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 supervised 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.
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
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)- Antonio Scialdone - original algorithm
- Ron Fechtner - implementation and extension in Python
This project is licensed under the BSD 3-Clause License - see the LICENSE file for details