Skip to main content

A python scRNA-Seq classifier

Project description

PyPI Docs Build Status bioconda

PyPairs - A python scRNA-Seq classifier

This is a python-reimplementation of the Pairs algorithm as described by A. Scialdone et. al. (2015). Original Paper available under: https://doi.org/10.1016/j.ymeth.2015.06.021

A supervided maschine learning algorithm aiming to classify single cells based on their transcriptomic signal. Initially created to predict cell cycle phase from scRNA-Seq data, this algorithm can be used for various applications.

Build to be fully compatible with Scanpy. For more details see the full documentation.

Getting Started

Note: Version 3 still under development.

Installation

This package is hosted at PyPi ( https://pypi.org/project/pypairs/ ) and can be installed on any system running Python3 via pip with:

pip install pypairs

Alternatively, pypairs can be installed using Conda (most easily obtained via the Miniconda Python distribution:

conda install -c bioconda pypairs

Minimal Example

Assuming you have two scRNA count files (csv, columns = samples, rows = genes) and one annotation file (csv, no header, two rows: “gene, class”) a minimal example would look like this

from pypairs import pairs, datasets

# Load samples from the oscope scRNA-Seq dataset with known cell cycle
training_data = datasets.leng15(mode='sorted')

# Run sandbag() to identify marker pairs
marker_pairs = pairs.sandbag(training_data, fraction=0.6)

# Load samples from the oscope scRNA-Seq dataset without known cell cycle
testing_data = datasets.leng15(mode='unsorted')

# Run cyclone() score and predict cell cycle classes
result = pairs.cyclone(testing_data, marker_pairs)

# Further downstream analysis
print(result)

Core Dependencies

Authors

  • Antonio Scialdone - original algorithm
  • Ron Fechtner - implementation and extension in Python

License

This project is licensed under the BSD 3-Clause License - see the LICENSE file for details

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for pypairs, version 3.0.5
Filename, size File type Python version Upload date Hashes
Filename, size pypairs-3.0.5.tar.gz (39.9 MB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page