HiSpaR

HiSpaR provides R bindings for HiSpa, a hierarchical Bayesian model that infers three-dimensional chromatin structures from Hi-C contact matrices using Markov Chain Monte Carlo (MCMC) sampling.

Key Features:

• Hierarchical Bayesian modeling: Implements a cluster-based hierarchical approach for inferring 3D chromatin structures from Hi-C contact matrices.
• Large-scale dataset handling: Efficiently processes large-scale Hi-C datasets through its hierarchical architecture.
• C++ integration: Uses Rcpp and RcppArmadillo for efficient integration with the original HiSpa C++ implementation.
• Parallel MCMC sampling: Enables fast computation of chromatin structure inference through parallelized MCMC sampling.

Scientific Applications:

• 3D chromatin structure inference: Reconstructs three-dimensional spatial organization of chromatin from Hi-C contact matrices.
• Hi-C data analysis: Processes Hi-C contact matrices to infer spatial positions of genomic loci.

Methodology:

HiSpaR implements hierarchical Bayesian inference with Markov Chain Monte Carlo (MCMC) sampling, using a cluster-based hierarchical approach integrated with C++ via Rcpp and RcppArmadillo for parallel computation.