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.

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.

This package provides datasets needed for ChAMP including a test dataset and blood controls for CNA analysis.

This package provides full genome sequences for Homo sapiens as provided by UCSC (hg19, February 2009) and stored in Biostrings objects.

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 a database of SIFT predictions for Homo sapiens dbSNP build 132.

This package provides a collection of functions and classes which serve as the foundation for packages such as PharmacoGx and RadioGx. It was created to abstract shared functionality to increase ease of maintainability and reduce code repetition in current and future Gx suite programs. Major features include a CoreSet class, from which RadioSet and PharmacoSet are derived, along with get and set methods for each respective slot. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating AUC or SF are included.

This package provides tools for analysis of ChIP-seq and other functional sequencing data.

This package provides full genome sequences for Danio rerio (Zebrafish) as provided by UCSC (danRer11, May 2017) and stored in Biostrings objects.

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.

This package provides a collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control.

This package provides various wrapper functions that have been written to streamline the more common analyses that a Biostatistician might see.

This package provides full genome sequences for Homo sapiens (Human) as provided by UCSC (hg38, Dec. 2013) and stored in Biostrings objects.

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

This package adopts tipping-point theory to transcriptome profiles to help unravel disease regulatory trajectory.

This package provides a dplyr-like interface for interacting with the common Bioconductor classes Ranges and GenomicRanges. By providing a grammatical and consistent way of manipulating these classes their accessibility for new Bioconductor users is hopefully increased.

iClusterPlus is developed for integrative clustering analysis of multi-type genomic data and is an enhanced version of iCluster proposed and developed by Shen, Olshen and Ladanyi (2009). Multi-type genomic data arise from the experiments where biological samples (e.g. tumor samples) are analyzed by multiple techniques, for instance, array comparative genomic hybridization (aCGH), gene expression microarray, RNA-seq and DNA-seq, and so on. In the iClusterPlus model, binary observations such as somatic mutation are modeled as Binomial processes; categorical observations such as copy number states are realizations of Multinomial random variables; counts are modeled as Poisson random processes; and continuous measures are modeled by Gaussian distributions.

This package provides data from 3 technical replicates of the cell line GM12878 from the EPIC methylation array.

This package installs a self-contained Conda instance that is managed by the R/Bioconductor installation machinery. This aims to provide a consistent Python environment that can be used reliably by Bioconductor packages. Functions are also provided to enable smooth interoperability of multiple Python environments in a single R session.

Basic4Cseq is an R package for basic filtering, analysis and subsequent visualization of 4C-seq data. Virtual fragment libraries can be created for any BSGenome package, and filter functions for both reads and fragments and basic quality controls are included. Fragment data in the vicinity of the experiment's viewpoint can be visualized as a coverage plot based on a running median approach and a multi-scale contact profile.

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 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.

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

This is a package for significance analysis of Microarrays for differential expression analysis, RNAseq data and related problems.