# TIGON

**Repository Path**: yongdong323/TIGON

## Basic Information

- **Project Name**: TIGON
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-11-04
- **Last Updated**: 2024-11-04

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# TIGON 
**T**rajectory **I**nference with **G**rowth via **O**ptimal transport and **N**eural network
![Doc/overview.jpg](https://github.com/yutongo/TIGON/blob/main/Doc/overview.jpg)

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10005456.svg)](https://doi.org/10.5281/zenodo.10005456)

TIGON is a dynamic unbalanced optimal transport (OT) model that reconstructs dynamic trajectories and population growth simultaneously as well as the underlying
gene regulatory network (GRN) from time-series scRNA-seq snapshots.


# Requirements
The training framework is implemented in PyTorch and Neural ODEs. Given a stable internet connection, it will take several minutes to install these packages:
* pytorch 1.13.1
* scipy 1.10.1
* [TorchDiffEqPack](https://jzkay12.github.io/TorchDiffEqPack/TorchDiffEqPack.odesolver.html) 1.0.1
* [torchdiffeq](https://github.com/rtqichen/torchdiffeq) 0.2.3
* Recommended: An Nvidia GPU with CUDA support for GPU acceleration


For generating plots to visualize results, the required packages are listed: 
* numpy 1.23.5
* seaborn 0.12.2
* matplotlib 3.5.3


# Input Files
`$Dataset.npy`: data coordinates from different time points. One simulation and three single-cell datasets used for TIGON paper are provided in folder `Input/`. 


# Usage
### Inputs:
`--dataset` Name of the data set. Options: EMT; Lineage; Bifurcation; Simulation, default = 'Simulation'. \
`--input_dir` Input Files Directory, default='Input/'. \
`--save_dir` Output Files Directory, default='Output/'. \
`--timepoints` Time points of data. \
`--niters` Number of traning iterations. \
`--lr` Learning rate. \
`--num_samples` Number of sampling points per epoch. \
`--hidden_dim` Dimension of hidden layer. \
`--n_hiddens` Number of hidden layers for the neural network learning velocity. 

### Outputs:
`ckpt.pth`: save model’s parameters and training errors.

### Examples:
Training process: `python3 TIGON.py`

Visualization of results: `python3 plot_result.py`

### Tutorials:
A Jupyter Notebook of the step-by-step tutorial is accessible from :

Training process: `Notebooks/Training.ipynb`

Visualization of results: `Notebooks/Visualization.ipynb`


# Sources
## Lineage tracing dataset
Single-cell lineage tracing dataset (raw data of `Lineage.npy`) can be obtained from: [Weinreb, Caleb, et al. "Lineage tracing on transcriptional landscapes links state to fate during differentiation." Science 367.6479 (2020): eaaw3381.](https://www.science.org/doi/full/10.1126/science.aaw3381?casa_token=cmaoSgI9KNQAAAAA%3Ah7lDBD7kPIfZDBTlYDHy9RPVHjX811LOPfxDitvbLiAugMxB1UUWvqMTtzKL4hU3oKdbyfBCw7mmIA)
## EMT dataset
TGFB1 induced EMT from A549 cancer cell line dataset (raw data of `EMT.npy`) can be obtained from: [Cook, David P., and Barbara C. Vanderhyden. "Context specificity of the EMT transcriptional response." Nature communications 11.1 (2020): 1-9.](https://www.nature.com/articles/s41467-020-16066-2)
## Bifurcation dataset
Single-cell qPCR dataset of iPSCs toward cardiomyocytes dataset (raw data of `Bifurcation.npy`) can be obtained from: [Bargaje, Rhishikesh, et al. "Cell population structure prior to bifurcation predicts efficiency of directed differentiation in human induced pluripotent cells." Proceedings of the National Academy of Sciences 114.9 (2017): 2271-2276.](https://www.pnas.org/doi/abs/10.1073/pnas.1621412114)
# Reference
Sha, Y., Qiu, Y., Zhou, P., & Nie, Q. (2023). Reconstructing growth and dynamic trajectories from single-cell transcriptomics data. _Nature Machine Intelligence._ https://www.nature.com/articles/s42256-023-00763-w#