scintegration 0.1.1

Integration score calculator for single-cell batch correction evaluation

scintegration

Integration Score Calculator for Single-Cell Batch Correction Evaluation

scintegration is a Python package that provides a comprehensive metric for evaluating batch correction methods in single-cell RNA-seq data. It calculates an integration score that balances biology preservation against batch effect removal, helping researchers choose the best batch correction approach for their data.

Key Features

• Single Metric: One integration score that captures the tradeoff between preserving biological signal and removing batch effects
• cz-benchmarks Integration: Works seamlessly with cz-benchmarks clustering and classification tasks
• Multiple Metrics: Combines ARI, NMI, Silhouette, and F1 scores for robust evaluation
• Interpretable: Scores range from -0.5 (poor) to +0.5 (excellent) with clear interpretation
• Class Imbalance Aware: Uses F1 score (macro-averaged) to handle datasets with rare cell types

Installation

Basic Installation

pip install scintegration

With Visualization Tools

pip install scintegration[visualization]

Development Installation

# Clone the repository
git clone https://github.com/SkylarPurks/scintegration.git
cd scintegration

# Install in editable mode with dev dependencies
pip install -e .[dev]

Quick Start

from scintegration import IntegrationScoreEvaluator
from czbenchmarks.tasks import ClusteringTask, MetadataLabelPredictionTask

# Step 1: Run czbenchmarks tasks (clustering + classification)
# For biological labels (e.g., cell types)
clustering_task_bio = ClusteringTask()
clustering_results_bio = clustering_task_bio.evaluate(
    embeddings={'scvi': scvi_embeddings, 'scgpt': scgpt_embeddings},
    labels=celltype_labels
)

classification_task_bio = MetadataLabelPredictionTask()
classification_results_bio = classification_task_bio.evaluate(
    embeddings={'scvi': scvi_embeddings, 'scgpt': scgpt_embeddings},
    labels=celltype_labels
)

# For batch labels (e.g., donors)
clustering_task_batch = ClusteringTask()
clustering_results_batch = clustering_task_batch.evaluate(
    embeddings={'scvi': scvi_embeddings, 'scgpt': scgpt_embeddings},
    labels=donor_labels
)

classification_task_batch = MetadataLabelPredictionTask()
classification_results_batch = classification_task_batch.evaluate(
    embeddings={'scvi': scvi_embeddings, 'scgpt': scgpt_embeddings},
    labels=donor_labels
)

# Step 2: Calculate integration scores
evaluator = IntegrationScoreEvaluator()
results = evaluator.evaluate(
    clustering_results_biology=clustering_results_bio,
    clustering_results_batch=clustering_results_batch,
    classification_results_biology=classification_results_bio,
    classification_results_batch=classification_results_batch
)

# Step 3: Analyze results
print(results.summary())
print(f"\nBest model: {results.best_model}")
print(f"Integration Score: {results.best_score.integration_score:.4f}")
print(f"Biology preservation: {results.best_score.B:.4f}")
print(f"Batch leakage: {results.best_score.L:.4f}")

# Convert to DataFrame for further analysis
df = results.to_dataframe()
print(df)

Understanding the Integration Score

Formula

The integration score is calculated as:

IS = (B - L) / (2 × (B + L))

Where:

• B (Biology): Average of ARI, NMI, Silhouette, and F1 for biological labels (e.g., cell types)
• L (Leakage): Average of ARI, NMI, Silhouette, and F1 for batch labels (e.g., donors)

Score Interpretation

Score Range	Interpretation	Description
IS ≥ 0.2	Excellent	Strong biology preservation, minimal batch effects
0.1 ≤ IS < 0.2	Very Good	Clear biology signal over batch effects
0.05 ≤ IS < 0.1	Good	Biology preservation exceeds batch leakage
0.0 ≤ IS < 0.05	Marginal	Biology and batch effects similar
-0.1 ≤ IS < 0.0	Poor	Batch effects exceed biology signal
IS < -0.1	Very Poor	Strong batch effects, weak biology

Why This Metric?

1. Balances Two Goals: Good batch correction should preserve biology (high B) while removing batch effects (low L)
2. Single Number: Easy to compare methods and choose the best one
3. Handles Imbalance: Uses F1 score instead of accuracy, treating rare cell types fairly
4. Theory-Grounded: Based on established metrics (ARI, NMI, Silhouette, F1)

Advanced Usage

Custom Metric Weights

# Emphasize clustering over classification
evaluator = IntegrationScoreEvaluator(
    weights={
        'ari': 0.3,
        'nmi': 0.3,
        'silhouette': 0.3,
        'f1': 0.1
    }
)

Access Individual Components

results = evaluator.evaluate(...)

for model_name, score in results.scores.items():
    print(f"\n{model_name}:")
    print(f"  Integration Score: {score.integration_score:.4f}")
    print(f"  Biology (B): {score.B:.4f}")
    print(f"    - ARI: {score.biology_components['ari']:.3f}")
    print(f"    - NMI: {score.biology_components['nmi']:.3f}")
    print(f"    - Silhouette: {score.biology_components['silhouette']:.3f}")
    print(f"    - F1: {score.biology_components['f1']:.3f}")

Ranked Models

# Get models sorted by performance
for rank, (model_name, score) in enumerate(results.get_ranked_models(), 1):
    print(f"#{rank}: {model_name} (IS = {score.integration_score:.4f})")

Integrated vs Non-Integrated Baseline

from scintegration import IntegrationScoreEvaluator

evaluator = IntegrationScoreEvaluator()

comparison = evaluator.evaluate_embeddings_with_baseline(
    embeddings_by_model={
        "scvi": {
            "with_integration": scvi_integrated,
            "without_integration": scvi_non_integrated,  # optional
        },
        "scgpt": {
            "with_integration": scgpt_integrated,
            # missing without_integration -> optional PCA fallback
        },
    },
    obs=adata.obs,
    biology_labels=celltype_labels,
    batch_labels=donor_labels,
    raw_features=adata.X,  # used only when baseline is missing
    use_pca_baseline_when_missing=True,
)

print(comparison.summary())
df = comparison.to_dataframe()

For each model, deltas are computed as:

• delta_integration_score = IS_with_integration - IS_without_integration
• delta_B = B_with_integration - B_without_integration
• delta_L = L_with_integration - L_without_integration

Interpretation:

• Positive delta_integration_score means integration improved overall score.
• Positive delta_B means better biology preservation.
• Negative delta_L means lower batch leakage (better batch removal).

Example Results

From a real dataset with 8,045 cells, 25 cell types (extreme imbalance: 2003:1), and 3 donors:

================================================================================
INTEGRATION SCORE RESULTS
================================================================================

#1 SCVI:
  Integration Score: +0.0702
  Biology (B):       0.6970
  Leakage (L):       0.5597
    Biology metrics: ARI=0.732, NMI=0.785, Sil=0.717, F1=0.554
    Batch metrics:   ARI=0.605, NMI=0.657, Sil=0.564, F1=0.413

#2 SCGPT:
  Integration Score: +0.0533
  Biology (B):       0.6822
  Leakage (L):       0.5764
    Biology metrics: ARI=0.720, NMI=0.774, Sil=0.699, F1=0.536
    Batch metrics:   ARI=0.620, NMI=0.672, Sil=0.577, F1=0.437

================================================================================
 BEST MODEL: SCVI (IS = +0.0702)
================================================================================

Interpretation: scVI achieves the best integration with ~70% biology preservation and ~56% batch leakage, resulting in a positive integration score indicating good batch correction.

API Reference

Main Classes

• IntegrationScoreEvaluator: Main API for calculating scores
• IntegrationScoreResults: Container for results with helper methods
• ModelScore: Individual model's score with components

Core Functions

• calculate_integration_score(B, L): Core formula
• compute_B_score(...): Biology preservation score
• compute_L_score(...): Batch leakage score
• normalize_silhouette(sil_score): Convert silhouette from [-1,1] to [0,1]

Utility Functions

• interpret_integration_score(score): Get qualitative interpretation
• batch_effect_percentage(L): Convert L to percentage
• biology_preservation_percentage(B): Convert B to percentage

Citation

If you use this package in your research, please cite:

@software{scintegration2026,
  author = {SkylarPurks},
  title = {scintegration: Integration Score Calculator for Single-Cell Batch Correction},
  year = {2026},
  url = {https://github.com/SkylarPurks/scintegration}
}

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes with tests
4. Run tests (pytest)
5. Format code (black .)
6. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Built on top of czbenchmarks
• Inspired by integration metrics from the single-cell community
• Uses established metrics: ARI, NMI, Silhouette coefficient, F1 score

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