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epiregulon

Gene regulatory network inference from single cell epigenomic data

Bioconductor version: 3.23 · Package version: 2.2.0

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.

Installation

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

BiocManager::install("epiregulon")

Details

MaintainerXiaosai Yao <xiaosai.yao@gmail.com>
AuthorXiaosai Yao [aut, cre] (ORCID: <https://orcid.org/0000-0001-9729-0726>), Tomasz Włodarczyk [aut] (ORCID: <https://orcid.org/0000-0003-1554-9699>), Aaron Lun [aut], Shang-Yang Chen [aut]
LicenseMIT + file LICENSE
URLhttps://github.com/xiaosaiyao/epiregulon/
Bug Reportshttps://github.com/xiaosaiyao/epiregulon/issues
Downloads rank194
Source branchRELEASE_3_23
biocViewsGeneExpression, GeneRegulation, GeneTarget, Network, NetworkInference, SingleCell, Software, Transcription

Documentation

Download

Dependencies

Depends: R (>= 4.5.0), SingleCellExperiment

Imports: AnnotationHub, BiocParallel, ExperimentHub, Matrix, Rcpp, S4Vectors, SummarizedExperiment, checkmate, entropy, lifecycle, methods, scran, scuttle, stats, utils, AnnotationHub, GenomeInfoDb, GenomicRanges, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, motifmatchr, IRanges, scrapper

LinkingTo: Rcpp

Suggests: knitr, rmarkdown, parallel, BiocStyle, testthat (>= 3.0.0), coin, scater, scMultiome

Reverse dependencies

Suggests Me (1): epiregulon.extra