HiCPotts
This is the development version of HiCPotts; for the stable release version, see HiCPotts.
All Bioconductor versions of HiCPotts
3.24 (devel), 3.23 (release), 3.22
Hierarchical Modeling to Identify and Correct Genomic Biases in Hi-C
Bioconductor version: 3.24 · Package version: 1.3.1
Bayesian analysis of Hi-C interaction counts using a three-state hierarchical mixture model with Potts spatial dependence and genomic distance, GC-content, transposable-element and accessibility covariates. The three biological components are low-mean noise, true signal with a distinct covariate-response pattern, and elevated false signal whose covariate-response slopes resemble the noise component. Robust regression fitting uses a multi-chain soft empirical-Bayes pilot to construct one shared prior that is frozen for all production chains, dispersion uses component-group-specific Gamma priors, zero inflation uses a conjugate augmented Gibbs step, and spatial coupling uses retained-state approximate Bayesian computation. The official classification pools post-burn-in latent-state frequencies from the fitted model; parameter-plus-Potts allocation is retained as a separate sensitivity analysis. Parameter summaries include posterior intervals, effective sample sizes and split-chain R-hat, with configurable diagnostic criteria for reporting. Cached native likelihood calculations, direct checkerboard allocation, reproducible cross-platform parallel chains, fit provenance and stage timings improve computational efficiency and auditability without changing the model target or official classification rule.
Author: Itunu. Godwin Osuntoki [aut, cre] 
, Nicolae. Radu Zabet [aut]
Maintainer: Itunu. Godwin Osuntoki <hitunes4 at gmail.com>
Citation
From within R, enter citation("HiCPotts"):
Itunu. Godwin Osuntoki, Nicolae. Radu Zabet. HiCPotts: Hierarchical Modeling to Identify and Correct Genomic Biases in Hi-C. doi:10.18129/B9.bioc.HiCPotts, R package version 1.3.1, https://bioconductor.org/packages/HiCPotts.
Generated from the package metadata; it may differ from the package's own citation.
Installation
To install this package, start R (version "4.6") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
## The following initializes the development version of Bioconductor
BiocManager::install(version = "devel")
BiocManager::install("HiCPotts")For older versions of R, please refer to the appropriate Bioconductor release.
Details
| Version | 1.3.1 |
| License | GPL-3 | file LICENSE |
| URL | https://github.com/igosungithub/HiCPotts |
| Bug Reports | https://github.com/igosungithub/HiCPotts/issues |
| Last updated | 2026-09-06 |
| In Bioconductor since | BioC 3.22 (R-4.5) (less than a year) |
| Downloads rank | 2238 of 2,456 |
| Source branch | devel |
| Build report | Bioconductor build system, r-universe |
| biocViews | Bayesian, Classification, DataImport, FunctionalGenomics, GenomeAnnotation, GenomeWideAssociation, HiddenMarkovModel, PeakDetection, Regression, Software, Spatial, StatisticalMethod |
| Package Short Url | https://bioconductor.org/packages/HiCPotts/ |
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("HiCPotts")| Bayesian Analysis of Hi-C Interactions with HiCPotts | HTML | R Script |
| HiCPotts Function and Argument Reference | HTML | R Script |
| Reference Manual | ||
| NEWS | Text |
Download
Follow the installation instructions to use this package in your R session.
| Source package | HiCPotts_1.3.1.tar.gz |
| Windows binary (x86_64) | HiCPotts_1.3.1.zip |
| macOS binary (arm64) | HiCPotts_1.3.1.tgz |
| macOS binary (x86_64) | HiCPotts_1.3.1.tgz |
| Source Repository | git clone https://git.bioconductor.org/packages/HiCPotts |
| Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/HiCPotts |
| Package Downloads Report | Download Stats |
Dependencies
Depends: R (>= 4.5)
Imports: Rcpp (>= 0.11.0), Biostrings, GenomicRanges, IRanges, S4Vectors, ggnewscale, parallel, rhdf5, rlang, rtracklayer, stats, withr
LinkingTo: Rcpp, RcppArmadillo
Suggests: BSgenome, BSgenome.Dmelanogaster.UCSC.dm6, BiocManager, BiocStyle, ggplot2 (>= 3.5.0), knitr (>= 1.30), reshape2 (>= 1.4.4), rmarkdown (>= 2.10), testthat (>= 3.0.0)