Metadata-Version: 2.1
Name: celescope
Version: 1.6.0
Summary: Single Cell Analysis Pipelines
Home-page: https://github.com/singleron-RD/CeleScope
Author: zhouyiqi
Author-email: zhouyiqi@singleronbio.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: cutadapt (==1.17)
Requires-Dist: pysam (==0.16.0.1)
Requires-Dist: scipy (==1.5.0)
Requires-Dist: numpy (==1.19.5)
Requires-Dist: jinja2 (>=2.10)
Requires-Dist: xopen (>=0.5.0)
Requires-Dist: editdistance (>=0.5.3)
Requires-Dist: sklearn (==0.0)
Requires-Dist: plotly (==4.14.3)
Requires-Dist: plotnine (==0.8.0)
Requires-Dist: matplotlib (==3.3.0)
Requires-Dist: cython
Requires-Dist: pytest
Requires-Dist: venn
Requires-Dist: pyranges


# CeleScope
CeleScope is a collection of bioinfomatics analysis pipelines developed at Singleron to process single cell sequencing data generated with Singleron products. These pipelines take paired-end FASTQ files as input and generate output files which can be used for downstream data analysis as well as a summary of QC criteria.

Detailed docs can be found in [manual](./docs/manual.md).

## Hardware/Software Requirements

- minimum 32GB RAM(to run STAR aligner)
- conda
- git

## Installation

1. Clone repo
```
git clone https://github.com/singleron-RD/CeleScope.git
```

2. Create conda environment and install conda packages
```
cd CeleScope
conda create -n celescope -y --file conda_pkgs.txt
```

Alternatively, you can use [mamba](https://github.com/mamba-org/mamba) to improve speed.
```
conda install mamba
mamba create -n celescope -y --file conda_pkgs.txt
```

3. Install celescope

Make sure you have activated the `celescope` conda environment before running `pip install celescope`. 
```
conda activate celescope
pip install celescope
```

## [Quick start](./docs/quick_start.md)




