This package provides a set of tools for interacting with GO and microarray data. A variety of basic manipulation tools for graphs, hypothesis testing and other simple calculations.

This package implements widgets to provide user interfaces.

ASICS quantifies concentration of metabolites in a complex spectrum. The identification of metabolites is performed by fitting a mixture model to the spectra of the library with a sparse penalty.

This package implements exact and approximate methods for singular value decomposition and principal components analysis, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Where possible, parallelization is achieved using the BiocParallel framework.

The package includes functions to retrieve the sequences around the peak, obtain enriched Gene Ontology (GO) terms, find the nearest gene, exon, miRNA or custom features such as most conserved elements and other transcription factor binding sites supplied by users. Starting 2.0.5, new functions have been added for finding the peaks with bi-directional promoters with summary statistics (peaksNearBDP), for summarizing the occurrence of motifs in peaks (summarizePatternInPeaks) and for adding other IDs to annotated peaks or enrichedGO (addGeneIDs).

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.

GOfuncR performs a gene ontology enrichment analysis based on the ontology enrichment software FUNC. GO-annotations are obtained from OrganismDb or OrgDb packages (Homo.sapiens by default); the GO-graph is included in the package and updated regularly. GOfuncR provides the standard candidate vs background enrichment analysis using the hypergeometric test, as well as three additional tests:

1. the Wilcoxon rank-sum test that is used when genes are ranked,

2. a binomial test that is used when genes are associated with two counts, and

3. a Chi-square or Fisher's exact test that is used in cases when genes are associated with four counts.

To correct for multiple testing and interdependency of the tests, family-wise error rates are computed based on random permutations of the gene-associated variables. GOfuncR also provides tools for exploring the ontology graph and the annotations, and options to take gene-length or spatial clustering of genes into account. It is also possible to provide custom gene coordinates, annotations and ontologies.

BiocSet displays different biological sets in a triple tibble format. These three tibbles are element, set, and elementset. The user has the ability to activate one of these three tibbles to perform common functions from the dplyr package. Mapping functionality and accessing web references for elements/sets are also available in BiocSet.

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.

The ggbio package extends and specializes the grammar of graphics for biological data. The graphics are designed to answer common scientific questions, in particular those often asked of high throughput genomics data. All core Bioconductor data structures are supported, where appropriate. The package supports detailed views of particular genomic regions, as well as genome-wide overviews. Supported overviews include ideograms and grand linear views. High-level plots include sequence fragment length, edge-linked interval to data view, mismatch pileup, and several splicing summaries.

The parody package provides routines for univariate and multivariate outlier detection with a focus on parametric methods, but support for some methods based on resistant statistics.

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 package offers simple statistical identification of contaminating sequence features in marker-gene or metagenomics data. It works on any kind of feature derived from environmental sequencing data (e.g. ASVs, OTUs, taxonomic groups, MAGs, etc). Requires DNA quantitation data or sequenced negative control samples.

This is a package providing tools to quantify and interpret multiple sources of biological and technical variation in gene expression experiments. It uses a linear mixed model to quantify variation in gene expression attributable to individual, tissue, time point, or technical variables. The package includes dream differential expression analysis for repeated measures.

The package contains 8 BAM files, 1 per sequencing run. Each BAM file was obtained by aligning the reads (paired-end) to the full hg19 genome with TopHat2, and then subsetting to keep only alignments on chr14. See accession number E-MTAB-1147 in the ArrayExpress database for details about the experiment, including links to the published study (by Zarnack et al., 2012) and to the FASTQ files.

This package provides functions for handling data from Bioconductor Affymetrix annotation data packages. It produces compact HTML and text reports including experimental data and URL links to many online databases. It allows searching of biological metadata using various criteria.

This package provides tools to accurately estimate cell type abundances from heterogeneous bulk expression. A reference-based method utilizes single-cell information to generate a signature matrix and transformation of bulk expression for accurate regression based estimates. A marker-based method utilizes known cell-specific marker genes to measure relative abundances across samples.

The package provides utility functions related to package development. These include functions that replace slots, and selectors for show methods. It aims to coalesce the various helper functions often re-used throughout the Bioconductor ecosystem.

This package provides an R wrapper of the popular bowtie2 sequencing reads aligner and AdapterRemoval, a convenient tool for rapid adapter trimming, identification, and read merging.

This package is a collection of gene expression data from a breast cancer study published in Wang et al. 2005 and Minn et al 2007.

This package exposes an annotation databases generated from UCSC by exposing these as TxDb objects.

This package is Cytometry dATa anALYSis Tools (CATALYST). Mass cytometry like Cytometry by time of flight (CyTOF) uses heavy metal isotopes rather than fluorescent tags as reporters to label antibodies, thereby substantially decreasing spectral overlap and allowing for examination of over 50 parameters at the single cell level. While spectral overlap is significantly less pronounced in CyTOF than flow cytometry, spillover due to detection sensitivity, isotopic impurities, and oxide formation can impede data interpretability. CATALYST was designed to provide a pipeline for preprocessing of cytometry data, including:

1. normalization using bead standards;

2. single-cell deconvolution;

3. bead-based compensation.

This package annmap provides annotation mappings for Affymetrix exon arrays and coordinate based queries to support deep sequencing data analysis. Database access is hidden behind the API which provides a set of functions such as genesInRange(), geneToExon(), exonDetails(), etc. Functions to plot gene architecture and BAM file data are also provided.

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