'gene_A': ['GX'], 'gene_B': ['GY'],
+            'type_gene_A': ['Ligand'], 'type_gene_B': ['Receptor'],
+        })
+        out = add_node_type(df)
+        assert out['gene_A'].iloc[0] == 'GX|L'
+        assert out['gene_B'].iloc[0] == 'GY|R'
+
+    def test_receptor_ligand(self):
+        df = pd.DataFrame({
+            'gene_A': ['GX'], 'gene_B': ['GY'],
+            'type_gene_A': ['Receptor'], 'type_gene_B': ['Ligand'],
+        })
+        out = add_node_type(df)
+        assert out['gene_A'].iloc[0] == 'GX|R'
+        assert out['gene_B'].iloc[0] == 'GY|L'
+
+    def test_transcription_factor(self):
+        df = pd.DataFrame({
+            'gene_A': ['TF1'], 'gene_B': ['TF2'],
+            'type_gene_A': ['Transcription Factor'],
+            'type_gene_B': ['Transcription Factor'],
+        })
+        out = add_node_type(df)
+        assert out['gene_A'].iloc[0] == 'TF1|TF'
+        assert out['gene_B'].iloc[0] == 'TF2|TF'
+
+
+# ===========================================================================
+# utils.py – graph_to_storable_json / graph_from_storable_json
+# ===========================================================================
+
+class TestGraphSerialization:
+    def _sample_graph(self):
+        G = nx.DiGraph()
+        G.add_edge('CellA', 'CellB', LRScore=1.5, weight=1.5)
+        G.add_edge('CellB', 'CellC', LRScore=2.0, weight=2.0)
+        return G
+
+    def test_to_json_returns_string(self):
+        s = graph_to_storable_json(self._sample_graph())
+        assert isinstance(s, str)
+
+    def test_json_is_parseable(self):
+        s = graph_to_storable_json(self._sample_graph())
+        parsed = json.loads(s)
+        assert 'nodes' in parsed
+        assert 'edges' in parsed
+
+    def test_roundtrip_nodes(self):
+        G = self._sample_graph()
+        G2 = graph_from_storable_json(graph_to_storable_json(G))
+        assert set(str(n) for n in G2.nodes()) == set(G.nodes())
+
+    def test_roundtrip_edges(self):
+        G = self._sample_graph()
+        G2 = graph_from_storable_json(graph_to_storable_json(G))
+        orig_edges = {(str(u), str(v)) for u, v in G.edges()}
+        new_edges = {(str(u), str(v)) for u, v in G2.edges()}
+        assert orig_edges == new_edges
+
+    def test_edge_attrs_preserved(self):
+        G = nx.DiGraph()
+        G.add_edge('X', 'Y', LRScore=3.14, weight=3.14)
+        G2 = graph_from_storable_json(graph_to_storable_json(G))
+        assert pytest.approx(G2['X']['Y']['LRScore'], abs=1e-5) == 3.14
+
+
+# ===========================================================================
+# utils.py – get_clustered_node_order / create_ordered_circular_layout
+# ===========================================================================
+
+class TestLayoutUtils:
+    def _edge_df(self):
+        return pd.DataFrame({
+            'source': ['A', 'B', 'C', 'A'],
+            'target': ['B', 'C', 'A', 'C'],
+            'LRScore': [1.0, 2.0, 0.5, 1.5],
+        })
+
+    def test_get_clustered_node_order_returns_all_nodes(self):
+        order = get_clustered_node_order(self._edge_df())
+        assert isinstance(order, list)
+        assert set(order) == {'A', 'B', 'C'}
+        assert len(order) == 3
+
+    def test_get_clustered_node_order_no_duplicates(self):
+        order = get_clustered_node_order(self._edge_df())
+        assert len(order) == len(set(order))
+
+    def test_create_ordered_circular_layout_count(self):
+        layout = create_ordered_circular_layout(['A', 'B', 'C', 'D'])
+        assert len(layout) == 4
+
+    def test_create_ordered_circular_layout_on_unit_circle(self):
+        layout = create_ordered_circular_layout(['A', 'B', 'C'])
+        for node, (x, y) in layout.items():
+            assert abs(x**2 + y**2 - 1.0) < 1e-9, f"Node {node} not on unit circle"
+
+
+# ===========================================================================
+# utils.py – ranking_net (unsigned and signed modes)
+# ===========================================================================
+
+class TestRankingNet:
+    def _simple_digraph(self):
+        G = nx.DiGraph()
+        G.add_edge('A', 'B', weight=1.0)
+        G.add_edge('B', 'C', weight=2.0)
+        G.add_edge('A', 'C', weight=0.5)
+        return G
+
+    def test_unsigned_columns(self):
+        result = ranking_net(self._simple_digraph(), mode=True)
+        assert set(result.columns) >= {'nodes', 'Listener', 'Influencer', 'Mediator'}
+
+    def test_unsigned_all_nodes_present(self):
+        result = ranking_net(self._simple_digraph(), mode=True)
+        assert set(result['nodes']) == {'A', 'B', 'C'}
+
+    def test_unsigned_no_nan(self):
+        result = ranking_net(self._simple_digraph(), mode=True)
+        assert not result[['Listener', 'Influencer', 'Mediator']].isna().any().any()
+
+    def test_signed_columns(self):
+        G = nx.DiGraph()
+        G.add_edge('A', 'B', weight=1.0)
+        G.add_edge('B', 'C', weight=-2.0)
+        result = ranking_net(G, mode=False)
+        assert set(result.columns) >= {'nodes', 'Listener', 'Influencer'}
+
+    def test_signed_no_mediator_column(self):
+        G = nx.DiGraph()
+        G.add_edge('A', 'B', weight=1.0)
+        result = ranking_net(G, mode=False)
+        assert 'Mediator' not in result.columns
+
+    def test_signed_all_nodes_present(self):
+        G = nx.DiGraph()
+        G.add_edge('A', 'B', weight=1.0)
+        G.add_edge('B', 'C', weight=-2.0)
+        result = ranking_net(G, mode=False)
+        assert set(result['nodes']) == {'A', 'B', 'C'}
+
+
+# ===========================================================================
+# utils.py – comparative_pagerank / comparative_med
+# ===========================================================================
+
+class TestComparativeRankingHelpers:
+    def _rankings_fixture(self):
+        """Minimal rankings dict with CTR and EXP entries."""
+        ctr = pd.DataFrame({'nodes': ['X', 'Y'], 'Pagerank': [0.6, 0.4], 'Mediator': [5.0, 3.0]})
+        exp = pd.DataFrame({'nodes': ['X', 'Y'], 'Pagerank': [0.7, 0.3], 'Mediator': [6.0, 2.0]})
+        return {'CTR': ctr, 'EXP': exp}
+
+    def _curr_rkg(self):
+        return pd.DataFrame({'nodes': ['X', 'Y'], 'Listener': [1, 2], 'Influencer': [2, 1]})
+
+    def test_comparative_pagerank_adds_column(self):
+        rankings = self._rankings_fixture()
+        curr = self._curr_rkg()
+        result = comparative_pagerank(rankings, "graphs", "EXP_x_CTR", curr)
+        assert 'Pagerank' in result.columns
+
+    def test_comparative_pagerank_filtered_name(self):
+        rankings = self._rankings_fixture()
+        curr = self._curr_rkg()
+        result = comparative_pagerank(rankings, "graphs", "EXP_x_CTR_filtered", curr)
+        assert 'Pagerank' in result.columns
+
+    def test_comparative_med_adds_column(self):
+        rankings = self._rankings_fixture()
+        curr = self._curr_rkg()
+        result = comparative_med(rankings, "graphs", "EXP_x_CTR", curr)
+        assert 'Mediator' in result.columns
+
+    def test_comparative_med_filtered_name(self):
+        rankings = self._rankings_fixture()
+        curr = self._curr_rkg()
+        result = comparative_med(rankings, "graphs", "EXP_x_CTR_filtered", curr)
+        assert 'Mediator' in result.columns
+
+
+# ===========================================================================
+# utils.py – fisher_test_cci with explicit comparison parameter
+# ===========================================================================
+
+class TestFisherTestExplicitComparison:
+    def test_explicit_comparison_adds_stats(self):
+        adata = _make_analysed_pair()
+        adata = fisher_test_cci(adata, 'LRScore', '/tmp/', comparison=[('EXP', 'CTR')])
+        stats = adata.uns['pycrosstalker']['results']['stats']
+        assert 'EXP_x_CTR' in stats
+
+    def test_explicit_comparison_result_has_expected_columns(self):
+        adata = _make_analysed_pair()
+        adata = fisher_test_cci(adata, 'LRScore', '/tmp/', comparison=[('EXP', 'CTR')])
+        result = adata.uns['pycrosstalker']['results']['stats']['EXP_x_CTR']
+        assert {'cellpair', 'p_value', 'lodds'}.issubset(result.columns)
+
+    def test_explicit_comparison_p_values_in_range(self):
+        adata = _make_analysed_pair()
+        adata = fisher_test_cci(adata, 'LRScore', '/tmp/', comparison=[('EXP', 'CTR')])
+        p_vals = adata.uns['pycrosstalker']['results']['stats']['EXP_x_CTR']['p_value']
+        assert ((p_vals >= 0) & (p_vals <= 1)).all()
+
+
+# ===========================================================================
+# utils.py – mannwhitneyu_test_cci with explicit comparison parameter
+# ===========================================================================
+
+class TestMannWhitneyExplicitComparison:
+    def test_explicit_comparison_adds_stats(self):
+        adata = _make_analysed_pair()
+        adata = mannwhitneyu_test_cci(adata, 'LRScore', '/tmp/', comparison=[('EXP', 'CTR')])
+        stats = adata.uns['pycrosstalker']['results']['stats']
+        assert 'EXP_x_CTR:MannU' in stats
+
+    def test_explicit_comparison_result_columns(self):
+        adata = _make_analysed_pair()
+        adata = mannwhitneyu_test_cci(adata, 'LRScore', '/tmp/', comparison=[('EXP', 'CTR')])
+        result = adata.uns['pycrosstalker']['results']['stats']['EXP_x_CTR:MannU']
+        assert {'cellpair', 'statistic', 'p_value', 'lfc'}.issubset(result.columns)
+
+
+# ===========================================================================
+# utils.py – from_liana
+# ===========================================================================
+
+class TestFromLiana:
+    def _base_df(self):
+        return pd.DataFrame({
+            'ligand': ['GENE_L1', 'GENE_L2'],
+            'receptor_complex': ['GENE_R1', 'GENE_R2'],
+            'source': ['CellA', 'CellB'],
+            'target': ['CellB', 'CellA'],
+            'cellphone_pvals': [0.01, 0.03],
+            'lr_means': [1.5, 2.0],
+        })
+
+    def test_dataframe_with_label_creates_path(self):
+        df = self._base_df()
+        df['label'] = 'condition1_lr_data'
+        adata = AnnData()
+        adata.uns['liana'] = df
+        result = from_liana(adata)
+        assert 'pycrosstalker' in result.uns
+        assert len(result.uns['pycrosstalker']['path']) > 0
+
+    def test_dataframe_pval_filter_applied(self):
+        df = self._base_df()
+        df['label'] = 'condition1_lr_data'
+        # Both p_values <= 0.05, so both should pass
+        adata = AnnData()
+        adata.uns['liana'] = df
+        result = from_liana(adata, pval_filter=True)
+        key = list(result.uns['pycrosstalker']['path'].keys())[0]
+        assert len(result.uns['pycrosstalker']['path'][key]) == 2
+
+    def test_dataframe_no_pval_filter(self):
+        df = self._base_df()
+        df['label'] = 'condition1_lr_data'
+        df['cellphone_pvals'] = [0.5, 0.8]  # both would be filtered out
+        adata = AnnData()
+        adata.uns['liana'] = df
+        result = from_liana(adata, pval_filter=False)
+        key = list(result.uns['pycrosstalker']['path'].keys())[0]
+        assert len(result.uns['pycrosstalker']['path'][key]) == 2
+
+    def test_dict_input_creates_path(self):
+        adata = AnnData()
+        adata.uns['liana'] = {'condition1_lr_data': self._base_df()}
+        result = from_liana(adata, pval_filter=False)
+        assert 'pycrosstalker' in result.uns
+        assert 'condition1_lr_data' in result.uns['pycrosstalker']['path']
+
+    def test_dict_correct_columns(self):
+        adata = AnnData()
+        adata.uns['liana'] = {'condition1_lr_data': self._base_df()}
+        result = from_liana(adata, pval_filter=False)
+        df = result.uns['pycrosstalker']['path']['condition1_lr_data']
+        expected = {'source', 'target', 'type_gene_A', 'type_gene_B', 'gene_A', 'gene_B', 'MeanLR'}
+        assert expected.issubset(df.columns)
+
+    def test_compute_means(self):
+        df = self._base_df()
+        df['label'] = 'condition1_lr_data'
+        df['ligand_means'] = [0.8, 1.2]
+        df['receptor_means'] = [1.1, 0.9]
+        adata = AnnData()
+        adata.uns['liana'] = df
+        result = from_liana(adata, compute_means=True, pval_filter=False)
+        assert len(result.uns['pycrosstalker']['path']) > 0
+        key = list(result.uns['pycrosstalker']['path'].keys())[0]
+        assert 'MeanLR' in result.uns['pycrosstalker']['path'][key].columns
+
+
+# ===========================================================================
+# Single_Condition.py – dict input, invalid input, receptor-first ordering
+# ===========================================================================
+
+class TestReadLRSingleCondition:
+    def test_dict_input_from_csv(self, tmp_path):
+        csv_path = tmp_path / "CTR_LR.csv"
+        make_lr_df(0).to_csv(csv_path, index=False)
+        result = cttl.read_lr_single_condition({'CTR': str(csv_path)}, sel_columns=SEL_COLUMNS)
+        assert isinstance(result, AnnData)
+        assert 'CTR' in result.uns['pycrosstalker']['results']['graphs']
+
+    def test_dict_input_creates_correct_structure(self, tmp_path):
+        csv_path = tmp_path / "CTR_LR.csv"
+        make_lr_df(0).to_csv(csv_path, index=False)
+        result = cttl.read_lr_single_condition({'CTR': str(csv_path)}, sel_columns=SEL_COLUMNS)
+        keys = {'graphs', 'graphs_ggi', 'tables', 'colors', 'coords', 'rankings', 'pca', 'stats'}
+        assert keys.issubset(result.uns['pycrosstalker']['results'].keys())
+
+    def test_invalid_input_type_raises(self):
+        with pytest.raises(ValueError, match="Input parameter"):
+            cttl.read_lr_single_condition(42, sel_columns=SEL_COLUMNS)
+
+    def test_invalid_dict_value_raises(self):
+        with pytest.raises(ValueError, match="Issue with input paths"):
+            cttl.read_lr_single_condition({'CTR': 42}, sel_columns=SEL_COLUMNS)
+
+    def test_receptor_first_ordering(self):
+        """Covers the else-branch when type_gene_A == 'Receptor' (not 'Ligand')."""
+        df = pd.DataFrame({
+            'source': ['CellA', 'CellA', 'CellB'],
+            'target': ['CellB', 'CellB', 'CellA'],
+            'gene_A': ['GENE_R1', 'GENE_R2', 'GENE_R1'],
+            'gene_B': ['GENE_L1', 'GENE_L2', 'GENE_L1'],
+            'type_gene_A': ['Receptor', 'Receptor', 'Receptor'],
+            'type_gene_B': ['Ligand', 'Ligand', 'Ligand'],
+            'MeanLR': [1.5, 2.0, 0.8],
+        })
+        adata = AnnData()
+        adata.uns['pycrosstalker'] = {'path': {'CTR': df}}
+        result = cttl.read_lr_single_condition(adata, sel_columns=SEL_COLUMNS)
+        assert 'CTR' in result.uns['pycrosstalker']['results']['graphs']
+        # ligpair/recpair should be correctly formed even with swapped gene order
+        assert 'ligpair' in result.uns['pycrosstalker']['results']['tables']['CTR'].columns
+
+
+# ===========================================================================
+# Comparative_condition.py – explicit comparison parameter
+# ===========================================================================
+
+class TestCreateDiffTableExplicit:
+    def test_explicit_comparison_creates_diff_graph(self):
+        adata = AnnData()
+        adata.uns['pycrosstalker'] = {'path': {'CTR': make_lr_df(0), 'EXP': make_lr_df(1)}}
+        adata = cttl.read_lr_single_condition(adata, sel_columns=SEL_COLUMNS)
+        adata = cttl.create_diff_table(adata, "/tmp/", comparison=[('EXP', 'CTR')])
+        results = adata.uns['pycrosstalker']['results']
+        assert 'EXP_x_CTR' in results['graphs']
+        assert 'EXP_x_CTR' in results['tables']
+        assert 'EXP_x_CTR' in results['graphs_ggi']
+
+    def test_explicit_comparison_lrscore_column_present(self):
+        adata = AnnData()
+        adata.uns['pycrosstalker'] = {'path': {'CTR': make_lr_df(0), 'EXP': make_lr_df(1)}}
+        adata = cttl.read_lr_single_condition(adata, sel_columns=SEL_COLUMNS)
+        adata = cttl.create_diff_table(adata, "/tmp/", comparison=[('EXP', 'CTR')])
+        graph_df = adata.uns['pycrosstalker']['results']['graphs']['EXP_x_CTR']
+        assert 'LRScore' in graph_df.columns
+
+    def test_explicit_multiple_comparisons(self):
+        adata = AnnData()
+        adata.uns['pycrosstalker'] = {
+            'path': {'CTR': make_lr_df(0), 'EXP1': make_lr_df(1), 'EXP2': make_lr_df(2)}
+        }
+        adata = cttl.read_lr_single_condition(adata, sel_columns=SEL_COLUMNS)
+        adata = cttl.create_diff_table(
+            adata, "/tmp/", comparison=[('EXP1', 'CTR'), ('EXP2', 'CTR')]
+        )
+        results = adata.uns['pycrosstalker']['results']
+        assert 'EXP1_x_CTR' in results['graphs']
+        assert 'EXP2_x_CTR' in results['graphs']
+
+
+# ===========================================================================
+# plots/plot.py – plot_cci additional branches
+# ===========================================================================
+
+class TestPlotCCIBranches:
+    def test_plot_cci_log_true(self, analysed_adata):
+        """Covers the log=True branch (line ~104)."""
+        data = analysed_adata.uns['pycrosstalker']['results']
+        fig, ax = ctpl.plot.plot_cci(
+            graph=data['graphs']['CTR'],
+            colors=data['colors'],
+            plt_name='Log Mode',
+            coords=data['coords'],
+            pg=list(data['rankings']['CTR']['Pagerank']),
+            log=True,
+            return_figure=True,
+        )
+        assert fig is not None
+        _close()
+
+    def test_plot_cci_single_coord(self):
+        """Covers the single-node coordinate branch (line ~85)."""
+        graph_df = pd.DataFrame({
+            'source': ['CellA'],
+            'target': ['CellA'],
+            'LRScore': [1.0],
+            'freq': [0.5],
+            'weight': [1.0],
+            'inter': [0.5],
+        })
+        colors = {'CellA': '#ff0000'}
+        coords = {'CellA': (0.0, 0.0)}
+        fig, ax = ctpl.plot.plot_cci(
+            graph=graph_df,
+            colors=colors,
+            plt_name='Single Node',
+            coords=coords,
+            pg=[0.5],
+            return_figure=True,
+        )
+        assert fig is not None
+        _close()
+
+    def test_plot_cci_with_emax(self, analysed_adata):
+        """Covers emax-specified branch."""
+        data = analysed_adata.uns['pycrosstalker']['results']
+        fig, ax = ctpl.plot.plot_cci(
+            graph=data['graphs']['CTR'],
+            colors=data['colors'],
+            plt_name='emax set',
+            coords=data['coords'],
+            pg=list(data['rankings']['CTR']['Pagerank']),
+            emax=5.0,
+            return_figure=True,
+        )
+        assert fig is not None
+        _close()
+
+
+# ===========================================================================
+# plots/plot.py – plot_bar_rankings
+# ===========================================================================
+
+class TestPlotBarRankings:
+    def test_comparative_ranking_runs(self, analysed_adata):
+        ctpl.plot.plot_bar_rankings(
+            analysed_adata, table_name='EXP_x_CTR', ranking='Pagerank'
+        )
+        _close()
+
+    def test_comparative_ranking_mode_cgi(self, analysed_adata):
+        ctpl.plot.plot_bar_rankings(
+            analysed_adata, table_name='EXP_x_CTR', ranking='Pagerank', mode='cgi'
+        )
+        _close()
+
+    def test_non_comparative_returns_none(self, analysed_adata):
+        """plot_bar_rankings with no _x_ table silently returns None."""
+        result = ctpl.plot.plot_bar_rankings(
+            analysed_adata, table_name='CTR', ranking='Pagerank'
+        )
+        assert result is None
+        _close()
+
+    def test_filter_sign_neg(self, analysed_adata):
+        ctpl.plot.plot_bar_rankings(
+            analysed_adata, table_name='EXP_x_CTR', ranking='Pagerank', filter_sign='neg'
+        )
+        _close()
+
+
+# ===========================================================================
+# plots/plot.py – plot_clustermap / plot_graph_clustermap
+# ===========================================================================
+
+class TestPlotClustermaps:
+    def test_plot_clustermap_runs(self, analysed_adata):
+        table = analysed_adata.uns['pycrosstalker']['results']['tables']['CTR']
+        ctpl.plot.plot_clustermap(table, title='CTR Clustermap')
+        _close()
+
+    def test_plot_graph_clustermap_runs(self, analysed_adata):
+        graph = analysed_adata.uns['pycrosstalker']['results']['graphs']['CTR']
+        ctpl.plot.plot_graph_clustermap(graph, title='CTR Graph Clustermap')
+        _close()
+
+    def test_plot_graph_clustermap_differential(self, analysed_adata):
+        """Covers the comparative (signed) graph case with negative values."""
+        graph = analysed_adata.uns['pycrosstalker']['results']['graphs']['EXP_x_CTR']
+        ctpl.plot.plot_graph_clustermap(graph, title='EXP_x_CTR Graph Clustermap')
+        _close()
+
+
+# ===========================================================================
+# plots/plot.py – plot_volcane
+# ===========================================================================
+
+class TestPlotVolcane:
+    def test_fisher_volcane_runs(self, analysed_adata):
+        stats = analysed_adata.uns['pycrosstalker']['results']['stats']['EXP_x_CTR'].copy()
+        ctpl.plot.plot_volcane(stats, method='fisher')
+        _close()
+
+    def test_mannwhitney_volcane_runs(self, analysed_adata):
+        stats = analysed_adata.uns['pycrosstalker']['results']['stats'].get('EXP_x_CTR:MannU')
+        if stats is not None and len(stats) > 0:
+            ctpl.plot.plot_volcane(stats.copy(), method='mannwhitneyu')
+        _close()
+
+
+# ===========================================================================
+# plots/plot.py – plot_pca_LR_comparative
+# ===========================================================================
+
+class TestPlotPCAComparative:
+    def test_pca_non_ggi_returns_plot(self, analysed_adata):
+        data = analysed_adata.uns['pycrosstalker']['results']
+        result = ctpl.plot.plot_pca_LR_comparative(data, 'CTR', ret=True, ggi=False)
+        assert result is not None
+        _close()
+
+    def test_pca_ggi_all_gene_types(self, analysed_adata):
+        data = analysed_adata.uns['pycrosstalker']['results']
+        ctpl.plot.plot_pca_LR_comparative(data, 'CTR_ggi', ret=False, ggi=True, gene_types='all')
+        _close()
+
+    def test_pca_ggi_lr_gene_types(self, analysed_adata):
+        data = analysed_adata.uns['pycrosstalker']['results']
+        ctpl.plot.plot_pca_LR_comparative(data, 'CTR_ggi', ret=False, ggi=True, gene_types='LR')
+        _close()
+
+    def test_pca_ggi_include_tf(self, analysed_adata):
+        import copy
+        # Deep-copy the rankings/pca entries so in-place PC1/PC2 writes from earlier
+        # tests do not corrupt the shared session fixture for this or later tests.
+        data = analysed_adata.uns['pycrosstalker']['results']
+        data_copy = dict(data)
+        data_copy['rankings'] = {k: v.copy() for k, v in data['rankings'].items()}
+        ctpl.plot.plot_pca_LR_comparative(
+            data_copy, 'CTR_ggi', ret=False, ggi=True, include_tf=True, gene_types='all'
+        )
+        _close()
+
+    def test_pca_ggi_returns_plot_when_ret_true(self, analysed_adata):
+        data = analysed_adata.uns['pycrosstalker']['results']
+        result = ctpl.plot.plot_pca_LR_comparative(data, 'CTR_ggi', ret=True, ggi=True)
+        assert result is not None
+        _close()
+
+
+# ===========================================================================
+# plots/plot.py – plot_sankey / gen_sankey
+# ===========================================================================
+
+class TestPlotSankey:
+    def test_no_target_uses_all_data(self, analysed_adata):
+        table = analysed_adata.uns['pycrosstalker']['results']['tables']['CTR'].copy()
+        ctpl.plot.plot_sankey(table, target=None, plt_name='All interactions')
+        _close()
+
+    def test_with_ligand_target(self, analysed_adata):
+        table = analysed_adata.uns['pycrosstalker']['results']['tables']['CTR'].copy()
+        ligand_genes = table[table['type_gene_A'] == 'Ligand']['gene_A'].dropna().unique()
+        if len(ligand_genes) > 0:
+            ctpl.plot.plot_sankey(table.copy(), target=ligand_genes[0], plt_name='Ligand target')
+        _close()
+
+    def test_with_receptor_target(self, analysed_adata):
+        table = analysed_adata.uns['pycrosstalker']['results']['tables']['CTR'].copy()
+        receptor_genes = table[table['type_gene_B'] == 'Receptor']['gene_B'].dropna().unique()
+        if len(receptor_genes) > 0:
+            ctpl.plot.plot_sankey(table.copy(), target=receptor_genes[0], plt_name='Receptor target')
+        _close()
+
+    def test_with_ligand_cluster_filter(self, analysed_adata):
+        table = analysed_adata.uns['pycrosstalker']['results']['tables']['CTR'].copy()
+        ctpl.plot.plot_sankey(
+            table, target=None, ligand_cluster=['CellA'], plt_name='Ligand cluster'
+        )
+        _close()
+
+    def test_with_receptor_cluster_filter(self, analysed_adata):
+        """Covers the receptor_cluster is not None branch (line ~433)."""
+        table = analysed_adata.uns['pycrosstalker']['results']['tables']['CTR'].copy()
+        ctpl.plot.plot_sankey(
+            table, target=None, receptor_cluster=['CellB'], plt_name='Receptor cluster'
+        )
+        _close()
+
+    def test_with_missing_target_prints_not_found(self, analysed_adata, capsys):
+        table = analysed_adata.uns['pycrosstalker']['results']['tables']['CTR'].copy()
+        ctpl.plot.plot_sankey(table, target='NONEXISTENT_GENE_XYZ', plt_name='Missing')
+        captured = capsys.readouterr()
+        assert 'Not Found' in captured.out
+        _close()
+
+
+# ===========================================================================
+# utils.py – remaining gaps
+# ===========================================================================
+
+class TestGraphSerializationNumpyScalars:
+    def test_numpy_scalar_edge_attrs_serialized(self):
+        """Covers the isinstance(v, np.generic) branch in graph_to_storable_json (line ~541)."""
+        G = nx.DiGraph()
+        G.add_edge('A', 'B', LRScore=np.float64(2.5), weight=np.float64(2.5))
+        json_str = graph_to_storable_json(G)
+        # Must round-trip without TypeError (np.float64 is not JSON-serializable natively)
+        parsed = json.loads(json_str)
+        assert 'edges' in parsed
+
+
+class TestFromLianaRemainingBranches:
+    def _base_df(self):
+        return pd.DataFrame({
+            'ligand': ['GENE_L1', 'GENE_L2'],
+            'receptor_complex': ['GENE_R1', 'GENE_R2'],
+            'source': ['CellA', 'CellB'],
+            'target': ['CellB', 'CellA'],
+            'cellphone_pvals': [0.01, 0.03],
+            'lr_means': [1.5, 2.0],
+            'ligand_means': [0.8, 1.2],
+            'receptor_means': [1.1, 0.9],
+        })
+
+    def test_dict_compute_means(self):
+        """Covers compute_means=True in the dict path (line ~737)."""
+        adata = AnnData()
+        adata.uns['liana'] = {'condition1_lr_data': self._base_df()}
+        result = from_liana(adata, compute_means=True, pval_filter=False)
+        key = list(result.uns['pycrosstalker']['path'].keys())[0]
+        assert 'MeanLR' in result.uns['pycrosstalker']['path'][key].columns
+
+    def test_dict_pval_filter_true(self):
+        """Covers pval_filter=True in the dict path (line ~740)."""
+        df = self._base_df().copy()
+        df['cellphone_pvals'] = [0.01, 0.8]  # only first row passes
+        adata = AnnData()
+        adata.uns['liana'] = {'condition1': df}
+        result = from_liana(adata, pval_filter=True)
+        key = list(result.uns['pycrosstalker']['path'].keys())[0]
+        assert len(result.uns['pycrosstalker']['path'][key]) == 1
+
+
+# ===========================================================================
+# plots/plot.py – plot_bar_rankings remaining branches
+# ===========================================================================
+
+class TestPlotBarRankingsTypeFiler:
+    """Cover the 'type' parameter branches in plot_bar_rankings (lines 314-326)."""
+
+    def test_type_single_char_L(self, analysed_adata):
+        ctpl.plot.plot_bar_rankings(
+            analysed_adata, table_name='EXP_x_CTR', ranking='Pagerank', type='L'
+        )
+        _close()
+
+    def test_type_two_char_lr(self, analysed_adata):
+        ctpl.plot.plot_bar_rankings(
+            analysed_adata, table_name='EXP_x_CTR', ranking='Pagerank', type='LR'
+        )
+        _close()
+
+    def test_type_two_char_tf(self, analysed_adata):
+        ctpl.plot.plot_bar_rankings(
+            analysed_adata, table_name='EXP_x_CTR', ranking='Pagerank', type='TF'
+        )
+        _close()
+
+    def test_type_three_char_rtf(self, analysed_adata):
+        ctpl.plot.plot_bar_rankings(
+            analysed_adata, table_name='EXP_x_CTR', ranking='Pagerank', type='RTF'
+        )
+        _close()
+
+    def test_type_three_char_ltf(self, analysed_adata):
+        ctpl.plot.plot_bar_rankings(
+            analysed_adata, table_name='EXP_x_CTR', ranking='Pagerank', type='LTF'
+        )
+        _close()
+
+    def test_filter_sign_pos_empty_returns_message(self, analysed_adata):
+        """Covers filter_sign='pos' (line 337) and empty check (line 343)."""
+        # Use a ranking column where all values for the filtered set are negative
+        # by filtering for a type that doesn't exist in the data, guaranteeing empty
+        result = ctpl.plot.plot_bar_rankings(
+            analysed_adata,
+            table_name='EXP_x_CTR',
+            ranking='Pagerank',
+            type='TF',       # No TF nodes → empty table
+            filter_sign='pos',
+        )
+        assert result == "No entries with provided Filters."
+        _close()
+
+
+# ===========================================================================
+# plots/plot.py – plot_volcane with annotated (red) points
+# ===========================================================================
+
+class TestPlotVolcaneAnnotated:
+    def test_volcane_with_red_points(self):
+        """Covers the text annotation branch when points are significant (line ~760)."""
+        # Construct a stats DataFrame where at least one cell pair is 'red'
+        # (|lfc| > fc_threshold AND p_value < p_threshold)
+        df = pd.DataFrame({
+            'cellpair': ['CellA@CellB', 'CellA@CellA', 'CellB@CellB'],
+            'p_value':  [0.001, 0.5, 0.8],
+            'lodds':    [3.0, 0.1, -0.2],  # first entry has |lodds|>1 and p<0.05
+        })
+        ctpl.plot.plot_volcane(df.copy(), method='fisher', p_threshold=0.05, fc_threshold=1)
+        _close()
+
+
+# ===========================================================================
+# plots/plot.py – gen_sankey2 (alternative Plotly-based Sankey)
+# ===========================================================================
+
+class TestGenSankey2:
+    def test_gen_sankey2_runs(self, analysed_adata):
+        """Directly exercises gen_sankey2 (lines 474-557)."""
+        table = analysed_adata.uns['pycrosstalker']['results']['tables']['CTR'].copy()
+        # gen_sankey2 requires positive count values; filter to ligand/receptor rows
+        df = table[
+            (table['type_gene_A'] == 'Ligand') & (table['type_gene_B'] == 'Receptor')
+        ].head(5).copy()
+        df = df[['source', 'gene_A', 'gene_B', 'target', 'LRScore']].copy()
+        df['LRScore'] = df['LRScore'].abs() + 0.1  # ensure positive, non-zero
+        ctpl.plot.gen_sankey2(
+            df, cat_cols=['source', 'gene_A', 'gene_B', 'target'], value_cols='LRScore',
+            title='Test gen_sankey2'
+        )
+        _close()

From 5d487bdc1c223dc3d404c1fa929b3ef962f86f2d Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Wed, 1 Apr 2026 15:24:22 +0000
Subject: [PATCH 3/3] Extract shared test helpers to test/helpers.py, eliminate
 duplication

Agent-Logs-Url: https://github.com/CostaLab/pyCrossTalkeR/sessions/5897d241-aa30-411e-9f49-eef0fafe5988

Co-authored-by: jsnagai <8943000+jsnagai@users.noreply.github.com>
---
 test/__init__.py                              |   0
 test/__pycache__/__init__.cpython-312.pyc     | Bin 0 -> 159 bytes
 .../conftest.cpython-312-pytest-9.0.2.pyc     | Bin 2217 -> 1120 bytes
 test/__pycache__/helpers.cpython-312.pyc      | Bin 0 -> 1431 bytes
 ...test_coverage.cpython-312-pytest-9.0.2.pyc | Bin 87674 -> 86732 bytes
 test/conftest.py                              |  30 ++--------------
 test/helpers.py                               |  32 ++++++++++++++++++
 test/test_coverage.py                         |  29 ++--------------
 8 files changed, 36 insertions(+), 55 deletions(-)
 create mode 100644 test/__init__.py
 create mode 100644 test/__pycache__/__init__.cpython-312.pyc
 create mode 100644 test/__pycache__/helpers.cpython-312.pyc
 create mode 100644 test/helpers.py

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diff --git a/test/conftest.py b/test/conftest.py
index 7de0ef7..c13fdb6 100644
--- a/test/conftest.py
+++ b/test/conftest.py
@@ -1,38 +1,12 @@
 """
 Shared pytest fixtures for pyCrossTalkeR tests.
 """
-import numpy as np
-import pandas as pd
 import pytest
 from anndata import AnnData
 from pycrosstalker import tools as cttl
+from test.helpers import SEL_COLUMNS, make_lr_df  # noqa: F401 – re-exported for tests
 
-SEL_COLUMNS = ['source', 'target', 'gene_A', 'gene_B', 'type_gene_A', 'type_gene_B', 'MeanLR']
-
-
-def make_lr_df(seed=0):
-    """Create a minimal synthetic LR interaction DataFrame.
-
-    Uses 3 cell types and 3 LR pairs so that PCA(n_components=2) always has
-    enough samples (3 nodes) and features (Listener, Influencer, Mediator).
-    """
-    np.random.seed(seed)
-    cell_types = ['CellA', 'CellB', 'CellC']
-    lr_pairs = [('GENE_L1', 'GENE_R1'), ('GENE_L2', 'GENE_R2'), ('GENE_L3', 'GENE_R3')]
-    rows = []
-    for src in cell_types:
-        for tgt in cell_types:
-            for gl, gr in lr_pairs:
-                rows.append({
-                    'source': src,
-                    'target': tgt,
-                    'gene_A': gl,
-                    'gene_B': gr,
-                    'type_gene_A': 'Ligand',
-                    'type_gene_B': 'Receptor',
-                    'MeanLR': abs(float(np.random.normal(1.5, 0.5))) + 0.1,
-                })
-    return pd.DataFrame(rows)
+__all__ = ["SEL_COLUMNS", "make_lr_df"]
 
 
 @pytest.fixture(scope="session")
diff --git a/test/helpers.py b/test/helpers.py
new file mode 100644
index 0000000..4c28561
--- /dev/null
+++ b/test/helpers.py
@@ -0,0 +1,32 @@
+"""
+Shared test helpers for pyCrossTalkeR tests.
+"""
+import numpy as np
+import pandas as pd
+
+SEL_COLUMNS = ['source', 'target', 'gene_A', 'gene_B', 'type_gene_A', 'type_gene_B', 'MeanLR']
+
+
+def make_lr_df(seed=0):
+    """Create a minimal synthetic LR interaction DataFrame.
+
+    Uses 3 cell types and 3 LR pairs so that PCA(n_components=2) always has
+    enough samples (3 nodes) and features (Listener, Influencer, Mediator).
+    """
+    np.random.seed(seed)
+    cell_types = ['CellA', 'CellB', 'CellC']
+    lr_pairs = [('GENE_L1', 'GENE_R1'), ('GENE_L2', 'GENE_R2'), ('GENE_L3', 'GENE_R3')]
+    rows = []
+    for src in cell_types:
+        for tgt in cell_types:
+            for gl, gr in lr_pairs:
+                rows.append({
+                    'source': src,
+                    'target': tgt,
+                    'gene_A': gl,
+                    'gene_B': gr,
+                    'type_gene_A': 'Ligand',
+                    'type_gene_B': 'Receptor',
+                    'MeanLR': abs(float(np.random.normal(1.5, 0.5))) + 0.1,
+                })
+    return pd.DataFrame(rows)
diff --git a/test/test_coverage.py b/test/test_coverage.py
index a2a4adf..59ba088 100644
--- a/test/test_coverage.py
+++ b/test/test_coverage.py
@@ -36,35 +36,10 @@
 )
 
 # ---------------------------------------------------------------------------
-# Shared constants and helpers (also defined in conftest.py for fixtures)
+# Shared constants and helpers (imported from test/helpers.py)
 # ---------------------------------------------------------------------------
 
-SEL_COLUMNS = ['source', 'target', 'gene_A', 'gene_B', 'type_gene_A', 'type_gene_B', 'MeanLR']
-
-
-def make_lr_df(seed=0):
-    """Create a minimal synthetic LR interaction DataFrame.
-
-    Uses 3 cell types and 3 LR pairs so PCA(n_components=2) always succeeds
-    (3 nodes >= 2 required components).
-    """
-    np.random.seed(seed)
-    cell_types = ['CellA', 'CellB', 'CellC']
-    lr_pairs = [('GENE_L1', 'GENE_R1'), ('GENE_L2', 'GENE_R2'), ('GENE_L3', 'GENE_R3')]
-    rows = []
-    for src in cell_types:
-        for tgt in cell_types:
-            for gl, gr in lr_pairs:
-                rows.append({
-                    'source': src,
-                    'target': tgt,
-                    'gene_A': gl,
-                    'gene_B': gr,
-                    'type_gene_A': 'Ligand',
-                    'type_gene_B': 'Receptor',
-                    'MeanLR': abs(float(np.random.normal(1.5, 0.5))) + 0.1,
-                })
-    return pd.DataFrame(rows)
+from test.helpers import SEL_COLUMNS, make_lr_df  # noqa: E402
 
 
 # ---------------------------------------------------------------------------