This package aims at representing and summarizing the entire single-cell profile of a sample. It allows researchers to perform important bioinformatic analyses at the sample-level such as visualization and quality control. The main functions Estimate sample distribution and calculate statistical divergence among samples, and visualize the distance matrix through MDS plots.

Gene selection based on a mixture of marginal distributions.

The NCBI Gene Expression Omnibus (GEO) represents the largest public repository of microarray data. However, finding data of interest can be challenging using current tools. GEOmetadb is an attempt to make access to the metadata associated with samples, platforms, and datasets much more feasible. This is accomplished by parsing all the NCBI GEO metadata into a SQLite database that can be stored and queried locally. GEOmetadb is simply a thin wrapper around the SQLite database along with associated documentation. Finally, the SQLite database is updated regularly as new data is added to GEO and can be downloaded at will for the most up-to-date metadata. GEOmetadb paper: http://bioinformatics.oxfordjournals.org/cgi/content/short/24/23/2798 .

Single cell RNA-Seq data for 5902 cells from 18 patients with oral cavity head and neck squamous cell carcinoma available as GEO accession [GSE103322] (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE103322). GSE103322 data have been parsed into a SincleCellExperiment object available in ExperimentHub.

This package provides transcript expression and bi-allelic genotypes corresponding to the chromosome 19 for CEU individuals from the GEUVADIS project, Lappalainen et al.

Package is a part of the gDR suite. It reexports functions from other packages in the gDR suite that contain critical processing functions and utilities. The vignette walks through the full processing pipeline for drug response analyses that the gDR suite offers.

Recurrent breakpoint gene detection on copy number aberration profiles.

If you have a set of genomic ranges, this package can help you with visualization and comparison. It produces several kinds of plots, for example: Chromosome distribution plots, which visualize how your regions are distributed over chromosomes; feature distance distribution plots, which visualizes how your regions are distributed relative to a feature of interest, like Transcription Start Sites (TSSs); genomic partition plots, which visualize how your regions overlap given genomic features such as promoters, introns, exons, or intergenic regions. It also makes it easy to compare one set of ranges to another.

Example data for the GPA package, consisting of the p-values of 1,219,805 SNPs for five psychiatric disorder GWAS from the psychiatric GWAS consortium (PGC), with the annotation data using genes preferentially expressed in the central nervous system (CNS).

The development of high-throughput sequencing led to increased use of co-expression analysis to go beyong single feature (i.e. gene) focus. We propose GWENA (Gene Whole co-Expression Network Analysis) , a tool designed to perform gene co-expression network analysis and explore the results in a single pipeline. It includes functional enrichment of modules of co-expressed genes, phenotypcal association, topological analysis and comparison of networks configuration between conditions.

Illumina Golden Gate Human Methylation Cancer Panel Version 1 annotation data (chip GGHumanMethCancerPanelv1) assembled using data from public repositories.

Identification of the most likely gene or genes through which variation at a given genomic locus in the human genome acts. The most basic functionality assumes that the closer gene is to the input locus, the more likely the gene is to be causative. Additionally, any empirical data that links genomic regions to genes (e.g. eQTL or genome conformation data) can be used if it is supplied in the UCSC .BED file format.

Affymetrix huex10 annotation data (chip huex10sttranscriptcluster) assembled using data from public repositories.

Unknown annotation data (chip hgubeta7) assembled using data from public repositories.

Annotation data file for humanCHRLOC assembled using data from public data repositories.

This package provides a package for detecting differential methylation. It exploits a Bayesian hidden Markov model that incorporates location dependence among genomic loci, unlike most existing methods that assume independence among observations. Bayesian priors are applied to permit information sharing across an entire chromosome for improved power of detection. The direct output of our software package is the best sequence of methylation states, eliminating the use of a subjective, and most of the time an arbitrary, threshold of p-value for determining significance. At last, our methodology does not require replication in either or both of the two comparison groups.

This package contains the data used in the vignettes and examples of the h5vc package.

Define utilities for exploration of human metabolome database, including functions to retrieve specific metabolite entries and data snapshots with pairwise associations (metabolite-gene,-protein,-disease).

This package provides a package containing an environment representing the HG-U133A.cdf file.

Affymetrix Affymetrix Hu35KsubD Array annotation data (chip hu35ksubd) 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 HG-U133A\_probe\_tab.

This package allows a direct access to the dataset generated by the Human Cell Atlas project for further processing in R and Bioconductor, in the comfortable format of SingleCellExperiment objects (available in other formats here: http://preview.data.humancellatlas.org/).

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

This package provides a collection of Hi-C files (pairs, (m)cool and fastq). These datasets can be read into R and further investigated and visualized with the HiContacts package. Data includes yeast Hi-C data generated by the Koszul lab from the Pasteur Institute.