This package offers a statistical framework based on customizable permutation tests to assess the association between genomic region sets and other genomic features.

This package implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. It provides a robust toolbox supporting over 70 methods for semantic similarity analysis.

This package provides examples and code that make use of the different graph related packages produced by Bioconductor.

This package ofers functions for importation, normalization, visualization, and quality control to correct identified sources of variability in array of CGH experiments.

This is a package for the assessment and comparison of the performance of risk prediction (survival) models.

This package provides basic utility functions for performing single-cell analyses, focusing on simple normalization, quality control and data transformations. It also provides some helper functions to assist development of other packages.

CelliD is a clustering-free method for extracting per-cell gene signatures from scRNA-seq. CelliD allows unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics protocols. The package can also be used to explore functional pathways enrichment in single cell data.

This package implements the circular binary segmentation (CBS) algorithm to segment DNA copy number data and identify genomic regions with abnormal copy number.

This package provides tools for estimating variance-mean dependence in count data from high-throughput genetic sequencing assays and for testing for differential expression based on a model using the negative binomial distribution.

This package provides full genome sequences for Homo sapiens from 1000genomes phase2 reference genome sequence (hs37d5), based on NCBI GRCh37.

This package contains genome-wide annotations for Human, primarily based on mapping using Entrez Gene identifiers.

AS (alternative splicing) is a common mechanism of post-transcriptional gene regulation in eukaryotic organisms that expands the functional and regulatory diversity of a single gene by generating multiple mRNA isoforms that encode structurally and functionally distinct proteins. ASpli is an integrative pipeline and user-friendly R package that facilitates the analysis of changes in both annotated and novel AS events. ASpli integrates several independent signals in order to deal with the complexity that might arise in splicing patterns.

This package provides an interface to simulate metabolic reconstruction from the BiGG database and other metabolic reconstruction databases. The package facilitates flux balance analysis (FBA) and the sampling of feasible flux distributions. Metabolic networks and estimated fluxes can be visualized with hypergraphs.

This package provides tools for the identification of differentially expressed genes and estimation of the False Discovery Rate (FDR) using both the Significance Analysis of Microarrays (SAM) and the Empirical Bayes Analyses of Microarrays (EBAM).

Fastseg implements a very fast and efficient segmentation algorithm. It can segment data from DNA microarrays and data from next generation sequencing for example to detect copy number segments. Further it can segment data from RNA microarrays like tiling arrays to identify transcripts. Most generally, it can segment data given as a matrix or as a vector. Various data formats can be used as input to fastseg like expression set objects for microarrays or GRanges for sequencing data.

The package is able to read bead-level data (raw TIFFs and text files) output by BeadScan as well as bead-summary data from BeadStudio. Methods for quality assessment and low-level analysis are provided.

The enrichplot package implements several visualization methods for interpreting functional enrichment results obtained from ORA or GSEA analyses. All the visualization methods are developed based on ggplot2 graphics.

The package alpine helps to model bias parameters and then using those parameters to estimate RNA-seq transcript abundance. Alpine is a package for estimating and visualizing many forms of sample-specific biases that can arise in RNA-seq, including fragment length distribution, positional bias on the transcript, read start bias (random hexamer priming), and fragment GC-content (amplification). It also offers bias-corrected estimates of transcript abundance in FPKM(Fragments Per Kilobase of transcript per Million mapped reads). It is currently designed for un-stranded paired-end RNA-seq data.

This package provides functionality for interactive visualization of RNA-seq datasets based on Principal Components Analysis. The methods provided allow for quick information extraction and effective data exploration. A Shiny application encapsulates the whole analysis.

The HiTC package was developed to explore high-throughput "C" data such as 5C or Hi-C. Dedicated R classes as well as standard methods for quality controls, normalization, visualization, and further analysis are also provided.

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

M3C is a consensus clustering algorithm that uses a Monte Carlo simulation to eliminate overestimation of K and can reject the null hypothesis K=1.

This package provides functions for handling translating between different identifieres using the Biocore Data Team data-packages (e.g. org.Bt.eg.db).

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.