Data

Tools

Indexes

Applications

Experiments

Open Source Science

emergent_laws_in_scrna-seq_data

Code to reproduce the statistical data analysis of Mouse Cell Atlas and Tabula Muris compendium proposed in the paper "Emergent statistical laws in single-cell transcriptomic data" .

https://github.com/silvialazzardi/emergent_laws_in_scrna-seq_data

Science Score: 67.0%

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

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 5 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: zenodo.org
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (8.2%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Code to reproduce the statistical data analysis of Mouse Cell Atlas and Tabula Muris compendium proposed in the paper "Emergent statistical laws in single-cell transcriptomic data" .

Basic Info
  • Host: GitHub
  • Owner: SilviaLazzardi
  • License: gpl-3.0
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 73.1 MB
Statistics
  • Stars: 0
  • Watchers: 2
  • Forks: 2
  • Open Issues: 0
  • Releases: 0
Created almost 6 years ago · Last pushed almost 3 years ago
Metadata Files
Readme License Citation

Readme.md

CI DOI code

graph abstact

Emergent statistical laws in single-cell transcriptomic data

DOI

Code to reproduce the statistical data analysis of Mouse Cell Atlas and Tabula Muris compendium proposed in the paper "Emergent statistical laws in single-cell transcriptomic data".

Analyses

Tabula Muris

It contains a notebook where is available the code used to analyze the Tabula Muris transcriptomic data. TabulaMurisData_Analysis.ipynb

Mouse Cell Atlas

In this folder it is possible to reproduce all the analyses involving Mouse Cell Atlas dataset.

Moreover running combined_analyses.ipynb it is possible to reproduce some analyses comparing different datasets as discussed in the paper.

Additional Tools

Download the data

Part of the data and results in this repository are stored using Data Version Control dvc tool.

It is possible to retrieve the data running bash dvc pull -r mydrive

Run in a Docker container

It is possible to run all the notebooks in this repository in a controlled container simply running

bash cd docker docker-compose up -d

and then pointing a browser to localhost

Paper

S. Lazzardi, F. Valle, A. Mazzolini, A. Scialdone, M. Caselle and M. Osella, Emergent statistical laws in single-cell transcriptomic data, Physical Review E (2023)

License

See LICENSE

Owner

  • Login: SilviaLazzardi
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Lazzardi"
  given-names: "Silvia"
  orcid: "http://orcid.org/0000-0001-8058-0065"
- family-names: "Valle"
  given-names: "Filippo"
  orcid: "http://orcid.org/0000-0003-3577-8667"
title: BioPhys-Turin/Emergent_Laws_in_scRNA-seq_Data 
version: first_release
date-released: 2017-12-18
doi: 10.5281/zenodo.6302674
url: "https://zenodo.org/record/6302674"

GitHub Events

Total
Last Year

Dependencies

docker/requirements.txt pypi
  • watermark *
.github/workflows/main.yml actions
  • actions/checkout v2 composite
docker/Dockerfile docker
  • docker.io/jupyter/datascience-notebook latest build
docker/docker-compose.yml docker
  • statistics_sc latest