Package: HiCPotts
Type: Package
Title: HiCPotts: Hierarchical Modeling to Identify and Correct Genomic
        Biases in Hi-C
Version: 1.3.0
Authors@R: c(person(given = "Itunu. Godwin", family = "Osuntoki", email = "hitunes4@gmail.com", role = c("aut", "cre"), 
           comment = c(ORCID = "0009-0005-1037-9346")),
           person(given = "Nicolae. Radu", family = "Zabet", email = "r.zabet@qmul.ac.uk", role = "aut"))
Description: The HiCPotts package provides a comprehensive Bayesian
        framework for analyzing Hi-C interaction data, integrating both
        spatial and genomic biases within a probabilistic modeling
        framework. At its core, HiCPotts leverages the Potts model (Wu,
        1982)—a well-established graphical model—to capture and
        quantify spatial dependencies across interaction loci arranged
        on a genomic lattice. By treating each interaction as a
        spatially correlated random variable, the Potts model enables
        robust segmentation of the genomic landscape into meaningful
        components, such as noise, true signals, and false signals. To
        model the influence of various genomic biases, HiCPotts employs
        a regression-based approach incorporating multiple covariates:
        Genomic distance (D): The distance between interacting loci,
        recognized as a fundamental driver of contact frequency.
        GC-content (GC): The local GC composition around the
        interacting loci, which can influence chromatin structure and
        interaction patterns. Transposable elements (TEs): The presence
        and abundance of repetitive elements that may shape contact
        probability through chromatin organization. Accessibility score
        (Acc): A measure of chromatin openness, informing how
        accessible certain genomic regions are to interaction. By
        embedding these covariates into a hierarchical mixture model,
        HiCPotts characterizes each interaction’s probability of
        belonging to one of several latent components. The model
        parameters, including regression coefficients, zero-inflation
        parameters (for ZIP/ZINB distributions), and dispersion terms
        (for NB/ZINB distributions), are inferred via a MCMC sampler.
        This algorithm draws samples from the joint posterior
        distribution, allowing for flexible posterior inference on
        model parameters and hidden states. From these posterior
        samples, HiCPotts computes posterior means of regression
        parameters and other quantities of interest. These posterior
        estimates are then used to calculate the posterior
        probabilities that assign each interaction to a specific
        component. The resulting classification sheds light on the
        underlying structure: distinguishing genuine high-confidence
        interactions (signal) from background noise and potential false
        signals, while simultaneously quantifying the impact of genomic
        biases on observed interaction frequencies. In summary,
        HiCPotts seamlessly integrates spatial modeling, bias
        correction, and probabilistic classification into a unified
        Bayesian inference framework. It provides rich posterior
        summaries and interpretable, model-based assignments of
        interaction states, enabling researchers to better understand
        the interplay between genomic organization, biases, and spatial
        correlation in Hi-C data.
License: GPL-3 | file LICENSE
Encoding: UTF-8
Imports: Rcpp(>= 0.11.0), parallel, stats, Biostrings, GenomicRanges,
        rtracklayer, rhdf5,IRanges, S4Vectors
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.3.3
NeedsCompilation: yes
Packaged: 2026-06-02 08:30:37 UTC; root
Suggests: BiocStyle, knitr (>= 1.30), rmarkdown (>= 2.10), ggplot2 (>=
        3.5.0), reshape2 (>= 1.4.4), testthat (>= 3.0.0), BiocManager,
        BSgenome.Dmelanogaster.UCSC.dm6, BSgenome
Config/testthat/edition: 3
Depends: R (>= 4.5)
VignetteBuilder: knitr
biocViews: StatisticalMethod, FunctionalGenomics, GenomeAnnotation,
        GenomeWideAssociation, PeakDetection, DataImport, Spatial,
        Bayesian, Classification, HiddenMarkovModel, Regression
URL: https://github.com/igosungithub/HiCPotts
BugReports: https://github.com/igosungithub/HiCPotts/issues
Author: Itunu. Godwin Osuntoki [aut, cre] (ORCID:
        <https://orcid.org/0009-0005-1037-9346>), Nicolae. Radu Zabet
        [aut]
Maintainer: Itunu. Godwin Osuntoki <hitunes4@gmail.com>
Config/pak/sysreqs: make libbz2-dev liblzma-dev libxml2-dev libssl-dev
        xz-utils zlib1g-dev
Repository: https://bioc.r-universe.dev
Date/Publication: 2026-04-28 13:05:15 UTC
RemoteUrl: https://github.com/bioc/HiCPotts
RemoteRef: HEAD
RemoteSha: acf4b60771e21fc54f2f8df3073539207682ef63
Built: R 4.6.0; x86_64-w64-mingw32; 2026-06-02 08:33:50 UTC; windows
Archs: x64
