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Author: eporetsky

tests/test_pca_analysis.py

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+import pytest
+import pandas as pd
+import numpy as np
+from mutclust.pca_analysis import calculate_eigen_genes
+
+def test_empty_cluster_error():
+    """Test that empty clusters raise an error."""
+    expression_data = pd.DataFrame({
+        'Sample1': [1.0],
+        'Sample2': [2.0]
+    }, index=['Gene1'])
+    
+    gene_clusters = [
+        ['Gene1'],
+        []  # Empty cluster
+    ]
+    
+    with pytest.raises(ValueError, match="Cannot perform PCA on empty clusters"):
+        calculate_eigen_genes(expression_data, gene_clusters)
+
+def test_single_gene_clusters():
+    """Test that single-gene clusters work correctly."""
+    expression_data = pd.DataFrame({
+        'Sample1': [1.0, 2.0],
+        'Sample2': [3.0, 4.0]
+    }, index=['Gene1', 'Gene2'])
+    
+    gene_clusters = [
+        ['Gene1'],
+        ['Gene2']
+    ]
+    
+    eigen_genes = calculate_eigen_genes(expression_data, gene_clusters)
+    
+    # Check that each cluster's values match the original expression
+    assert np.allclose(eigen_genes['Cluster_0'].values, 
+                      expression_data.loc['Gene1'].values, rtol=1e-5)
+    assert np.allclose(eigen_genes['Cluster_1'].values, 
+                      expression_data.loc['Gene2'].values, rtol=1e-5)
+
+def test_large_dataset_parallel():
+    """Test that the function can handle larger datasets in parallel."""
+    # Create a larger dataset with 100 genes and 50 samples
+    np.random.seed(42)
+    n_genes = 100
+    n_samples = 50
+    
+    # Generate random expression data
+    expression_data = pd.DataFrame(
+        np.random.randn(n_genes, n_samples),
+        index=[f'Gene{i}' for i in range(n_genes)],
+        columns=[f'Sample{i}' for i in range(n_samples)]
+    )
+    
+    # Create 10 clusters with 10 genes each
+    gene_clusters = [
+        [f'Gene{i}' for i in range(j*10, (j+1)*10)]
+        for j in range(10)
+    ]
+    
+    # Calculate eigen-genes
+    eigen_genes = calculate_eigen_genes(expression_data, gene_clusters)
+    
+    # Check the output format
+    assert isinstance(eigen_genes, pd.DataFrame)
+    assert eigen_genes.shape == (n_samples, 10)  # 50 samples, 10 clusters
+    assert all(eigen_genes.columns == [f'Cluster_{i}' for i in range(10)])
+    
+    # Check that each cluster's eigen-gene has the right dimensions
+    for i in range(10):
+        assert len(eigen_genes[f'Cluster_{i}']) == n_samples
+        # Check that the values are not all zeros
+        assert not np.allclose(eigen_genes[f'Cluster_{i}'], 0)