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Gene regulatory network inference from single cell epigenomic data

Bioconductor version: Release (3.19)

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.

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

Maintainer: Xiaosai Yao <xiaosai.yao at>

Citation (from within R, enter citation("epiregulon")):


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Epiregulon tutorial with MultiAssayExperiment HTML R Script
Reference Manual PDF


biocViews GeneExpression, GeneRegulation, GeneTarget, Network, NetworkInference, SingleCell, Software, Transcription
Version 1.0.1
In Bioconductor since BioC 3.19 (R-4.4) (< 6 months)
License MIT + file LICENSE
Depends R (>= 4.4), SingleCellExperiment
Imports AnnotationHub, BiocParallel, ExperimentHub, Matrix, Rcpp, S4Vectors, SummarizedExperiment, bluster, checkmate, entropy, lifecycle, methods, scran, scuttle, stats, utils, scMultiome, GenomeInfoDb, GenomicRanges, AUCell, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, motifmatchr, IRanges, beachmat
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Suggests knitr, rmarkdown, parallel, BiocStyle, testthat (>= 3.0.0), coin, scater
Linking To Rcpp, beachmat
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Suggests Me epiregulon.extra
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