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Joint Trajectory Inference for Single-cell Genomics Using Deep Learning with a Mixture Prior

This is a Python package, VITAE, to perform trajectory inference for single-cell RNA sequencing (scRNA-seq). VITAE is a probabilistic method combining a latent hierarchical mixture model with variational autoencoders to infer trajectories from posterior approximations. VITAE is computationally scalable and can adjust for confounding covariates to learn a shared trajectory from multiple datasets. VITAE also provides uncertainty quantification of the inferred trajectory and cell positions and can find differentially expressed genes along the trajectory. For more information, please check out our manuscript on bioRXiv.

Tutorials

We provide some example notebooks. You could start working with VITAE on tutorial_dentate.

In case GitHub rendering stops working, NbViewer is an alternative online tool to render Jupyter Notebooks.

Datasets and Documents are availble.

Dependency

Our Python package is available on conda-forge and PyPI and the user can install the CPU version with the following command:

# using conda with conda-forge channel
>>> conda install -c conda-forge pyvitae

# or using PyPI
>>> pip install pyvitae

To enable GPU for TensorFlow, one should install CUDA dependencies and the tensorflow-gpu package. We also recommend using conda, miniconda, or virtualenv to manage the Python environment and install the package in a new environment. After installing all required packages, one can open the Jupyter Notebook via the terminal:

>>> jupyter notebook

The required TensorFlow versions are:

License

This project is licensed under the terms of the MIT license.