iVAE 0.0.5

Interpretable Variational Autoencoder for Single-Cell Data

About

iVAE is an enhanced representation learning method designed for capturing lineage features and gene expression patterns in single-cell transcriptomics. Compared to a standard VAE, iVAE incorporates a pivotal interpretative module that increases the correlation between latent components. This enhanced correlation helps the model learn gene expression patterns in single-cell data where correlations are present.

Installation

You can install the iVAE package using:

pip install iVAE

This repository is hosted at iVAE GitHub Repository.

Usage

You can customize the behavior of the script by providing additional arguments:

• --epochs: Number of training epochs (default: 1000)
• --layer: Layer to use from the AnnData object (default: 'counts')
• --percent: Percent parameter value (default: 0.01)
• --irecon: Irecon parameter value (default: 0.0)
• --beta: Beta parameter value (default: 1.0)
• --dip: Dip parameter value (default: 0.0)
• --tc: TC parameter value (default: 0.0)
• --info: Info parameter value (default: 0.0)
• --hidden_dim: Hidden dimension size (default: 128)
• --latent_dim: Latent dimension size (default: 10)
• --i_dim: i dimension size (default: 2)
• --lr: Learning rate (default: 1e-4)
• --data_path: Path to the data file (default: 'data.h5ad')
• --output_dir: Directory to save the results (default: 'iVAE_output')

Example of running with custom parameters:

iVAE --epochs 500 --layer 'counts' --data_path 'path/to/your/data.h5ad' --output_dir 'iVAE_output'

Output

After running the script, the latent space representations are saved in the specified output directory (iVAE_output by default):

• iembed.npy: Contains the output from the get_iembed() function.
• latent.npy: Contains the output from the get_latent() function.

These files are NumPy arrays that can be loaded using numpy.load() for further analysis.

Example of Loading Output Data

You can load and analyze the output data using the following Python code:

import numpy as np

# Load the iembed data
iembed = np.load('iVAE_output/iembed.npy')

# Load the latent data
latent = np.load('iVAE_output/latent.npy')

# Perform your analysis
print("iembed shape:", iembed.shape)
print("latent shape:", latent.shape)

API docs

The documentation is available. Click the doc for details.

License

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

Contact

For questions or issues, please contact Zeyu Fu at [e-mail].

---