Identifies chromatin interaction modules by constructing a Hi-C contact network based on statistically significant interactions, followed by network clustering. The method enables comparison of module connectivity across two Hi-C datasets and is capable of detecting cell-type-specific regulatory modules. By integrating network analysis with chromatin conformation data, this approach provides insights into the spatial organization of the genome and its functional implications in gene regulation. Author: Sora Yoon (2025) <https://github.com/ysora/HiCociety>.
Version: 0.1.38 Depends: R (≥ 3.5.0) Imports: strawr, shape, fitdistrplus, igraph, ggraph, foreach, doParallel, biomaRt, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, org.Mm.eg.db, org.Hs.eg.db, Rcpp, AnnotationDbi, GenomicFeatures, parallel, IRanges, S4Vectors, grDevices, graphics, stats, BiocManager, BiocGenerics, GenomicRanges, pracma, signal, HiCocietyExample LinkingTo: Rcpp Published: 2025-05-13 DOI: 10.32614/CRAN.package.HiCociety Author: Sora Yoon [aut, cre] Maintainer: Sora Yoon <sora.yoon at pennmedicine.upenn.edu> License: GPL-3 NeedsCompilation: yes CRAN checks: HiCociety results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=HiCociety to link to this page.
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