scgraph-eval 0.1.1

A tool for evalauting single-cell embeddings using graph-based relations

scgraph-eval

A tool for evaluating single-cell embeddings using graph-based relationships. This package helps analyze the consistency of cell type relationships across different batches in single-cell data.

Features

• Calculate trimmed means for cell type centroids
• Compute pairwise distances between cell types
• Process multiple batches to assess embedding consistency
• Support for both PCA and custom embeddings
• Built-in handling for highly variable genes (HVG)

Installation

You can install the package via pip:

pip install scgraph-eval

Usage

Python API

from scgraph import scGraph

# Initialize the graph analyzer
graph = scGraph(
    adata_path="path/to/your/data.h5ad",   # Path to AnnData object
    batch_key="batch",                     # Column name for batch information
    label_key="cell_type",                 # Column name for cell type labels
    trim_rate=0.05,                        # Trim rate for robust mean calculation
    thres_batch=100,                       # Minimum number of cells per batch
    thres_celltype=10                      # Minimum number of cells per cell type
)

# Run the analysis
results = graph.main()

# Save results
results.to_csv("embedding_evaluation_results.csv")

Command Line Interface

scgraph-eval --adata_path path/to/data.h5ad \
             --batch_key batch \
             --label_key cell_type \
             --trim_rate 0.05 \
             --thres_batch 100 \
             --thres_celltype 10 \
             --savename results

Output

The package outputs comparison metrics between different embeddings:

• Rank-PCA: Spearman correlation with PCA-based relationships
• Corr-PCA: Pearson correlation with PCA-based relationships
• Corr-Weighted: Weighted correlation considering distance-based importance

Requirements

• numpy
• pandas
• scanpy
• tqdm
• scipy

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

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

Citation

If you use this package in your research, please cite: [Citation information to be added]

Contact

For questions and feedback:

• Hanchen Wang
• Email: [e-mail]