Package: epiregulon 2.3.3

Xiaosai Yao

epiregulon: Gene regulatory network inference from single cell epigenomic data

Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions.

Authors:Xiaosai Yao [aut, cre], Tomasz Włodarczyk [aut], Aaron Lun [aut], Shang-Yang Chen [aut]

epiregulon_2.3.3.tar.gz
epiregulon_2.3.3.zip(r-4.7)epiregulon_2.3.3.zip(r-4.6)epiregulon_2.3.3.zip(r-4.5)
epiregulon_2.3.3.tgz(r-4.6-x86_64)epiregulon_2.3.3.tgz(r-4.6-arm64)epiregulon_2.3.3.tgz(r-4.5-x86_64)epiregulon_2.3.3.tgz(r-4.5-arm64)
epiregulon_2.3.3.tar.gz(r-4.7-arm64)epiregulon_2.3.3.tar.gz(r-4.7-x86_64)epiregulon_2.3.3.tar.gz(r-4.6-arm64)epiregulon_2.3.3.tar.gz(r-4.6-x86_64)
epiregulon_2.3.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
epiregulon/json (API)

# Install 'epiregulon' in R:

install.packages('epiregulon', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/xiaosaiyao/epiregulon/issues

Uses libs:

c++– GNU Standard C++ Library v3

On BioConductor:epiregulon-2.3.3(bioc 3.24)epiregulon-2.2.0(bioc 3.23)

singlecell generegulation networkinference network geneexpression transcription genetarget cpp

7.38 score 28 stars 1 packages 32 scripts 12 exports 114 dependencies

Last updated from:713a0f9205. Checks:1 WARNING, 8 NOTE, 2 OK, 3 ERROR. Indexed: yes.

Target	Result	Time
bioc-checks	WARNING	397
linux-devel-arm64	NOTE	878
linux-devel-x86_64	NOTE	1037
source / vignettes	OK	849
linux-release-arm64	NOTE	879
linux-release-x86_64	NOTE	1162
macos-release-arm64	NOTE	1541
macos-release-x86_64	ERROR	1425
macos-oldrel-arm64	ERROR	783
macos-oldrel-x86_64	ERROR	1317
windows-devel	NOTE	1675
windows-release	NOTE	2000
windows-oldrel	NOTE	1739
wasm-release	OK	390

Exports:addLogFC addMotifScore addTFMotifInfo addWeights aggregateAcrossCellsFast calculateActivity calculateP2G findMarkersSimple getRegulon getTFMotifInfo optimizeMetacellNumber pruneRegulon

Dependencies:abind AnnotationDbi AnnotationHub askpass assorthead backports beachmat BH Biobase BiocBaseUtils BiocFileCache BiocGenerics BiocIO biocmake BiocManager BiocNeighbors BiocParallel BiocVersion Biostrings bit bit64 bitops blob BSgenome BSgenome.Hsapiens.UCSC.hg19 BSgenome.Hsapiens.UCSC.hg38 BSgenome.Mmusculus.UCSC.mm10 cachem caTools checkmate cigarillo cli codetools cpp11 crayon curl DBI dbplyr DelayedArray dir.expiry DirichletMultinomial dplyr entropy ExperimentHub fastmap filelock formatR futile.logger futile.options generics GenomeInfoDb GenomicAlignments GenomicRanges glue gtools httr httr2 IRanges jsonlite KEGGREST lambda.r lattice lifecycle magrittr Matrix MatrixGenerics matrixStats memoise metapod mime motifmatchr openssl pillar pkgconfig png purrr pwalign R6 rappdirs Rcpp RcppArmadillo RCurl restfulr Rhtslib Rigraphlib rjson rlang Rsamtools RSQLite rtracklayer S4Arrays S4Vectors scrapper Seqinfo seqLogo SingleCellExperiment snow SparseArray stringi stringr SummarizedExperiment sys TFBSTools TFMPvalue tibble tidyr tidyselect UCSC.utils utf8 vctrs withr XML XVector yaml

Epiregulon tutorial with MultiAssayExperiment

Rendered frommultiome.mae.Rmdusingknitr::rmarkdown

Last update: 2026-06-15
Started: 2022-09-12

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Add log fold changes of gene expression to regulons	addLogFC
Add Motif Scores	addMotifScore
Add TF binding motif occupancy information to the peak2gene object	addTFMotifInfo
Calculate weights for the regulons by computing co-association between TF and target gene expression	addWeights
Aggregate cells in SingleCellExperiment	aggregateAcrossCellsFast
Calculate the per cell activity of master regulators based on a regulon	calculateActivity
Establish peak to gene links based on correlations between ATAC-seq peaks and RNA-seq genes	calculateP2G
Find Differential Genes	findMarkersSimple
Combine the TF binding info and peak to gene correlations to generate regulons	getRegulon
Retrieve TF binding sites or motif positions	getTFMotifInfo
Determine the optimal number of metacells to be used by 'calculateP2G' function	optimizeMetacellNumber
Prune regulons for true transcription factor - regulatory elements - target genes relationships	pruneRegulon