Package: HiCPotts 1.3.1

HiCPotts: Hierarchical Modeling to Identify and Correct Genomic Biases in Hi-C

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.

Authors:Itunu. Godwin Osuntoki [aut, cre], Nicolae. Radu Zabet [aut]