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https://github.com/KrishnaswamyLab/TrajectoryNet

TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics
https://github.com/KrishnaswamyLab/TrajectoryNet

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TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics

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README 

        
Pytorch Implementation of TrajectoryNet

=======================================



This library runs code associated with the TrajectoryNet paper [1].



In brief, TrajectoryNet is a Continuous Normalizing Flow model which can

perform dynamic optimal transport using energy regularization and / or a

combination of velocity, density, and growth regularizations to better match

cellular trajectories. 



Our setting is similar to that of `WaddingtonOT

`_. In that we have access to a bunch of

population measurements of cells over time and would like to model the dynamics

of cells over that time period. TrajectoryNet is trained end-to-end and is

continuous both in gene space and in time.



Installation

------------



TrajectoryNet is available in `pypi`. Install by running the following



.. code-block:: bash



    pip install TrajectoryNet



This code was tested with python 3.7 and 3.8.



Example

-------



.. image:: figures/eb_high_quality.png

    :alt: EB PHATE Scatterplot

    :height: 300



.. image:: figures/EB-Trajectory.gif

    :alt: Trajectory of density over time

    :height: 300



Basic Usage

-----------



Run with



.. code-block:: bash



    python -m TrajectoryNet.main --dataset SCURVE



To run TrajectoryNet on the `S Curve` example in the paper.  To use a

custom dataset expose the coordinates and timepoint information according

to the example jupyter notebooks in the `/notebooks/` folder.



If you have an `AnnData `_ object then take a look at

`notebooks/Example_Anndata_to_TrajectoryNet.ipynb

`_,

which shows how to load one of the example `scvelo `_ anndata objects into

TrajectoryNet. Alternatively you can use the custom (compressed) format for

TrajectoryNet as described below.



For this format TrajectoryNet requires the following:



1. An embedding matrix titled `[embedding_name]` (Cells x Dimensions)

2. A sample labels array titled `sample_labels` (Cells)



To run TrajectoryNet with a custom dataset use:



.. code-block:: bash



    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}

    }