Package with metadata for genotyping Illumina Omni2.5 Quad arrays using the crlmm package.

Affymetrix hugene10 annotation data (chip hugene10sttranscriptcluster) assembled using data from public repositories.

Data used by the barcode package for microarrays of type hgu133a.

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

Affymetrix Affymetrix HT_MG-430_PM Array annotation data (chip htmg430pm) assembled using data from public repositories.

Agilent Human 2 cDNA Microarry Kit annotation data (chip hgug4101a) assembled using data from public repositories.

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 provides a package containing an environment representing the HG_U95A.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 HT\_MG-430\_PM\_probe\_tab.

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

This package provides a package containing an environment representing the Hu35KsubB.CDF file.

This package provides access to the scRNA-seq, scATAC-seq, multiome, CITE-seq and spatial transcriptomics (Visium) data generated by the tonsil cell atlas in the context of the Human Cell Atlas (HCA). The data is provided via the Bioconductor project in the form of SingleCellExperiments. Additionally, information on the whole compendium of identified cell types is provided in form of a glossary.

Affymetrix Affymetrix HT_MG-430B Array annotation data (chip htmg430b) 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 HuGene-1\_0-st-v1\_probe\_tab.

This package fits heterogeneous error models for analysis of microarray data.

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.

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

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 HG-U133B.cdf file.

This package provides a package containing an environment representing the HG-U219.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.

Codelink Human Whole Genome Bioarray (~55 000 human genes) annotation data (chip hwgcod) assembled using data from public repositories.