Package: HiSpaR 1.1.0

Yingcheng Luo

HiSpaR: Hierarchical Inference of Spatial Positions from Hi-C Data

Provides R bindings for HiSpa, a hierarchical Bayesian model for inferring three-dimensional chromatin structures from Hi-C contact matrices using Markov Chain Monte Carlo (MCMC) sampling. The package implements a cluster-based hierarchical approach that efficiently handles large-scale Hi-C datasets. It uses Rcpp and RcppArmadillo for efficient C++ integration with the original HiSpa C++ implementation, enabling fast computation of chromatin structure inference through parallel MCMC sampling.

Authors:Yingcheng Luo [aut, cre]

HiSpaR_1.1.0.tar.gz

HiSpaR_1.1.0.tgz(r-4.6-x86_64)HiSpaR_1.1.0.tgz(r-4.6-arm64)HiSpaR_1.1.0.tgz(r-4.5-x86_64)HiSpaR_1.1.0.tgz(r-4.5-arm64)
HiSpaR_1.1.0.tar.gz(r-4.7-arm64)HiSpaR_1.1.0.tar.gz(r-4.7-x86_64)HiSpaR_1.1.0.tar.gz(r-4.6-arm64)HiSpaR_1.1.0.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
HiSpaR/json (API)

# Install 'HiSpaR' in R:

install.packages('HiSpaR', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/masterstormtrooper/hispar/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

su1_contact_mat - Example Hi-C Contact Matrix

On BioConductor:HiSpaR-1.1.0(bioc 3.24)HiSpaR-1.0.0(bioc 3.23)

software epigenetics hic structuralprediction bayesian spatial openblas cpp

4.48 score 3 scripts 1 exports 61 dependencies

Last updated from:14a3af9fa4. Checks:9 NOTE, 1 OK, 4 FAIL. Indexed: yes.

Target	Result	Time
bioc-checks	NOTE	221
linux-devel-arm64	NOTE	295
linux-devel-x86_64	NOTE	345
source / vignettes	OK	464
linux-release-arm64	NOTE	289
linux-release-x86_64	NOTE	368
macos-release-arm64	NOTE	173
macos-release-x86_64	NOTE	382
macos-oldrel-arm64	NOTE	154
macos-oldrel-x86_64	NOTE	335
windows-devel	FAIL	375
windows-release	FAIL	381
windows-oldrel	FAIL	293
wasm-release	FAIL	200