Metadata-Version: 2.1
Name: sctour
Version: 1.0.0
Summary: a deep learning architecture for robust inference and accurate prediction of cellular dynamics
Home-page: https://github.com/LiQian-XC/sctour
Author: Qian Li
Author-email: liqian.picb@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Development Status :: 4 - Beta
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Requires-Dist: torch (>=1.9.1)
Requires-Dist: torchdiffeq (>=0.2.2)
Requires-Dist: numpy (>=1.19.2)
Requires-Dist: scanpy (>=1.7.1)
Requires-Dist: anndata (>=0.7.5)
Requires-Dist: scipy (>=1.5.2)
Requires-Dist: tqdm (>=4.32.2)
Requires-Dist: scikit-learn (>=0.24.1)
Requires-Dist: leidenalg (>=0.8.4)


# scTour

<img src="https://github.com/LiQian-XC/sctour/blob/main/docs/source/_static/img/scTour_head_image.png" width="400px" align="left">

scTour is an innovative and comprehensive method for dissecting cellular dynamics by analysing datasets derived from single-cell genomics.

It provides a unifying framework to depict the full picture of developmental processes from multiple angles including the developmental pseudotime, vector field and latent space.

It further generalises these functionalities to a multi-task architecture for within-dataset inference and cross-dataset prediction of cellular dynamics in a batch-insensitive manner.  

## Key features

- cell pseudotime estimation with no need for specifying starting cells.
- transcriptomic vector field inference with no discrimination between spliced and unspliced mRNAs.
- latent space mapping by combining intrinsic transcriptomic structure with extrinsic pseudotime ordering.
- model-based prediction of pseudotime, vector field, and latent space for query cells/datasets/time intervals.
- insensitive to batch effects; robust to cell subsampling; scalable to large datasets.

## Installation

[![Python Versions](https://img.shields.io/badge/python-3.7+-brightgreen.svg)](https://pypi.org/project/sctour)

```console
pip install sctour
```

## Documentation

[![Documentation Status](https://readthedocs.org/projects/sctour/badge/?version=latest)](https://sctour.readthedocs.io/en/latest/?badge=latest)

Full documentation can be found [here](https://sctour.readthedocs.io/en/latest/).

## Reference

[Qian Li, scTour: a deep learning architecture for robust inference and accurate prediction of cellular dynamics. Genome Biology, 2023](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-023-02988-9)


