Affymetrix Affymetrix HG_U95B Array annotation data (chip hgu95b) assembled using data from public repositories.

HuBMAP provides an open, global bio-molecular atlas of the human body at the cellular level. The `datasets()`, `samples()`, `donors()`, `publications()`, and `collections()` functions retrieves the information for each of these entity types. `*_details()` are available for individual entries of each entity type. `*_derived()` are available for retrieving derived datasets or samples for individual entries of each entity type. Data files can be accessed using `bulk_data_transfer()`.

Affymetrix hugene11 annotation data (chip hugene11stprobeset) assembled using data from public repositories.

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 HT\_HG-U133\_Plus\_PM\_probe\_tab.

This package was created by frmaTools version 1.13.0.

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 HT\_Rat230\_PM\_probe\_tab.

Affymetrix hugene11 annotation data (chip hugene11sttranscriptcluster) assembled using data from public repositories.

Affymetrix hugene21 annotation data (chip hugene21stprobeset) assembled using data from public repositories.

Package with metadata for genotyping Illumina 660kQuad arrays using the crlmm package.

Affymetrix hta20 annotation data (chip hta20transcriptcluster) assembled using data from public repositories.

This package provides a package containing an environment representing the HG_U95E.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 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 HT\_HG-U133A\_probe\_tab.

Affymetrix time course experiment on human stem cells (two time points: undifferentiated and differentiated).

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-U133\_Plus\_2\_probe\_tab.

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

Agilent Human 1A (V2) annotation data (chip hgug4110b) assembled using data from public repositories.

This package provides a package containing an environment representing the HIV PRTPlus 2.CDF file.

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.

Package with metadata for genotyping Illumina CytoSNP 12 arrays using the crlmm package.

The hdxmsqc package enables us to analyse and visualise the quality of HDX-MS experiments. Either as a final quality check before downstream analysis and publication or as part of a interative procedure to determine the quality of the data. The package builds on the QFeatures and Spectra packages to integrate with other mass-spectrometry data.

This package provides a package containing an environment representing the Hu35KsubD.CDF file.

Affymetrix Affymetrix HG-Focus Array annotation data (chip hgfocus) assembled using data from public repositories.

Affymetrix Affymetrix Hu35KsubA Array annotation data (chip hu35ksuba) assembled using data from public repositories.