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scproximite

sc Proximity Evaluation (scProximitE) is a framework for evaluating proximity metric performance on scRNA-seq data properties.

https://github.com/ebony-watson/scproximite

Science Score: 67.0%

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    Links to: zenodo.org
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    Low similarity (10.5%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

sc Proximity Evaluation (scProximitE) is a framework for evaluating proximity metric performance on scRNA-seq data properties.

Basic Info
  • Host: GitHub
  • Owner: Ebony-Watson
  • License: gpl-3.0
  • Language: HTML
  • Default Branch: main
  • Size: 52.2 MB
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 0
  • Open Issues: 1
  • Releases: 0
Created about 4 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License Citation

README.md

Evaluating the performance of proximity metrics for quantification of cell-cell similarity in single cell RNA-seq data

This package is designed for evaluating the performance of various proximity metrics (including distance, similarity, dissimilarity, correlation etc. metrics) with respect to quantifying cell-cell similarity in scRNA-seq datasets. The study for which the package was originally created and the performance of the metrics included in the package with respect to various dataset-specific properties of scRNA-seq data is available at https://doi.org/10.1093/bib/bbac387.

If relevant, please cite this package using the paper citation: Ebony Rose Watson, Ariane Mora, Atefeh Taherian Fard, Jessica Cara Mar, How does the structure of data impact cell–cell similarity? Evaluating how structural properties influence the performance of proximity metrics in single cell RNA-seq data, Briefings in Bioinformatics, Volume 23, Issue 6, November 2022, bbac387, https://doi.org/10.1093/bib/bbac387

Docs

Documentation and reproducibility are available at:

https://ebony-watson.github.io/scProximitE

Install

pip install scproximite

Note: scproximite was developed using Python 3.8, of you have any issues we recommend using conda and creating a new environment before installing: conda create --name scproximite python=3.8 conda activate scproximite pip install scproximite

Run tutorials

  1. Get tutorial data from zeonodo: https://zenodo.org/record/6443267 (DOI: 10.5281/zenodo.6443266)
  2. Add to the data/framework folder
  3. Run jupyter notebook in the tutorials folder

You should now be able to run the tutorial notebooks. Note if you don't have R installed you won't be able to run the notebook that uses R metrics: Proximity_Metrics_R.ipynb.

Owner

  • Login: Ebony-Watson
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Watson"
  given-names: "Ebony Rose"
- family-names: "Mora"
  given-names: "Ariane"
title: "scProximitE"
version: 1.0.0
doi: 10.5281/zenodo.6443267
date-released: 2022-04-15
url: "https://github.com/Ebony-Watson/scProximitE"

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  • AlSzmigiel (1)
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 13 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 1
  • Total maintainers: 1
pypi.org: scproximite
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 13 Last month
Rankings
Dependent packages count: 10.1%
Dependent repos count: 21.6%
Stargazers count: 27.9%
Forks count: 29.8%
Average: 34.6%
Downloads: 83.8%
Maintainers (1)
ebony_rw
Last synced: 10 months ago

Dependencies

setup.py pypi
  • genieclust *
  • jupyterlab *
  • leidenalg *
  • matplotlib *
  • natsort *
  • numpy *
  • pandas *
  • scanpy ==1.8.2
  • scikit_posthocs *
  • sciviso *
  • seaborn *
  • sklearn *
  • stats *
  • tensorflow *
  • torch *