Metadata-Version: 2.1
Name: scltnn
Version: 0.0.7
Summary: A library to calculate the latent time of scRNA-seq
Home-page: https://github.com/Starlitnightly/scltnn
Author: starlitnightly
Author-email: starlitnightly@163.com
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: scanpy (>=1.9)
Requires-Dist: keras (>=2.8)
Requires-Dist: numpy (>=1.22)
Requires-Dist: pandas (>=1.4)
Requires-Dist: distfit

# scLTNN (single cell latent time neuron network)

[![Lifecycle:maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing) [![License:GPL](https://img.shields.io/badge/license-GPL-blue)](https://img.shields.io/apm/l/vim-mode) [![pypi-badge](https://img.shields.io/pypi/v/scltnn)](https://pypi.org/project/scltnn) [![bulid:passing](https://img.shields.io/appveyor/build/gruntjs/grunt)](https://img.shields.io/appveyor/build/gruntjs/grunt) [![Documentation Status](https://readthedocs.org/projects/scltnn/badge/?version=latest)](https://scltnn.readthedocs.io/en/latest/?badge=latest)

**A composite regression neural network for latent timing prediction of single-cell RNA-seq data**

[![ltnn](ltnn.png)](ltnn.png)

For more details, please check out our [publication]().

## Directory structure

````
.
├── scltnn                  # Main Python package
├── experiments             # Experiments and case studies
├── scltnn                  # the raw code of scltnn
├── model                   # the pre-model by ANN
├── source                  # Documentation files
├── LICENSE
└── README.md
````

## Installation

The `scLTNN` package can be installed via pip: 

```
pip install scltnn
```

## Usage

Please checkout the documentations and tutorials at [scltnn.readthedocs.io](https://scltnn.readthedocs.io/en/latest/index.html).

## Reproduce results

1. Follow instructions in `data` to prepare the necessary data, it can be download at https://figshare.com/articles/dataset/scltnn_data/20383416
2. Follow instructions in `experiments` for case studies

## Contact

- Zehua Zeng ([starlitnightly@163.com](mailto:starlitnightly@163.com))

