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 Hu35KsubC.CDF file.

HPiP (Host-Pathogen Interaction Prediction) uses an ensemble learning algorithm for prediction of host-pathogen protein-protein interactions (HP-PPIs) using structural and physicochemical descriptors computed from amino acid-composition of host and pathogen proteins.The proposed package can effectively address data shortages and data unavailability for HP-PPI network reconstructions. Moreover, establishing computational frameworks in that regard will reveal mechanistic insights into infectious diseases and suggest potential HP-PPI targets, thus narrowing down the range of possible candidates for subsequent wet-lab experimental validations.

Affymetrix Affymetrix HG_U95E Array annotation data (chip hgu95e) assembled using data from public repositories.

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).

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

Affymetrix Affymetrix HT_HG-U133_Plus_B Array annotation data (chip hthgu133plusb) assembled using data from public repositories.

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

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

Hilbert curve is a type of space-filling curves that fold one dimensional axis into a two dimensional space, but with still preserves the locality. This package aims to provide an easy and flexible way to visualize data through Hilbert curve.

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

Affymetrix hta20 annotation data (chip hta20transcriptcluster) 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 HC-G110\_probe\_tab.

HiCBricks is a library designed for handling large high-resolution Hi-C datasets. Over the years, the Hi-C field has experienced a rapid increase in the size and complexity of datasets. HiCBricks is meant to overcome the challenges related to the analysis of such large datasets within the R environment. HiCBricks offers user-friendly and efficient solutions for handling large high-resolution Hi-C datasets. The package provides an R/Bioconductor framework with the bricks to build more complex data analysis pipelines and algorithms. HiCBricks already incorporates example algorithms for calling domain boundaries and functions for high quality data visualization.

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.

Codelink UniSet Human I Bioarray (~10 000 human genes) annotation data (chip h10kcod) assembled using data from public repositories.

This package provides a package containing an environment representing the HG_U95C.CDF file.

Affymetrix Affymetrix HT_Rat-Focus Array annotation data (chip htratfocus) assembled using data from public repositories.

Affymetrix Affymetrix HG_U95D Array annotation data (chip hgu95d) assembled using data from public repositories.

RNG_MRC Human Pangenomic 25k Set annotation data (chip hs25kresogen) assembled using data from public repositories.

Affymetrix Affymetrix HT_Rat230_PM Array annotation data (chip htrat230pm) assembled using data from public repositories.

This package provides a package to identify very short IBD segments in large sequencing data by FABIA biclustering. Two haplotypes are identical by descent (IBD) if they share a segment that both inherited from a common ancestor. Current IBD methods reliably detect long IBD segments because many minor alleles in the segment are concordant between the two haplotypes. However, many cohort studies contain unrelated individuals which share only short IBD segments. This package provides software to identify short IBD segments in sequencing data. Knowledge of short IBD segments are relevant for phasing of genotyping data, association studies, and for population genetics, where they shed light on the evolutionary history of humans. The package supports VCF formats, is based on sparse matrix operations, and provides visualization of haplotype clusters in different formats.

Package with metadata fast genotyping Illumina 1M 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.