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

RnBeads facilitates comprehensive analysis of various types of DNA methylation data at the genome scale.

This package provides the HTSlib C library for high-throughput nucleotide sequence analysis. The package is primarily useful to developers of other R packages who wish to make use of HTSlib.

The QFeatures infrastructure enables the management and processing of quantitative features for high-throughput mass spectrometry assays. It provides a familiar Bioconductor user experience to manages quantitative data across different assay levels (such as peptide spectrum matches, peptides and proteins) in a coherent and tractable format.

This package lets you carry out network-based gene set analysis by incorporating external information about interactions among genes, as well as novel interactions learned from data. It implements methods described in Shojaie A, Michailidis G (2010) <doi:10.1093/biomet/asq038>, Shojaie A, Michailidis G (2009) <doi:10.1089/cmb.2008.0081>, and Ma J, Shojaie A, Michailidis G (2016) <doi:10.1093/bioinformatics/btw410>.

This package provides tools for exporting and importing classification trees and clusters to other programs.

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

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 package provides tools for Bayesian integrated analysis of Affymetrix GeneChips.

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 package is importing data from Illumina SNP experiments and it performs copy number calculations and reports.

This package provides functions to add metadata to ExperimentHub db and resource files to AWS S3 buckets.

This package manages the installation of CMake for building Bioconductor packages. This avoids the need for end-users to manually install CMake on their system. No action is performed if a suitable version of CMake is already available.

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.

satuRn provides a framework for performing differential transcript usage analyses. The package consists of three main functions. The first function, fitDTU, fits quasi-binomial generalized linear models that model transcript usage in different groups of interest. The second function, testDTU, tests for differential usage of transcripts between groups of interest. Finally, plotDTU visualizes the usage profiles of transcripts in groups of interest.

This package helps with the analysis of array CGH data by detecting of the breakpoints in the genomic profiles and assignment of a status (gain, normal or loss) to each chromosomal regions identified.

TreeSummarizedExperiment extends SingleCellExperiment to include hierarchical information on the rows or columns of the rectangular data.

This package provides tools for representing and modeling data in the EMBL-EBI GWAS catalog.

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.

Microarray quality assessment is a major concern of microarray analysts. This package provides some simple approaches to in silico creation of quality problems in CEL-level data to help evaluate performance of quality metrics.

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 tools to acquire, annotate, convert and store data for use in Bioconductor’s AnnotationHub.

This package provides methods for working with Illumina arrays using the gdsfmt package.

Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (dropout imputation). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. The ADImpute package proposes two methods to address this issue:

1. a gene regulatory network-based approach using gene-gene relationships learnt from external data;

2. a baseline approach corresponding to a sample-wide average.

ADImpute implements these novel methods and also combines them with existing imputation methods like DrImpute and SAVER. ADImpute can learn the best performing method per gene and combine the results from different methods into an ensemble.