https://github.com/cytomining/copairs

Find profile pairs and compute metrics between them.

https://github.com/cytomining/copairs

Science Score: 49.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 6 DOI reference(s) in README
  • Academic publication links
  • Committers with academic emails
    2 of 7 committers (28.6%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (11.8%) to scientific vocabulary

Keywords from Contributors

cytomining
Last synced: 7 months ago · JSON representation

Repository

Find profile pairs and compute metrics between them.

Basic Info
Statistics
  • Stars: 19
  • Watchers: 6
  • Forks: 9
  • Open Issues: 14
  • Releases: 16
Created about 3 years ago · Last pushed 7 months ago
Metadata Files
Readme License

README.md

# 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: bash pip install copairs

To also install dependencies for running examples, run: bash pip install copairs[demo]

Testing

To run tests, run: bash 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 paper:

Kalinin, A.A., Arevalo, J., Serrano, E., Vulliard, L., Tsang, H., Bornholdt, M., Muñoz, A.F., Sivagurunathan, S., Rajwa, B., Carpenter, A.E., Way, G.P. and Singh, S., 2025. A versatile information retrieval framework for evaluating profile strength and similarity. Nature Communications 16, 5181. doi:10.1038/s41467-025-60306-2

BibTeX: @article{kalinin2025versatile, author = {Kalinin, Alexandr A. and Arevalo, John and Serrano, Erik and Vulliard, Loan and Tsang, Hillary and Bornholdt, Michael and Muñoz, Alán F. and Sivagurunathan, Suganya and Rajwa, Bartek and Carpenter, Anne E. and Way, Gregory P. and Singh, Shantanu}, title = {A versatile information retrieval framework for evaluating profile strength and similarity}, journal = {Nature Communications}, year = {2025}, volume = {16}, number = {1}, pages = {5181}, doi = {10.1038/s41467-025-60306-2}, url = {https://doi.org/10.1038/s41467-025-60306-2}, issn = {2041-1723} }

Owner

  • Name: cytomining
  • Login: cytomining
  • Kind: organization

GitHub Events

Total
  • Create event: 10
  • Issues event: 24
  • Release event: 6
  • Watch event: 13
  • Delete event: 3
  • Member event: 3
  • Issue comment event: 59
  • Push event: 16
  • Pull request review comment event: 28
  • Pull request event: 23
  • Pull request review event: 37
  • Fork event: 2
Last Year
  • Create event: 10
  • Issues event: 24
  • Release event: 6
  • Watch event: 13
  • Delete event: 3
  • Member event: 3
  • Issue comment event: 59
  • Push event: 16
  • Pull request review comment event: 28
  • Pull request event: 23
  • Pull request review event: 37
  • Fork event: 2

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 210
  • Total Committers: 7
  • Avg Commits per committer: 30.0
  • Development Distribution Score (DDS): 0.495
Past Year
  • Commits: 80
  • Committers: 5
  • Avg Commits per committer: 16.0
  • Development Distribution Score (DDS): 0.55
Top Committers
Name Email Commits
John Arevalo j****o@g****m 106
alxndrkalinin 1****n@u****m 71
Alan Munoz a****g@g****m 21
axiomcura e****3@g****m 6
Shantanu Singh s****h@b****g 3
Alán F. Muñoz a****o@b****g 2
jessica-ewald 3****d@u****m 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 7 months ago

All Time
  • Total issues: 47
  • Total pull requests: 61
  • Average time to close issues: 4 months
  • Average time to close pull requests: 7 days
  • Total issue authors: 9
  • Total pull request authors: 6
  • Average comments per issue: 0.79
  • Average comments per pull request: 1.23
  • Merged pull requests: 49
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 16
  • Pull requests: 27
  • Average time to close issues: 6 days
  • Average time to close pull requests: 8 days
  • Issue authors: 5
  • Pull request authors: 4
  • Average comments per issue: 0.56
  • Average comments per pull request: 2.7
  • Merged pull requests: 18
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • johnarevalo (27)
  • alxndrkalinin (9)
  • afermg (4)
  • shntnu (2)
  • axiomcura (1)
  • AMCalejandro (1)
  • ritvikvasan (1)
  • jessica-ewald (1)
  • danr (1)
Pull Request Authors
  • johnarevalo (25)
  • alxndrkalinin (15)
  • afermg (11)
  • shntnu (5)
  • jessica-ewald (4)
  • axiomcura (1)
Top Labels
Issue Labels
bug (3) enhancement (2) documentation (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 272 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 7
  • Total maintainers: 3
pypi.org: copairs

Find pairs and compute metrics between them

  • Versions: 7
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 272 Last month
Rankings
Dependent packages count: 10.4%
Average: 34.4%
Dependent repos count: 58.4%
Maintainers (3)
Last synced: 7 months ago

Dependencies

.github/workflows/python-package.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v3 composite
pyproject.toml pypi
  • pandas *
  • tqdm *