Gcrma adjusts for background intensities in Affymetrix array data which include optical noise and non-specific binding (NSB). The main function gcrma converts background adjusted probe intensities to expression measures using the same normalization and summarization methods as a Robust Multiarray Average (RMA). Gcrma uses probe sequence information to estimate probe affinity to NSB. The sequence information is summarized in a more complex way than the simple GC content. Instead, the base types (A, T, G or C) at each position along the probe determine the affinity of each probe. The parameters of the position-specific base contributions to the probe affinity is estimated in an NSB experiment in which only NSB but no gene-specific binding is expected.

The standard index of DNA methylation (beta) is computed from methylated and unmethylated signal intensities. Betas calculated from raw signal intensities perform well, but using 11 methylomic datasets we demonstrate that quantile normalization methods produce marked improvement. The commonly used procedure of normalizing betas is inferior to the separate normalization of M and U, and it is also advantageous to normalize Type I and Type II assays separately. This package provides 15 flavours of betas and three performance metrics, with methods for objects produced by the methylumi and minfi packages.

This package exposes an annotation database generated from Ensembl.

This package contains the helper files that are required to run the Bioconductor package CopywriteR. It contains pre-assembled 1kb bin GC-content and mappability files for the reference genomes hg18, hg19, hg38, mm9 and mm10. In addition, it contains a blacklist filter to remove regions that display copy number variation. Files are stored as GRanges objects from the GenomicRanges Bioconductor package.

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 package provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.

This package provides a class and subclasses for storing non-scalar objects in matrix entries. This is akin to a ragged array but the raggedness is in the third dimension, much like a bumpy surface--hence the name. Of particular interest is the BumpyDataFrameMatrix, where each entry is a Bioconductor data frame. This allows us to naturally represent multivariate data in a format that is compatible with two-dimensional containers like the SummarizedExperiment and MultiAssayExperiment objects.

SCONE is an R package for comparing and ranking the performance of different normalization schemes for single-cell RNA-seq and other high-throughput analyses.

This package provides basic functions for filtering genes from high-throughput sequencing experiments.

Analysis of Ct values from high throughput quantitative real-time PCR (qPCR) assays across multiple conditions or replicates. The input data can be from spatially-defined formats such ABI TaqMan Low Density Arrays or OpenArray; LightCycler from Roche Applied Science; the CFX plates from Bio-Rad Laboratories; conventional 96- or 384-well plates; or microfluidic devices such as the Dynamic Arrays from Fluidigm Corporation. HTqPCR handles data loading, quality assessment, normalization, visualization and parametric or non-parametric testing for statistical significance in Ct values between features (e.g. genes, microRNAs).

The objective of AGDEX is to evaluate whether the results of a pair of two-group differential expression analysis comparisons show a level of agreement that is greater than expected if the group labels for each two-group comparison are randomly assigned. The agreement is evaluated for the entire transcriptome and (optionally) for a collection of pre-defined gene-sets. Additionally, the procedure performs permutation-based differential expression and meta analysis at both gene and gene-set levels of the data from each experiment.

This package provides a method for finding an enrichment of cancer simple somatic mutations (SNVs and Indels) in functional elements across the human genome. ActiveDriverWGS detects coding and noncoding driver elements using whole genome sequencing data.

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.

This package provides tools to analyze and visualize high-throughput metabolomics data acquired using chromatography-mass spectrometry. These tools preprocess data in a way that enables reliable and powerful differential analysis.

This package provides a software suite for the automated analysis of Affymetrix arrays.

This package provides full genome sequences for Mus musculus (Mouse) as provided by UCSC (mm9, Jul. 2007) and stored in Biostrings objects. The sequences are the same as in BSgenome.Mmusculus.UCSC.mm9, 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 adopts tipping-point theory to transcriptome profiles to help unravel disease regulatory trajectory.

This is an annotation package for Illumina's EPIC methylation arrays.

This package provides statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics.

This package provides a set of tools to for machine and deep learning in R from amino acid and nucleotide sequences focusing on adaptive immune receptors. The package includes pre-processing of sequences, unifying gene nomenclature usage, encoding sequences, and combining models. This package will serve as the basis of future immune receptor sequence functions/packages/models compatible with the scRepertoire ecosystem.

Many modern biological datasets consist of small counts that are not well fit by standard linear-Gaussian methods such as principal component analysis. This package provides implementations of count-based feature selection and dimension reduction algorithms. These methods can be used to facilitate unsupervised analysis of any high-dimensional data such as single-cell RNA-seq.

This package efficiently obtains count vectors from indexed bam files. It counts the number of nucleotide sequence reads in given genomic ranges and it computes reads profiles and coverage profiles. It also handles paired-end data.

This package implements an expiration system for access to versioned directories. Directories that have not been accessed by a registered function within a certain time frame are deleted. This aims to reduce disk usage by eliminating obsolete caches generated by old versions of packages.

Explore and download data from the recount project available at https://jhubiostatistics.shinyapps.io/recount/. Using the recount package you can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level, the raw counts, the phenotype metadata used, the urls to the sample coverage bigWig files or the mean coverage bigWig file for a particular study. The RangedSummarizedExperiment objects can be used by different packages for performing differential expression analysis. Using http://bioconductor.org/packages/derfinder you can perform annotation-agnostic differential expression analyses with the data from the recount project as described at https://www.nature.com/nbt/journal/v35/n4/full/nbt.3838.html.