This package offers interactive Shiny displays for Bioconductor objects. In addition, this package empowers users to develop engaging visualizations and interfaces for working with Bioconductor data.

This package implements methods to analyze and visualize functional profiles (GO and KEGG) of gene and gene clusters.

This package provides platform design info for Affymetrix Mapping50K_Xba240 (pd.mapping50k.xba240).

This package lets you carry out network-based gene set analysis by incorporating external information about interactions among genes, as well as novel interactions learned from data. It implements methods described in Shojaie A, Michailidis G (2010) <doi:10.1093/biomet/asq038>, Shojaie A, Michailidis G (2009) <doi:10.1089/cmb.2008.0081>, and Ma J, Shojaie A, Michailidis G (2016) <doi:10.1093/bioinformatics/btw410>.

MetaboCoreUtils defines metabolomics-related core functionality provided as low-level functions to allow a data structure-independent usage across various R packages. This includes functions to calculate between ion (adduct) and compound mass-to-charge ratios and masses or functions to work with chemical formulas. The package provides also a set of adduct definitions and information on some commercially available internal standard mixes commonly used in MS experiments.

The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, and Hits) are implemented in the S4Vectors package itself.

This package defines data structures for linkage disequilibrium (LD) measures in populations. Its purpose is to simplify handling of existing population-level data for the purpose of flexibly defining LD blocks.

This package provides a package to analyze oligonucleotide arrays (expression/SNP/tiling/exon) at probe-level. It currently supports Affymetrix (CEL files) and NimbleGen arrays (XYS files).

This package provides functions for calculation and visualization of performance metrics for evaluation of ranking and binary classification (assignment) methods. It also contains a Shiny application for interactive exploration of results.

This package provides visualization tools for flow cytometry data.

Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments.

Oscope is a oscillatory genes identifier in unsynchronized single cell RNA-seq. This statistical pipeline has been developed to identify and recover the base cycle profiles of oscillating genes in an unsynchronized single cell RNA-seq experiment. The Oscope pipeline includes three modules: a sine model module to search for candidate oscillator pairs; a K-medoids clustering module to cluster candidate oscillators into groups; and an extended nearest insertion module to recover the base cycle order for each oscillator group.

This package provides tools for discriminative motif discovery in high throughput genetic sequencing data sets using regression methods.

This package provides a GUI for analysis of Affymetrix microarray gene expression data using the affy and limma packages.

This package provides gene-level read counts from RNA-Seq for gallein-treated and control zebrafish.

This package uses a Bayesian hierarchical model to detect enriched regions from ChIP-chip experiments. The common goal in analyzing this ChIP-chip data is to detect DNA-protein interactions from ChIP-chip experiments. The BAC package has mainly been tested with Affymetrix tiling array data. However, we expect it to work with other platforms (e.g. Agilent, Nimblegen, cDNA, etc.). Note that BAC does not deal with normalization, so you will have to normalize your data beforehand.

This package helps with the analysis of array CGH data by detecting of the breakpoints in the genomic profiles and assignment of a status (gain, normal or loss) to each chromosomal regions identified.

This package provides full genome sequences for Homo sapiens (Human) as provided by UCSC (hg19, Feb. 2009) and stored in Biostrings objects. The sequences are the same as in BSgenome.Hsapiens.UCSC.hg19, except that each of them has the 4 following masks on top: (1) the mask of assembly gaps (AGAPS mask), (2) the mask of intra-contig ambiguities (AMB mask), (3) the mask of repeats from RepeatMasker (RM mask), and (4) the mask of repeats from Tandem Repeats Finder (TRF mask). Only the AGAPS and AMB masks are "active" by default.

This package implements methods to remove unwanted variation (RUV) of Risso et al. (2014) for the normalization of RNA-Seq read counts between samples.

This package provides a package for RNA basepair analysis, including the visualization of basepairs as arc diagrams for easy comparison and annotation of sequence and structure. Arc diagrams can additionally be projected onto multiple sequence alignments to assess basepair conservation and covariation, with numerical methods for computing statistics for each.

This package contains data and functions that define and allow translation between different chromosome sequence naming conventions (e.g., "chr1" versus "1"), including a function that attempts to place sequence names in their natural, rather than lexicographic, order.

This package provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working with the nucleotide content of the alignments.

RnBeads facilitates comprehensive analysis of various types of DNA methylation data at the genome scale.

This package provides full genome sequences for Mus musculus (Mouse) as provided by UCSC (mm9, July 2007) and stored in Biostrings objects.