Pytorch Implementation of TrajectoryNet

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 SCURVE

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:

1. An embedding matrix titled [embedding_name] (Cells x Dimensions)
2. A sample labels array titled sample_labels (Cells)
3. (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

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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}
}