A neural ode solution for imputing trajectories between pointclouds.
Project description
This library runs code associated with the TrajectoryNet paper [1].
Installation
TrajectoryNet is available in pypi. Install by running the following
pip install TrajectoryNet
This code was tested with python 3.7 and 3.8.
Example
Basic Usage
Run with
python -m TrajectoryNet.main --dataset EB
To use a custom dataset expose the coordinates and timepoint information according to the example jupyter notebooks in the /notebooks/ folder.
TrajectoryNet requires the following:
An embedding matrix titled [embedding_name] (Cells x Dimensions)
A sample labels array titled sample_labels (Cells)
(Optionally) a delta embedding representing RNA velocity titled delta_[embedding_name] (Cells x Dimensions)
To run TrajectoryNet with a custom dataset use:
python -m TrajectoryNet.main --dataset [PATH_TO_NPZ_FILE] --embedding_name [EMBEDDING_NAME]
python -m TrajectoryNet.eval --dataset [PATH_TO_NPZ_FILE] --embedding_name [EMBEDDING_NAME]
See notebooks/EB-Eval.ipynb for an example on how to use TrajectoryNet on a PCA embedding to get trajectories in the gene space.
References
[1] Tong, A., Huang, J., Wolf, G., van Dijk, D., and Krishnaswamy, S. TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics. In International Conference on Machine Learning, 2020. arxiv ICML
—
If you found this library useful, please consider citing:
@inproceedings{tong2020trajectorynet, title = {TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics}, shorttitle = {{{TrajectoryNet}}}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, author = {Tong, Alexander and Huang, Jessie and Wolf, Guy and {van Dijk}, David and Krishnaswamy, Smita}, year = {2020} }
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