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ccaf

Cell cycle classifier for scRNA-seq data.

https://github.com/plaisier-lab/ccaf

Science Score: 33.0%

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

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    CITATION.cff file
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    codemeta.json file
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    .zenodo.json file
  • ✓
    DOI references
    Found 6 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: pubmed.ncbi, ncbi.nlm.nih.gov
  • ✓
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    1 of 1 committers (100.0%) from academic institutions
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    Scientific vocabulary similarity
    Low similarity (12.7%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Cell cycle classifier for scRNA-seq data.

Basic Info
  • Host: GitHub
  • Owner: plaisier-lab
  • License: gpl-3.0
  • Language: Python
  • Default Branch: master
  • Size: 17 MB
Statistics
  • Stars: 6
  • Watchers: 3
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Created almost 6 years ago · Last pushed almost 4 years ago
Metadata Files
Readme License

README.md

ccAF: cell cycle ASU-Fred Hutch neural network based scRNA-seq cell cycle classifier

The ability to accurately assign a cell cycle phase based on a transcriptome profile has many potential uses in single cell studies and beyond. We have developed a cell cycle classifier based on a scRNA-seq optimized Neural Network (NN) based machine learning algorithm ACTINN. The ACTINN code was adapted from: https://github.com/mafeiyang/ACTINN

Dependencies

There are four dependencies that must be met for ccAF to classify cell cycle states: 1. numpy - (install) 2. scipy - (install) 3. scanpy - (install) 3. tensorflow - (install)

Python dependency installation commands:

NOTE! pip may need to be replaced with pip3 depending upon your setup.

shell pip3 install numpy scipy scanpy tensorflow

Installation of ccAF classifier

The ccAF classifier can be installed with the following command:

shell pip install ccAF

Alternatively use the ccAF Docker container

We facilitate the use of ccAF by providing a Docker Hub container cplaisier/scrnaseqvelocity which has all the dependencies and libraries required to run the ccAF classifier. To see how the Docker container is configured plaese refer to the Dockerfile. Please install Docker and then from the command line run:

shell docker pull cplaisier/scrna_seq_velocity

Then run the Docker container using the following command (replace with the directory where you have the scRNA-seq data to be classified):

shell docker run -it -v '<path to scRNA-seq profiles directory>:/files' cplaisier/scrna_seq_velocity

This will start the Docker container in interactive mode and will leave you at a command prompt. You will then want to change directory to where you have your scRNA-seq or trasncriptome profiling data.

Gene labels must be in human Gene Ensembl IDs to run ccAF

The data input into ccAF must use human Ensembl gene IDs (ENSG<#>), whithout the version number. If your data is not currenly labeled with Ensemble gene IDs you may try mygene or go to the BioMart.

Running ccAF against your scRNA-seq data

The first step in using ccAF is to import your scRNA-seq profiling data into scanpy. A scanpy data object is the expected input into the ccAF classifier:

```python import scanpy import ccAF

Load WT U5 hNSC data used to train classifier as a loom file

set1scanpy = sc.readloom('data/WT.loom')

Predict cell cycle phase labels

predictedLabels = ccAF.predictlabels(set1scanpy) ```

More complete example is available as test.py on the GitHub page.

Contact

For issues or comments please contact: Chris Plaisier

Citation

Neural G0: a quiescent-like state found in neuroepithelial-derived cells and glioma. Samantha A. O'Connor, Heather M. Feldman, Chad M. Toledo, Sonali Arora, Pia Hoellerbauer, Philip Corrin, Lucas Carter, Megan Kufeld, Hamid Bolouri, Ryan Basom, Jeffrey Delrow, Jose L. McFaline-Figueroa, Cole Trapnell, Steven M. Pollard, Anoop Patel, Patrick J. Paddison, Christopher L. Plaisier. bioRxiv 446344; doi: https://doi.org/10.1101/446344

Owner

  • Name: Plaisier Lab (ASU)
  • Login: plaisier-lab
  • Kind: organization
  • Email: plaisier@asu.edu
  • Location: Tempe, AZ

The Plaisier Lab is involved in computational biology approaches to study cancer and other biological systems.

GitHub Events

Total
  • Issues event: 4
  • Issue comment event: 5
Last Year
  • Issues event: 4
  • Issue comment event: 5

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 6
  • Total Committers: 1
  • Avg Commits per committer: 6.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Christopher L Plaisier, PhD p****r@a****u 6
Committer Domains (Top 20 + Academic)
asu.edu: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 3
  • Total pull requests: 0
  • Average time to close issues: 8 months
  • Average time to close pull requests: N/A
  • Total issue authors: 3
  • Total pull request authors: 0
  • Average comments per issue: 1.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • MagpiePKU (1)
  • marcouderzo (1)
  • cplaisier (1)
  • 14zac2 (1)
Pull Request Authors
Top Labels
Issue Labels
bug (1) documentation (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 17 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 2
  • Total maintainers: 1
pypi.org: ccaf

Classify scRNA-seq profiling with highly resolved cell cycle phases.

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 17 Last month
Rankings
Dependent packages count: 10.0%
Stargazers count: 23.1%
Forks count: 29.8%
Average: 39.4%
Downloads: 66.4%
Dependent repos count: 67.6%
Maintainers (1)
plaisier
Last synced: 11 months ago

Dependencies

setup.py pypi
  • numpy *