copairs 0.4.4

Find pairs and compute metrics between them

copairs

copairs is a Python package for finding groups of profiles based on metadata and calculate mean Average Precision to assess intra- vs inter-group similarities.

Getting started

System requirements

copairs supports Python 3.8+ and should work with all modern operating systems (tested with MacOS 13.5, Ubuntu 18.04, Windows 10).

Dependencies

copairs depends on widely used Python packages:

• numpy
• pandas
• tqdm
• statsmodels
• [optional] plotly

Installation

To install copairs and dependencies, run:

pip install copairs

To also install dependencies for running examples, run:

pip install copairs[demo]

Testing

To run tests, run:

pip install -e .[test]
pytest

Usage

We provide examples demonstrating how to use copairs for:

• grouping profiles based on their metadata
• calculating mAP to assess phenotypic activity of perturbations
• calculating mAP to assess phenotypic consistency of perturbations
• estimating null size for mAP p-value calculation

Citation

If you find this work useful for your research, please cite our pre-print:

Kalinin, A.A., Arevalo, J., Vulliard, L., Serrano, E., Tsang, H., Bornholdt, M., Rajwa, B., Carpenter, A.E., Way, G.P. and Singh, S., 2024. A versatile information retrieval framework for evaluating profile strength and similarity. bioRxiv, pp.2024-04. doi:10.1101/2024.04.01.587631

BibTeX:

@article{kalinin2024versatile,
  title={A versatile information retrieval framework for evaluating profile strength and similarity},
  author={Kalinin, Alexandr A and Arevalo, John and Vulliard, Loan and Serrano, Erik and Tsang, Hillary and Bornholdt, Michael and Rajwa, Bartek and Carpenter, Anne E and Way, Gregory P and Singh, Shantanu},
  journal={bioRxiv},
  pages={2024--04},
  year={2024},
  doi={10.1101/2024.04.01.587631}
}