Re-analysis of human gene expression data generated on the Affymetrix HG_U133PlusV2 (EH176) and Affymetrix HG_U133A (EH177) platforms. The original data were normalized using robust multiarray averaging (RMA) to obtain an integrated gene expression atlas across diverse biological sample types and conditions. The entire compendia comprisee 9395 arrays for EH176 and 5372 arrays for EH177.

This package provides a package built under the Bayesian framework of applying hierarchical latent Dirichlet allocation. It statistically tests whether the mutational exposures of mutational signatures (Shiraishi-model signatures) are different between two groups. The package also provides inference and visualization.

HGC (short for Hierarchical Graph-based Clustering) is an R package for conducting hierarchical clustering on large-scale single-cell RNA-seq (scRNA-seq) data. The key idea is to construct a dendrogram of cells on their shared nearest neighbor (SNN) graph. HGC provides functions for building graphs and for conducting hierarchical clustering on the graph. The users with old R version could visit https://github.com/XuegongLab/HGC/tree/HGC4oldRVersion to get HGC package built for R 3.6.

HiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. HiCcompare operates on processed Hi-C data in the form of chromosome-specific chromatin interaction matrices. It accepts three-column tab-separated text files storing chromatin interaction matrices in a sparse matrix format which are available from several sources. HiCcompare is designed to give the user the ability to perform a comparative analysis on the 3-Dimensional structure of the genomes of cells in different biological states.`HiCcompare` differs from other packages that attempt to compare Hi-C data in that it works on processed data in chromatin interaction matrix format instead of pre-processed sequencing data. In addition, `HiCcompare` provides a non-parametric method for the joint normalization and removal of biases between two Hi-C datasets for the purpose of comparative analysis. `HiCcompare` also provides a simple yet robust method for detecting differences between Hi-C datasets.

This package provides a package containing an environment representing the HT_HG-U133A.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 HT_HG-U133_Plus_PM.CDF file.

This is a data package for normalised homosapien data downloaded from DEE2. The package both downloads, normalises, and filters the data, and provides a way to access the data from a canonical store without needing local processing. This package was built as a way to generate and store canonical test data for CellScore.

This package provides a set of functions useful in the analysis of 3D genomic interactions. It includes the import of standard HiC data formats into R and HiC normalisation procedures. The main objective of this package is to improve the visualization and quantification of the analysis of HiC contacts through aggregation. The package allows to import 1D genomics data, such as peaks from ATACSeq, ChIPSeq, to create potential couples between features of interest under user-defined parameters such as distance between pairs of features of interest. It allows then the extraction of contact values from the HiC data for these couples and to perform Aggregated Peak Analysis (APA) for visualization, but also to compare normalized contact values between conditions. Overall the package allows to integrate 1D genomics data with 3D genomics data, providing an easy access to HiC contact values.

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

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.

Affymetrix Affymetrix HG_U95E Array annotation data (chip hgu95e) 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 Hu35KsubD\_probe\_tab.

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.

The HOPACH clustering algorithm builds a hierarchical tree of clusters by recursively partitioning a data set, while ordering and possibly collapsing clusters at each level. The algorithm uses the Mean/Median Split Silhouette (MSS) criteria to identify the level of the tree with maximally homogeneous clusters. It also runs the tree down to produce a final ordered list of the elements. The non-parametric bootstrap allows one to estimate the probability that each element belongs to each cluster (fuzzy clustering).

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 Hu35KsubC\_probe\_tab.

Affymetrix hugene20 annotation data (chip hugene20stprobeset) assembled using data from public repositories.

This package provides a package containing an environment representing the HG-U133A_2.cdf file.

Agilent Human 1 cDNA Microarray Kit annotation data (chip hgug4100a) assembled using data from public repositories.

Affymetrix Affymetrix HT_HG-U133A Array annotation data (chip hthgu133a) assembled using data from public repositories.

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

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

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

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