gene-trajectory 1.0.4
Compute gene trajectories
Requires Python: >=3.9
Keywords: Gene trajectory, scRNA-seq

# Python Gene Trajectory
This package is a Python implementation of GeneTrajectory. 
The method is described in detail in the article [
Gene trajectory inference for single-cell data by optimal transport metrics](
https://doi.org/10.1038/s41587-024-02186-3).

Documentation and tutorials are available at https://genetrajectory-python.readthedocs.io
For the R implementation, go to the [GeneTrajectory](https://github.com/KlugerLab/GeneTrajectory) project. 


Note that, although the implementation is equivalent, it will produce slightly different results to the R implementation
because the signs of eigenvectors may differ and because of the randomness of K-means during the `coarse_grain` step. 


# Install #
The main version of the package can be installed as 
```
pip install gene-trajectory
```

If you are planning to run the tool in [Jupyter Notebook](https://jupyter.org/), the additional dependencies can be 
installed as
```
pip install 'gene-trajectory[widgets]'
```

The development version of the package can be installed as 
```
pip install git+https://github.com/Klugerlab/GeneTrajectory-python.git
```

# Tutorials #
There are tutorials in Jupyter Notebook format in the [online documentation](https://genetrajectory-python.readthedocs.io) 
and the [notebooks](https://github.com/KlugerLab/GeneTrajectory-python/tree/main/notebooks) folder of the GitHub project. 


# How to cite Gene Trajectory #
If you use this tool in your research and find it useful, you can cite the following reference from our paper
[Gene trajectory inference for single-cell data by optimal transport metrics](https://doi.org/10.1038/s41587-024-02186-3).
In Bibtex format:
```bibtex

@article{qu_gene_2024,
	title = {Gene trajectory inference for single-cell data by optimal transport metrics},
	issn = {1546-1696},
	url = {https://doi.org/10.1038/s41587-024-02186-3},
	doi = {10.1038/s41587-024-02186-3},
	abstract = {Single-cell RNA sequencing has been widely used to investigate cell state transitions and gene dynamics of biological processes. Current strategies to infer the sequential dynamics of genes in a process typically rely on constructing cell pseudotime through cell trajectory inference. However, the presence of concurrent gene processes in the same group of cells and technical noise can obscure the true progression of the processes studied. To address this challenge, we present GeneTrajectory, an approach that identifies trajectories of genes rather than trajectories of cells. Specifically, optimal transport distances are calculated between gene distributions across the cell–cell graph to extract gene programs and define their gene pseudotemporal order. Here we demonstrate that GeneTrajectory accurately extracts progressive gene dynamics in myeloid lineage maturation. Moreover, we show that GeneTrajectory deconvolves key gene programs underlying mouse skin hair follicle dermal condensate differentiation that could not be resolved by cell trajectory approaches. GeneTrajectory facilitates the discovery of gene programs that control the changes and activities of biological processes.},
	journal = {Nature Biotechnology},
	author = {Qu, Rihao and Cheng, Xiuyuan and Sefik, Esen and Stanley III, Jay S. and Landa, Boris and Strino, Francesco and Platt, Sarah and Garritano, James and Odell, Ian D. and Coifman, Ronald and Flavell, Richard A. and Myung, Peggy and Kluger, Yuval},
	month = apr,
	year = {2024},
}
```