Affymetrix Affymetrix HT_MG-430B Array annotation data (chip htmg430b) assembled using data from public repositories.

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).

This package provides a package containing an environment representing the HGU133Plus2_Hs_Hspec.cdf file.

This package provides a package containing an environment representing the HT_MG-430B.cdf file.

R generic interface to Hi-C contact matrices in `.(m)cool`, `.hic` or HiC-Pro derived formats, as well as other Hi-C processed file formats. Contact matrices can be partially parsed using a random access method, allowing a memory-efficient representation of Hi-C data in R. The `HiCExperiment` class stores the Hi-C contacts parsed from local contact matrix files. `HiCExperiment` instances can be further investigated in R using the `HiContacts` analysis package.

This package provides a package containing an environment representing the HT_Rat230_PM.cdf file.

This package provides a package containing an environment representing the Hu35KsubC.CDF file.

Imputes HLA classical alleles using GWAS SNP data, and it relies on a training set of HLA and SNP genotypes. HIBAG can be used by researchers with published parameter estimates instead of requiring access to large training sample datasets. It combines the concepts of attribute bagging, an ensemble classifier method, with haplotype inference for SNPs and HLA types. Attribute bagging is a technique which improves the accuracy and stability of classifier ensembles using bootstrap aggregating and random variable selection.

This is an ExperimentHub Data package that helps to access the spatially-resolved transcriptomics and single-nucleus RNA sequencing data. The datasets are generated from adjacent tissue sections of the anterior human hippocampus across ten adult neurotypical donors. The datasets are based on [spatial_hpc](https://github.com/LieberInstitute/spatial_hpc) project by Lieber Institute for Brain Development (LIBD) researchers and collaborators.

This package provides a package containing an environment representing the HG_U95E.CDF file.

This package provides a package containing an environment representing the HuGene-1_0-st-v1.cdf file.

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-U133A\_tag\_probe\_tab.

This package provides a package containing an environment representing the HG-U133A.cdf file.

This package provides a package containing an environment representing the Hu6800subA.CDF file.

HMP16SData is a Bioconductor ExperimentData package of the Human Microbiome Project (HMP) 16S rRNA sequencing data for variable regions 1–3 and 3–5. Raw data files are provided in the package as downloaded from the HMP Data Analysis and Coordination Center. Processed data is provided as SummarizedExperiment class objects via ExperimentHub.

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

HiCDOC normalizes intrachromosomal Hi-C matrices, uses unsupervised learning to predict A/B compartments from multiple replicates, and detects significant compartment changes between experiment conditions. It provides a collection of functions assembled into a pipeline to filter and normalize the data, predict the compartments and visualize the results. It accepts several type of data: tabular `.tsv` files, Cooler `.cool` or `.mcool` files, Juicer `.hic` files or HiC-Pro `.matrix` and `.bed` files.

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.

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-U95B\_probe\_tab.

Affymetrix Affymetrix HC_G110 Array annotation data (chip hcg110) assembled using data from public repositories.

This package implements a filtering procedure for replicated transcriptome sequencing data based on a global Jaccard similarity index in order to identify genes with low, constant levels of expression across one or more experimental conditions.

An implementation, which takes input data and makes it available for proper batch effect removal by ComBat or Limma. The implementation appropriately handles missing values by dissecting the input matrix into smaller matrices with sufficient data to feed the ComBat or limma algorithm. The adjusted data is returned to the user as a rebuild matrix. The implementation is meant to make as much data available as possible with minimal data loss.

Affymetrix Affymetrix HT_HG-U133B Array annotation data (chip hthgu133b) assembled using data from public repositories.

Affymetrix Affymetrix HG-U133A_2 Array annotation data (chip hgu133a2) assembled using data from public repositories.