Model-based Trajectory Inference for Single-Cell RNA Sequencing 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.

notebook	system	details	reference
tutorial_dentate	neurons	3585 cells and 2182 genes, 10x Genomics	Hochgerner et al. (2018)
tutorial_mouse_brain	neurons	16651 cells and 14707 genes	Yuzwa et al. (2017), Ruan et al. (2021)

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 PyPI and the user can install the CPU version with the following command. To enable GPU for Tensorflow, one should install CUDA dependencies and tensorflow-gpu package. We also recommend to use conda, miniconda or virtualenv to manage Python environment and install the package in a new environment.

>> pip install pyvitae

After installing all required packages, one can open the Jupyter Notebook via terminal:

>>> jupyter notebook

The required tensorflow versions are:

Package	Version
tensorflow	>=2.3.0
tensorflow_probability	>=0.11.0

License

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