This package implements exact and approximate methods for nearest neighbor detection, in a framework that allows them to be easily switched within Bioconductor packages or workflows. The exact algorithm is implemented using pre-clustering with the k-means algorithm. Functions are also provided to search for all neighbors within a given distance. Parallelization is achieved for all methods using the BiocParallel framework.

This package provides infrastructure shared by all Biostrings-based genome data packages and support for efficient SNP representation.

This package provides a set of annotation maps describing the entire Disease Ontology.

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 provides the data that were used in the http://quinlanlab.org/tutorials/bedtools/bedtools.html. It includes a subset of the DnaseI hypersensitivity data from "Maurano et al. Systematic Localization of Common Disease-Associated Variation in Regulatory DNA. Science. 2012. Vol. 337 no. 6099 pp. 1190-1195." The rest of the tracks were originally downloaded from the UCSC table browser. See the HelloRanges vignette for a port of the bedtools tutorial to R.

This package provides delayed computation of a matrix of scaled and centered values. The result is equivalent to using the scale function but avoids explicit realization of a dense matrix during block processing. This permits greater efficiency in common operations, most notably matrix multiplication.

This package provides UCSC phastCons conservation scores for the human genome (hg19) calculated from multiple alignments with other 99 vertebrate species.

This is a package to perform the Adaptive Robust Regression method (ARRm) for the normalization of methylation data from the Illumina Infinium HumanMethylation 450k assay.

This package provides a package that provides a client interface to the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST server.

This package provides tools to identify cell populations in Flow Cytometry data using non-parametric clustering and segmented-regression-based change point detection.

This package awst (Asymmetric Within-Sample Transformation) that regularizes RNA-seq read counts and reduces the effect of noise on the classification of samples. AWST comprises two main steps: standardization and smoothing. These steps transform gene expression data to reduce the noise of the lowly expressed features, which suffer from background effects and low signal-to-noise ratio, and the influence of the highly expressed features, which may be the result of amplification bias and other experimental artifacts.

BANDITS is a Bayesian hierarchical model for detecting differential splicing of genes and transcripts, via DTU (differential transcript usage), between two or more conditions. The method uses a Bayesian hierarchical framework, which allows for sample specific proportions in a Dirichlet-Multinomial model, and samples the allocation of fragments to the transcripts. Parameters are inferred via MCMC (Markov chain Monte Carlo) techniques and a DTU test is performed via a multivariate Wald test on the posterior densities for the average relative abundance of transcripts.

This package is a computational tool box for radio-genomic analysis which integrates radio-response data, radio-biological modelling and comprehensive cell line annotations for hundreds of cancer cell lines. The RadioSet class enables creation and manipulation of standardized datasets including information about cancer cells lines, radio-response assays and dose-response indicators. Included methods allow fitting and plotting dose-response data using established radio-biological models along with quality control to validate results. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating AUC or SF are included.

This package provides classes and methods to support Gene Set Enrichment Analysis (GSEA).

This package provides high performance functions for row and column operations on sparse matrices. Currently, the optimizations are limited to data in the column sparse format.

This package provides functionality for running and comparing many different clusterings of single-cell sequencing data or other large mRNA expression data sets.

Starting with a BAM file, this package provides the necessary functions for quality assessment, read start position recalibration, the counting of genomic sequence reads on CDS, 3'UTR, and 5'UTR, and plotting of count data: pairs, log fold-change, codon frequency and coverage assessment, principal component analysis on codon coverage.

This package provides flexible, quantitative, and integrative genomic visualizations for publication-quality multi-panel figures.

This package provides software and data to support the case studies monograph.

This package provides a set of tools for making TxDb objects from genomic annotations from various sources (e.g. UCSC, Ensembl, and GFF files). These tools allow the user to download the genomic locations of transcripts, exons, and CDS, for a given assembly, and to import them in a TxDb object. TxDb objects are implemented in the GenomicFeatures package, together with flexible methods for extracting the desired features in convenient formats.

This package analyzes and creates plots of array CGH data. Also, it allows usage of CBS, wavelet-based smoothing, HMM, BioHMM, GLAD, CGHseg. Most computations are parallelized (either via forking or with clusters, including MPI and sockets clusters) and use ff for storing data.

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.

mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data.

This package implements functions for copy number variant calling, plotting, export and analysis from whole-genome single cell sequencing data.