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epiregulon

inference of transcription factor activity at the single cell level

https://github.com/xiaosaiyao/epiregulon

Science Score: 59.0%

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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    Found codemeta.json file
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    Found .zenodo.json file
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    DOI references
    Found 3 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: biorxiv.org
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    Committers with academic emails
    2 of 13 committers (15.4%) from academic institutions
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    Institutional organization owner
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    Scientific vocabulary similarity
    Low similarity (13.1%) to scientific vocabulary

Keywords from Contributors

bioconductor-package genomics rna-velocity bioconductor gene rnaseq bioinformatics singler u24ca289073 core-package
Last synced: 6 months ago · JSON representation

Repository

inference of transcription factor activity at the single cell level

Basic Info
Statistics
  • Stars: 18
  • Watchers: 2
  • Forks: 1
  • Open Issues: 4
  • Releases: 0
Created over 3 years ago · Last pushed 6 months ago
Metadata Files
Readme Changelog License

README.md

plot

Introduction

Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and cell states. The main function of the epiregulon package is to construct gene regulatory networks and infer transcription factor (TF) activity in single cells by integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data.

For full documentation, please refer to the epiregulon book.

plot There are three related packages. The core epiregulon package supports SingleCellExperiment objects. If the users would like to start from ArchR projects, they may choose to use epiregulon.archr package, which allows for seamless integration with the ArchR package. Moreover, we provide a suite of tools in epiregulon.extra package for enrichment analysis, visualization, and network analysis which can be run on the epireglon or epiregulon.archr output.

Installation

```

install devtools

if(!require(devtools)) install.packages("devtools")

install basic epiregulon package

devtools::install_github(repo='xiaosaiyao/epiregulon')

install extended version of epiregulon

devtools::install_github(repo='xiaosaiyao/epiregulon.archr')

install extended version of epiregulon

devtools::install_github(repo='xiaosaiyao/epiregulon.extra') ``` Example data included in the tutorial are available from scMultiome

``` if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager")

BiocManager::install("scMultiome") ```

System Requirements

Hardware Requirements

The epiregulon package has been tested on a standard MacBook with 16GB of RAM and 8 cores

Software Requirements

The epiregulon package is supported for macOS, Linux and Windows. The package has been tested on the following systems:

  • macOS: Monterey (12.7.1)
  • Linux: Ubuntu 22.04.2 LTS
  • Windows: Windows 2022

Users should have R version 4.3.0 or higher

Functions

Functions in the suite of Epiregulon packages plot

Reference

Tomasz Włodarczyk, Aaron Lun, Diana Wu, Shreya Menon, Shushan Toneyan, Kerstin Seidel, Liang Wang, Jenille Tan, Shang-Yang Chen, Timothy Keyes, Aleksander Chlebowski, Yu Guo, Ciara Metcalfe, Marc Hafner, Christian W. Siebel, M. Ryan Corces, Robert Yauch, Shiqi Xie, Xiaosai Yao. 2023. "Inference of single-cell transcription factor activity to dissect mechanisms of lineage plasticity and drug response" bioRxiv 2023.11.27.568955; doi: https://doi.org/10.1101/2023.11.27.568955

Contact: Xiaosai Yao, Genentech Inc.

Owner

  • Name: Xiaosai Yao
  • Login: xiaosaiyao
  • Kind: user
  • Company: Genentech

Oncology Bioinformatics Scientist

GitHub Events

Total
  • Issues event: 12
  • Watch event: 5
  • Delete event: 4
  • Issue comment event: 16
  • Push event: 13
  • Pull request review event: 2
  • Pull request event: 8
  • Fork event: 1
  • Create event: 2
Last Year
  • Issues event: 12
  • Watch event: 5
  • Delete event: 4
  • Issue comment event: 16
  • Push event: 13
  • Pull request review event: 2
  • Pull request event: 8
  • Fork event: 1
  • Create event: 2

Committers

Last synced: 6 months ago

All Time
  • Total Commits: 759
  • Total Committers: 13
  • Avg Commits per committer: 58.385
  • Development Distribution Score (DDS): 0.69
Past Year
  • Commits: 67
  • Committers: 7
  • Avg Commits per committer: 9.571
  • Development Distribution Score (DDS): 0.567
Top Committers
Name Email Commits
Xiaosai Yao y****i@g****m 235
TomVuod t****k@c****m 181
TomVuod t****o@g****m 138
Xiaosai Yao y****9@g****m 130
CHENS179 c****9@g****m 44
Chen, Shang-Yang {GRBP~South San Francisco} c****g@g****m 7
Tomasz Włodarczyk 1****d@u****m 5
Aaron Lun l****n@g****m 4
J Wokaty j****y@s****u 4
J Wokaty j****y@u****m 3
LTLA i****s@g****m 3
Xiaosai Yao 7****o@u****m 3
A Wokaty a****y@s****u 2
Committer Domains (Top 20 + Academic)
gene.com: 5 sph.cuny.edu: 2 contractors.roche.com: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 10
  • Total pull requests: 26
  • Average time to close issues: 21 days
  • Average time to close pull requests: 4 days
  • Total issue authors: 6
  • Total pull request authors: 3
  • Average comments per issue: 1.1
  • Average comments per pull request: 0.23
  • Merged pull requests: 19
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 7
  • Pull requests: 3
  • Average time to close issues: about 1 month
  • Average time to close pull requests: 19 days
  • Issue authors: 5
  • Pull request authors: 1
  • Average comments per issue: 0.86
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • xiaosaiyao (3)
  • gilgolan73 (2)
  • wmacnair (2)
  • LTLA (1)
  • dongwei1220 (1)
  • phoebe-yg (1)
Pull Request Authors
  • TomVuod (41)
  • LTLA (2)
  • xiaosaiyao (2)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • bioconductor 3,640 total
  • Total dependent packages: 1
  • Total dependent repositories: 0
  • Total versions: 4
  • Total maintainers: 1
bioconductor.org: epiregulon

Gene regulatory network inference from single cell epigenomic data

  • Versions: 4
  • Dependent Packages: 1
  • Dependent Repositories: 0
  • Downloads: 3,640 Total
Rankings
Dependent repos count: 0.0%
Average: 15.6%
Dependent packages count: 31.3%
Maintainers (1)
xiaosai.yao@gmail.com
Last synced: 6 months ago

Dependencies

DESCRIPTION cran
  • R >= 4.2.0 depends
  • SingleCellExperiment * depends
  • AUCell * imports
  • BSgenome.Hsapiens.UCSC.hg19 * imports
  • BSgenome.Hsapiens.UCSC.hg38 * imports
  • BSgenome.Mmusculus.UCSC.mm10 * imports
  • BiocParallel * imports
  • GenomeInfoDb * imports
  • GenomicRanges * imports
  • Matrix * imports
  • Rcpp * imports
  • S4Vectors * imports
  • SummarizedExperiment * imports
  • bluster * imports
  • checkmate * imports
  • entropy * imports
  • lifecycle * imports
  • methods * imports
  • motifmatchr * imports
  • scMultiome * imports
  • scran * imports
  • scuttle * imports
  • stats * imports
  • utils * imports
  • BiocStyle * suggests
  • chromVARmotifs * suggests
  • coin * suggests
  • dsassembly * suggests
  • knitr * suggests
  • parallel * suggests
  • rmarkdown * suggests
  • scater * suggests
  • testthat >= 3.0.0 suggests