peco

Predict cell cycle phase in a continuum from single-cell RNA-seq data

https://github.com/jhsiao999/peco

Science Score: 39.0%

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  • DOI references
    Found 3 DOI reference(s) in README
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  • Scientific vocabulary similarity
    Low similarity (10.9%) to scientific vocabulary

Keywords

cell-cycle single-cell-rna-seq
Last synced: 10 months ago · JSON representation

Repository

Predict cell cycle phase in a continuum from single-cell RNA-seq data

Basic Info
Statistics
  • Stars: 12
  • Watchers: 3
  • Forks: 4
  • Open Issues: 5
  • Releases: 13
Topics
cell-cycle single-cell-rna-seq
Created over 7 years ago · Last pushed over 5 years ago
Metadata Files
Readme Changelog License

README.md

Travis-CI Build
Status AppVeyor build
status CircleCI build
status License: GPL
v3

peco

peco is an R package for PrEdicting Cell cycle prOgression in a continuum using scRNA-seq data. peco provides functions to build a training data set and predict cell cycle on a continuum.

Installation

To install and load the package, run:

r install.packages("devtools") devtools::install_github("jhsiao999/peco") library(peco)

For for a detailed illustration of peco, see the vignette.

Contact

Please contact Joyce Hsiao at joyce.hsiao1@gmail.com for questions on the package or the methods.

How to cite

Hsiao, C. J., Tung, P., Blischak, J. D., Burnett, J., Dey, K. K., Barr, A. K., Stephens, M., and Gilad, Y. (2020). Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis. Genome Biology, 30(4): 611-621, doi:10.1101/gr.247759.11

License

Copyright (c) 2019-2020, Joyce Hsiao.

All source code and software in this repository are made available under the terms of the GNU General Public License. See file LICENSE for the full text of the license.

Owner

  • Name: Chiaowen Joyce Hsiao
  • Login: jhsiao999
  • Kind: user
  • Location: United States
  • Company: University of Chicago

GitHub Events

Total
Last Year

Committers

Last synced: 11 months ago

All Time
  • Total Commits: 178
  • Total Committers: 4
  • Avg Commits per committer: 44.5
  • Development Distribution Score (DDS): 0.427
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
jhsiao999 j****1@g****m 102
Peter Carbonetto p****o@g****m 50
John Blischak j****k@g****m 24
Nitesh Turaga n****a@g****m 2

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 11
  • Total pull requests: 41
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 5 days
  • Total issue authors: 6
  • Total pull request authors: 3
  • Average comments per issue: 1.82
  • Average comments per pull request: 0.24
  • Merged pull requests: 39
  • 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
Top Authors
Issue Authors
  • jhsiao999 (4)
  • kasperdanielhansen (3)
  • snajder-r (1)
  • Yolanda-HT (1)
  • kennethabarr (1)
  • blood-abc (1)
Pull Request Authors
  • jhsiao999 (25)
  • jdblischak (7)
  • pcarbo (1)
Top Labels
Issue Labels
enhancement (3)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • bioconductor 8,296 total
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 5
  • Total maintainers: 1
bioconductor.org: peco

A Supervised Approach for **P**r**e**dicting **c**ell Cycle Pr**o**gression using scRNA-seq data

  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 8,296 Total
Rankings
Dependent repos count: 0.0%
Dependent packages count: 0.0%
Average: 26.8%
Downloads: 80.5%
Maintainers (1)
Last synced: 11 months ago