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