This package provides an annotation database of Homo sapiens genome data. It is derived from the UCSC hg38 genome and based on the "knownGene" track. The database is exposed as a TxDb object.

Wrapping an array-like object (typically an on-disk object) in a DelayedArray object allows one to perform common array operations on it without loading the object in memory. In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism. Note that this also works on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, and ordinary arrays and data frames.

This is a package for saving SingleCellExperiment into file artifacts, and loading them back into memory. This is a more 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.

Human Phenotype Ontology (HPO) was developed to create a consistent description of gene products with disease perspectives, and is essential for supporting functional genomics in disease context. Accurate disease descriptions can discover new relationships between genes and disease, and new functions for previous uncharacteried genes and alleles.

This package identifies differential expression in high-throughput count data, such as that derived from next-generation sequencing machines, calculating estimated posterior likelihoods of differential expression (or more complex hypotheses) via empirical Bayesian methods.

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 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.

BioQC performs quality control of high-throughput expression data based on tissue gene signatures. It can detect tissue heterogeneity in gene expression data. The core algorithm is a Wilcoxon-Mann-Whitney test that is optimised for high performance.

This package provides a manifest package for Illumina's EPIC v2.0 methylation arrays. The version 2 covers more than 935K CpG sites in the human genome hg38. It is an update of the original EPIC v1.0 array (i.e., the 850K methylation array).

This package provides a framework to perform Non-negative Matrix Factorization (NMF). The package implements a set of already published algorithms and seeding methods, and provides a framework to test, develop and plug new or custom algorithms. Most of the built-in algorithms have been optimized in C++, and the main interface function provides an easy way of performing parallel computations on multicore machines.

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.

This package implements widgets to provide user interfaces.

This package contains tools to support the construction of tcltk widgets in R.

This is a representation of public golub data with some covariate data of provenance unknown to the maintainer at present; it now employs ExpressionSet format.

This package is designed to facilitate comparison of automated gating methods against manual gating done in flowJo. This package allows you to import basic flowJo workspaces into BioConductor and replicate the gating from flowJo using the flowCore functionality. Gating hierarchies, groups of samples, compensation, and transformation are performed so that the output matches the flowJo analysis.

The AffyRNADegradation package helps with the assessment and correction of RNA degradation effects in Affymetrix 3 expression arrays. The parameter d gives a robust and accurate measure of RNA integrity. The correction removes the probe positional bias, and thus improves comparability of samples that are affected by RNA degradation.

This package implements sampling, iteration, and input of FASTQ files. It includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, and easily manipulated for a diversity of purposes. The package also contains legacy support for early single-end, ungapped alignment formats.

Cicero computes putative cis-regulatory maps from single-cell chromatin accessibility data. It also extends the monocle package for use in chromatin accessibility data.

This package provides a tool for non linear mapping (non linear regression) using a mixture of regression model and an inverse regression strategy. The methods include the GLLiM model (see Deleforge et al (2015) <DOI:10.1007/s11222-014-9461-5>) based on Gaussian mixtures and a robust version of GLLiM, named SLLiM (see Perthame et al (2016) <DOI:10.1016/j.jmva.2017.09.009>) based on a mixture of Generalized Student distributions. The methods also include BLLiM (see Devijver et al (2017) <arXiv:1701.07899>) which is an extension of GLLiM with a sparse block diagonal structure for large covariance matrices (particularly interesting for transcriptomic data).

This package provides data from 6 samples across 2 groups from 450k methylation arrays.

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 an interface to implementations of the GatingML2.0 standard to exchange gated cytometry data with other software platforms.

This is the core package for the automated analysis of Affymetrix arrays.

This package can do differential expression analysis of RNA-seq expression profiles with biological replication. It implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. It be applied to differential signal analysis of other types of genomic data that produce counts, including ChIP-seq, SAGE and CAGE.