Affymetrix Affymetrix Hu6800 Array annotation data (chip hu6800) assembled using data from public repositories.

Sample dataset obtained from http://www.hapmap.org.

HiCBricks is a library designed for handling large high-resolution Hi-C datasets. Over the years, the Hi-C field has experienced a rapid increase in the size and complexity of datasets. HiCBricks is meant to overcome the challenges related to the analysis of such large datasets within the R environment. HiCBricks offers user-friendly and efficient solutions for handling large high-resolution Hi-C datasets. The package provides an R/Bioconductor framework with the bricks to build more complex data analysis pipelines and algorithms. HiCBricks already incorporates example algorithms for calling domain boundaries and functions for high quality data visualization.

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was HG\_U95A\_probe\_tab.

Hilbert curve is a type of space-filling curves that fold one dimensional axis into a two dimensional space, but with still preserves the locality. This package aims to provide an easy and flexible way to visualize data through Hilbert curve.

The HiC data from Human Fibroblast IMR90 cell line (HindIII restriction) was retrieved from the GEO website, accession number GSE35156 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE35156). The raw reads were processed as explained in Dixon et al. (Nature 2012).

Affymetrix Affymetrix HT_Rat-Focus Array annotation data (chip htratfocus) assembled using data from public repositories.

This package provides a package containing an environment representing the Hu35KsubA.CDF file.

This package provides a package containing metadata for Hs6UG171 arrays assembled using data from public repositories.

This package provides a package containing an environment representing the Hu6800subD.CDF file.

Codelink Human Inflammation 16 Bioarray annotation data (chip hi16cod) assembled using data from public repositories.

This package was automatically created by package AnnotationDbi version 1.11.8. The exon-level probeset genome location was retrieved from Netaffx using AffyCompatible. The exon-level probeset genome location was retrieved from Netaffx using AffyCompatible. Genome release hg19.

Gene-level count matrix data for bulk RNA-seq dataset with many replicates. The data are provided as easy to use SummarizedExperiment objects. The source data that is made accessible through this package comes from https://github.com/bartongroup/profDGE48.

Package with metadata for genotyping Illumina 1M Duo arrays using the crlmm package.

This package was created by frmaTools version 1.19.3 and hgu133ahsentrezgcdf version 19.0.0.

In epigenome-wide association studies, the measured signals for each sample are a mixture of methylation profiles from different cell types. The current approaches to the association detection only claim whether a cytosine-phosphate-guanine (CpG) site is associated with the phenotype or not, but they cannot determine the cell type in which the risk-CpG site is affected by the phenotype. We propose a solid statistical method, HIgh REsolution (HIRE), which not only substantially improves the power of association detection at the aggregated level as compared to the existing methods but also enables the detection of risk-CpG sites for individual cell types. The "HIREewas" R package is to implement HIRE model in R.

Package with metadata for genotyping Illumina 650k arrays using the crlmm package.

Annotation data file for humanCHRLOC assembled using data from public data repositories.

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.

The CellScore Standard Dataset contains expression data from a wide variety of human cells and tissues, which should be used as standard cell types in the calculation of the CellScore. All data was curated from public databases such as Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) or ArrayExpress (https://www.ebi.ac.uk/arrayexpress/). This standard dataset only contains data from the Affymetrix GeneChip Human Genome U133 Plus 2.0 microarrays. Samples were manually annotated using the database information or consulting the publications in which the datasets originated. The sample annotations are stored in the phenoData slot of the expressionSet object. Raw data (CEL files) were processed with the affy package to generate present/absent calls (mas5calls) and background-subtracted values, which were then normalized by the R-package yugene to yield the final expression values for the standard expression matrix. The annotation table for the microarray was retrieved from the BioC annotation package hgu133plus2. All data are stored in an expressionSet object.

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was Hu35KsubB\_probe\_tab.

This package provides a package to identify very short IBD segments in large sequencing data by FABIA biclustering. Two haplotypes are identical by descent (IBD) if they share a segment that both inherited from a common ancestor. Current IBD methods reliably detect long IBD segments because many minor alleles in the segment are concordant between the two haplotypes. However, many cohort studies contain unrelated individuals which share only short IBD segments. This package provides software to identify short IBD segments in sequencing data. Knowledge of short IBD segments are relevant for phasing of genotyping data, association studies, and for population genetics, where they shed light on the evolutionary history of humans. The package supports VCF formats, is based on sparse matrix operations, and provides visualization of haplotype clusters in different formats.

Affymetrix hugene10 annotation data (chip hugene10stprobeset) assembled using data from public repositories.

This package provides a package containing an environment representing the Hu6800subC.CDF file.