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.
We provide some example notebooks. You could start working with VITAE on tutorial_dentate.
|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.
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
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:
This project is licensed under the terms of the MIT license.