scgraph-eval 0.1.2

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)
• Option to analyze only 2D embeddings (like UMAP or t-SNE)

Installation

You can install the package via pip:

pip install scgraph-eval

Usage

Python API

from scgraph import scGraph

# Initialize the graph analyzer
scgraph = 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
    only_umap=True,                        # Only evaluate 2D embeddings (mostly umaps)
)

# Run the analysis, return a pandas dataframe
results = scgraph.main()

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

Command Line Interface

scgraph --adata_path path/to/data.h5ad --batch_key batch --label_key cell_type --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

How It Works

1. For each batch, the tool calculates centroids for each cell type in the embedding space
2. Pairwise distances between cell types are computed to establish relationship graphs
3. A consensus relationship graph is derived by combining information across all batches
4. Each embedding is evaluated by comparing its cell type relationships to the consensus

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:

@article{wang2024metric, title={Metric mirages in cell embeddings}, author={Wang, Hanchen and Leskovec, Jure and Regev, Aviv}, journal={BioRxiv}, pages={2024--04}, year={2024}, publisher={Cold Spring Harbor Laboratory} }

Contact

For questions and feedback:

• Hanchen Wang
• Email: [e-mail]