Metadata-Version: 2.1
Name: singleCellHaystack
Version: 0.0.4
Summary: An implementation of [singleCellHaystack](https://github.com/alexisvdb/singleCellHaystack) in python.
Author-email: Diego Diez <diego10ruiz@gmail.com>
License: MIT License
        
        Copyright (c) 2022 Diego Diez
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Project-URL: Homepage, https://github.com/ddiez/singleCellHaystack-py
Project-URL: Bug Tracker, https://github.com/ddiez/singleCellHaystack-py/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Intended Audience :: Science/Research
Classifier: Development Status :: 4 - Beta
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: anndata
Requires-Dist: scanpy
Requires-Dist: pandas
Requires-Dist: tqdm

singleCellHaystack
==================

[![Lifecycle:beta](https://img.shields.io/badge/lifecycle-beta-orange.svg)](https://github.com/ddiez/singleCellHaystack-py)
[![](https://github.com/ddiez/singleCellHaystack-py/actions/workflows/python-package.yml/badge.svg)](https://github.com/ddiez/singleCellHaystack-py/actions/workflows/python-package.yml)
[![PyPI](https://img.shields.io/pypi/v/singleCellHaystack?logo=PyPI)](https://pypi.org/project/singleCellHaystack)
[![PyPIDownloads](https://pepy.tech/badge/singleCellHaystack)](https://pepy.tech/project/singleCellHaystack)

This repository contains a python implementation of [singleCellHaystack](https://github.com/alexisvdb/singleCellHaystack) (version >= 1.0.0).

This package is currently in beta. The most important functionality in the R package works, but some features are not yet available. Here is a (probably imcomplete) list of missing features. Some will be added in the future.

* `weights.advanced.Q` (formerly known as `use.advanced.sampling`).
* `seeding` method for calculating grid points.

# Installation

You can install singleCellHaystack from [pypi](https://pypi.org):

```
pip install singleCellHaystack
```

>Support for conda installation will be added in the future.

# Example

```
import scanpy as sc
import singleCellHaystack as hs

adata = sc.read_h5ad("data.h5ad")

[... process adata object ...]

res = hs.haystack(adata, coord="pca")
res["results"]
```

# References

- Our manuscript describing the updated, more generally applicable version of `singleCellHaystack` inclusing this python implementation is available on [bioRxiv](https://www.biorxiv.org/content/10.1101/2022.11.13.516355v1).

- Our manuscript describing the original implementation of `singleCellHaystack` for R ([version 0.3.4](https://github.com/alexisvdb/singleCellHaystack/tree/binary)) was published in [Nature Communications](https://doi.org/10.1038/s41467-020-17900-3).

If you use `singleCellHaystack` in your research please cite our work using:

Vandenbon A, Diez D (2020). “A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data.” *Nature Communications*, *11*(1), 4318. [doi:10.1038/s41467-020-17900-3](https://doi.org/10.1038/s41467-020-17900-3).
