GAGE is a published method for gene set (enrichment or GSEA) or pathway analysis. GAGE is generally applicable independent of microarray or RNA-Seq data attributes including sample sizes, experimental designs, assay platforms, and other types of heterogeneity. The gage package provides functions for basic GAGE analysis, result processing and presentation. In addition, it provides demo microarray data and commonly used gene set data based on KEGG pathways and GO terms. These functions and data are also useful for gene set analysis using other methods.

The AnVIL is a cloud computing resource developed in part by the National Human Genome Research Institute. The AnVIL package provides end-user and developer functionality. AnVIL provides fast binary package installation, utilities for working with Terra/AnVIL table and data resources, and convenient functions for file movement to and from Google cloud storage. For developers, AnVIL provides programmatic access to the Terra, Leonardo, Rawls, Dockstore, and Gen3 RESTful programming interface, including helper functions to transform JSON responses to formats more amenable to manipulation in R.

This package provides a number of utility functions for handling single-cell RNA-seq data from droplet technologies such as 10X Genomics. This includes data loading from count matrices or molecule information files, identification of cells from empty droplets, removal of barcode-swapped pseudo-cells, and downsampling of the count matrix.

This package provides a simple single-sample gene signature scoring method that uses rank-based statistics to analyze the sample's gene expression profile. It scores the expression activities of gene sets at a single-sample level.

This package is developed for the analysis and visualization of clonal tracking data. The required data is formed by samples and tag abundances in matrix form, usually from cellular barcoding experiments, integration site retrieval analyses, or similar technologies.

This package provides methods for visualizing large multivariate datasets using static and interactive scatterplot matrices, parallel coordinate plots, volcano plots, and litre plots. It includes examples for visualizing RNA-sequencing datasets and differentially expressed genes.

This R/Bioconductor package provides an interface between HDF5 and R. HDF5's main features are the ability to store and access very large and/or complex datasets and a wide variety of metadata on mass storage (disk) through a completely portable file format. The rhdf5 package is thus suited for the exchange of large and/or complex datasets between R and other software package, and for letting R applications work on datasets that are larger than the available RAM.

This is a package with metadata for genotyping Illumina 370k arrays using the crlmm package.

This package provides a set of low-level utilities to retrieve data from the UCSC Genome Browser. Most functions in the package access the data via the UCSC REST API but some of them query the UCSC MySQL server directly. Note that the primary purpose of the package is to support higher-level functionalities implemented in downstream packages like GenomeInfoDb or txdbmaker.

This is a package to perform the Adaptive Robust Regression method (ARRm) for the normalization of methylation data from the Illumina Infinium HumanMethylation 450k assay.

Fit-Hi-C is a tool for assigning statistical confidence estimates to intra-chromosomal contact maps produced by genome-wide genome architecture assays such as Hi-C.

The package xmapbridge can plot graphs in the X:Map genome browser. X:Map uses the Google Maps API to provide a scrollable view of the genome. It supports a number of species, and can be accessed at http://xmap.picr.man.ac.uk. This package exports plotting files in a suitable format. Graph plotting in R is done using calls to the functions xmap.plot and xmap.points, which have parameters that aim to be similar to those used by the standard plot methods in R. These result in data being written to a set of files (in a specific directory structure) that contain the data to be displayed, as well as some additional meta-data describing each of the graphs.

This is a package for saving Bioconductor data structures into file artifacts, and loading them back into memory. This is a more robust and portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

This package provides datasets needed for ChAMP including a test dataset and blood controls for CNA analysis.

FlowSOM offers visualization options for cytometry data, by using self-organizing map clustering and minimal spanning trees.

This R package provides tools for handling genomic interaction data, such as ChIA-PET/Hi-C, annotating genomic features with interaction information and producing various plots and statistics.

This R package is providing functions to perform geneset significance analysis over simple cross-sectional data between 2 and 5 phenotypes of interest.

Explore, diagnose, and compare variant calls using filters. The VariantTools package supports a workflow for loading data, calling single sample variants and tumor-specific somatic mutations or other sample-specific variant types (e.g., RNA editing). Most of the functions operate on alignments (BAM files) or datasets of called variants. The user is expected to have already aligned the reads with a separate tool, e.g., GSNAP via gmapR.

This package contains data for mapping between NCBI taxonomy ID and species. It is used by functions in the GenomeInfoDb package.

This package provides tools for analysis of ChIP-seq and other functional sequencing data.

Genome wide studies of translational control is emerging as a tool to study various biological conditions. The output from such analysis is both the mRNA level (e.g. cytosolic mRNA level) and the level of mRNA actively involved in translation (the actively translating mRNA level) for each mRNA. The standard analysis of such data strives towards identifying differential translational between two or more sample classes - i.e., differences in actively translated mRNA levels that are independent of underlying differences in cytosolic mRNA levels. This package allows for such analysis using partial variances and the random variance model. As 10s of thousands of mRNAs are analyzed in parallel the library performs a number of tests to assure that the data set is suitable for such analysis.

This package implements the gene expression anti-profiles method. Anti-profiles are a new approach for developing cancer genomic signatures that specifically take advantage of gene expression heterogeneity. They explicitly model increased gene expression variability in cancer to define robust and reproducible gene expression signatures capable of accurately distinguishing tumor samples from healthy controls.

This package provides tools to detect Gene Ontology and/or other user defined categories which are over/under represented in RNA-seq data.

This package aggregateBioVar contains tools to summarize single cell gene expression profiles at the level of subject for single cell RNA-seq data collected from more than one subject (e.g. biological sample or technical replicates). A SingleCellExperiment object is taken as input and converted to a list of SummarizedExperiment objects, where each list element corresponds to an assigned cell type. The SummarizedExperiment objects contain aggregate gene-by-subject count matrices and inter-subject column metadata for individual subjects that can be processed using downstream bulk RNA-seq tools.