sdevelo 0.2.5

SDEvelo: a deep generative approach for transcriptional dynamics with cell-specific latent time and multivariate stochastic modeling

SDEvelo: a deep generative approach for transcriptional dynamics with cell-specific latent time and multivariate stochastic modeling

Overview

sdevelo leverages advanced stochastic differential equations (SDE) to provide a novel approach to RNA velocity analysis in single-cell RNA sequencing (scRNA-seq). This deep generative model accurately captures the complex, stochastic nature of transcriptional dynamics, offering new insights into cell differentiation and state transitions.

System Requirements

• Operating Systems: Linux (Ubuntu, CentOS), macOS, Windows 10.
• Python Version: Python 3.8 and above.
• Dependencies: anndata==0.10.7 matplotlib==3.7.1 numpy==1.23.5 scipy==1.8.1 scvelo>=0.3.0 seaborn==0.11.2 torch==1.13.1+cu117
• Hardware Requirements: No non-standard hardware required.
• Installation Time: sdevelo's installation should be completed within approximately 5 minutes.

Installation Guide

• Step 1: Ensure Python 3.8+ is installed on your system.

• Step 2: Install sdevelo via pip:

  pip install sdevelo
  

Demo

Experience the power of SDEvelo through our interactive demo provided as a Jupyter Notebook.

Running the Demo

1. Navigate to the docs/demo_simulation directory within this repository.
2. Locate the Jupyter Notebook titled demo_simulation.ipynb.
3. Open the notebook in Jupyter Lab or Jupyter Notebook and execute the cells in order.

Expected Output

By running the demo, you will generate:

• A streamline plot depicting the transcriptional dynamics.
• A latent time heatmap that visualizes the progression of cells over time.

Expected Run Time

On a typical desktop computer, the demo should complete within approximately 300 seconds.

Future Demos

We continuously strive to enhance SDEvelo. Stay tuned for additional demos by checking our repository for updates.

Instruction to use

• Step 1: Configure the arguments and parameters for your dataset. Refer to the provided demo_simulation.ipynb for examples of data configuration, model execution, and visualization.
• Step 2: Run the SDEvelo model.
• Step 3: Visualize the results based on the estimated SDEvelo model.