This package was automatically created by package AnnotationForge version 1.7.17. The exon-level probeset genome location was retrieved from Netaffx using AffyCompatible.

Base annotation databases for human, intended ONLY to be used by AnnotationDbi to produce regular annotation packages.

Affymetrix Affymetrix HG_U95C Array annotation data (chip hgu95c) assembled using data from public repositories.

HVP is a quantitative batch effect metric that estimates the proportion of variance associated with batch effects in a data set.

The package contains a modular pipeline for analysis of HELP microarray data, and includes graphical and mathematical tools with more general applications.

Systematic 3D interaction calls and differential analysis for Hi-C and HiChIP. The HiC-DC+ (Hi-C/HiChIP direct caller plus) package enables principled statistical analysis of Hi-C and HiChIP data sets – including calling significant interactions within a single experiment and performing differential analysis between conditions given replicate experiments – to facilitate global integrative studies. HiC-DC+ estimates significant interactions in a Hi-C or HiChIP experiment directly from the raw contact matrix for each chromosome up to a specified genomic distance, binned by uniform genomic intervals or restriction enzyme fragments, by training a background model to account for random polymer ligation and systematic sources of read count variation.

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

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-U219\_probe\_tab.

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

Package containing example and annotation data for Hipathia package. Hipathia is a method for the computation of signal transduction along signaling pathways from transcriptomic data. The method is based on an iterative algorithm which is able to compute the signal intensity passing through the nodes of a network by taking into account the level of expression of each gene and the intensity of the signal arriving to it. It also provides a new approach to functional analysis allowing to compute the signal arriving to the functions annotated to each pathway. Hipathia depends on this package to be functional.

This package provides a package containing an environment representing the HG-U133B.cdf file.

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

An R Package for Geneset Enrichment Workflows.

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-Focus\_probe\_tab.

This package provides a package containing an environment representing the HT_HG-U133B.cdf file.

agilent AMADID 026652 annotation data (chip hgug4845a) assembled using data from public repositories.

Qiagen Qiagen V3.0 oligo set annotation data (chip hguqiagenv3) assembled using data from public repositories.

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

FHCRC Genomics Shared Resource HuO22 Annotation Data (HuO22) assembled using data from public repositories.

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

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

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.

For scRNA-seq data, it selects features and clusters the cells simultaneously for single-cell UMI data. It has a novel feature selection method using the zero inflation instead of gene variance, and computationally faster than other existing methods since it only relies on PCA+Kmeans rather than graph-clustering or consensus clustering.

Affymetrix Affymetrix HT_HG-U133_Plus_A Array annotation data (chip hthgu133plusa) assembled using data from public repositories.