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sctenifoldpy

A python package implements scTenifoldnet and scTenifoldknk

https://github.com/qwerty239qwe/sctenifoldpy

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 1 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: sciencedirect.com
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (10.1%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

A python package implements scTenifoldnet and scTenifoldknk

Basic Info
Statistics
  • Stars: 9
  • Watchers: 4
  • Forks: 4
  • Open Issues: 8
  • Releases: 1
Created almost 5 years ago · Last pushed over 3 years ago
Metadata Files
Readme License Citation

README.md

scTenifoldpy

PyPI pyversions Pattern GitHub license

This package is a Python version of scTenifoldNet and scTenifoldKnk. If you are a R/MATLAB user, please install them to use their functions. Also, please cite the original paper properly if you are using this in a scientific publication. Thank you!

Installation

pip install scTenifoldpy

Usages

scTenifold can be imported as a normal Python package:

scTenifoldNet

```python from scTenifold.data import gettestdf from scTenifold import scTenifoldNet

df1, df2 = gettestdf(ncells=1000), gettestdf(ncells=1000) sc = scTenifoldNet(df1, df2, "X", "Y", qckws={"minlib_size": 10}) result = sc.build() ```

scTenifoldKnk

```python from scTenifold.data import gettestdf from scTenifold import scTenifoldKnk

df = gettestdf(ncells=1000) sc = scTenifoldKnk(data=df, komethod="default", kogenes=["NG-1"], # the gene you wants to knock out qckws={"minlibsize": 10, "min_percent": 0.001}, ) result = sc.build() ```

Command Line tool

Once the package is installed, users can use commandline tool to generate all the results
Use this command to create a config.yml file, shell python -m scTenifold config -t 1 -p ./net_config.yml Next, open the config file, add data path, and edit the parameters.
Then use the command below to produce the scTenifoldNet results: shell python -m scTenifold net -c ./net_config.yml -o ./output_folder

Or use the command below to produce the knockout results: shell python -m scTenifold knk -c ./knk_config.yml -o ./output_folder

Owner

  • Name: YT Lin
  • Login: qwerty239qwe
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this package, please cite it as below."
preferred-citation:
  type: article
  authors:
  - family-names: "Osorio"
    given-names: "Daniel"
    orcid: "https://orcid.org/0000-0003-4424-8422"
  - family-names: "Li"
    given-names: "Guanxun"
  - family-names: "Zhong"
    given-names: "Yan"
    orcid: "https://orcid.org/0000-0003-2412-043X"
  - family-names: "Huang"
    given-names: "Jianhua Z."
  - family-names: "Cai"
    given-names: "James"
    orcid: "https://orcid.org/0000-0002-8081-6725"
  title: "scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data"
  doi: "10.1016/j.patter.2020.100139"
  journal: "Patterns"
  volume: 1
  issue: 9
  year: 2020
  number: 100139
  start: 1
  end: 18
  url: "https://www.sciencedirect.com/science/article/pii/S2666389920301872?via%3Dihub"

GitHub Events

Total
  • Issues event: 1
  • Watch event: 6
  • Issue comment event: 2
  • Fork event: 1
Last Year
  • Issues event: 1
  • Watch event: 6
  • Issue comment event: 2
  • Fork event: 1

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 80
  • Total Committers: 3
  • Avg Commits per committer: 26.667
  • Development Distribution Score (DDS): 0.05
Top Committers
Name Email Commits
qwerty239qwe q****e@g****m 76
yu-de lin R****6@n****m 3
yoo 7****o@u****m 1
Committer Domains (Top 20 + Academic)
ntu.mail.com: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 10
  • Total pull requests: 5
  • Average time to close issues: about 1 month
  • Average time to close pull requests: 7 minutes
  • Total issue authors: 4
  • Total pull request authors: 2
  • Average comments per issue: 0.7
  • Average comments per pull request: 0.0
  • Merged pull requests: 4
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • qwerty239qwe (6)
  • seekning (2)
  • nureenmz (2)
  • lizzyjoan (1)
  • charlesgwellem (1)
Pull Request Authors
  • qwerty239qwe (4)
  • linWiky (1)
Top Labels
Issue Labels
enhancement (5) documentation (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 47 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 5
  • Total maintainers: 1
pypi.org: sctenifoldpy

scTenifoldpy

  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 47 Last month
Rankings
Dependent packages count: 10.1%
Forks count: 19.1%
Stargazers count: 20.3%
Dependent repos count: 21.6%
Average: 26.5%
Downloads: 61.5%
Maintainers (1)
qwerty239qwe
Last synced: 7 months ago

Dependencies

docs/requirements.txt pypi
  • ipykernel *
  • nbsphinx *
  • sphinx *
  • sphinxcontrib-napoleon *
requirements.txt pypi
  • PyYAML *
  • matplotlib *
  • networkx *
  • numpy *
  • pandas *
  • pytest *
  • ray *
  • requests *
  • scanpy *
  • scikit-learn *
  • scipy *
  • seaborn *
  • setuptools *
  • tensorly *
  • typer *
.github/workflows/ci.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
setup.py pypi