The genome is divided into non-overlapping fixed-sized bins, number of sequence reads in each counted, adjusted with a simultaneous two-dimensional loess correction for sequence mappability and GC content, and filtered to remove spurious regions in the genome. Downstream steps of segmentation and calling are also implemented via packages DNAcopy and CGHcall, respectively.

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

The ReportingTools package enables users to easily display reports of analysis results generated from sources such as microarray and sequencing data. The package allows users to create HTML pages that may be viewed on a web browser, or in other formats. Users can generate tables with sortable and filterable columns, make and display plots, and link table entries to other data sources such as NCBI or larger plots within the HTML page. Using the package, users can also produce a table of contents page to link various reports together for a particular project that can be viewed in a web browser.

This package aggregateBioVar contains tools to summarize single cell gene expression profiles at the level of subject for single cell RNA-seq data collected from more than one subject (e.g. biological sample or technical replicates). A SingleCellExperiment object is taken as input and converted to a list of SummarizedExperiment objects, where each list element corresponds to an assigned cell type. The SummarizedExperiment objects contain aggregate gene-by-subject count matrices and inter-subject column metadata for individual subjects that can be processed using downstream bulk RNA-seq tools.

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 exposes a C elegans annotation database generated from UCSC by exposing these as TxDb objects.

This is a manifest package for Illumina's EPIC methylation arrays.

This is a package for biclustering analysis and exploration of results.

This package provides functions for visualizing hypergraphs.

Genome wide studies of translational control is emerging as a tool to study various biological conditions. The output from such analysis is both the mRNA level (e.g. cytosolic mRNA level) and the level of mRNA actively involved in translation (the actively translating mRNA level) for each mRNA. The standard analysis of such data strives towards identifying differential translational between two or more sample classes - i.e., differences in actively translated mRNA levels that are independent of underlying differences in cytosolic mRNA levels. This package allows for such analysis using partial variances and the random variance model. As 10s of thousands of mRNAs are analyzed in parallel the library performs a number of tests to assure that the data set is suitable for such analysis.

This package makes GREAT (Genomic Regions Enrichment of Annotations Tool) analysis automatic by constructing a HTTP POST request according to user's input and automatically retrieving results from GREAT web server.

This package is designed to ease the application and comparison of multiple hypothesis testing procedures for FWER, gFWER, FDR and FDX. Methods are standardized and usable by the accompanying mutossGUI package.

This is a comprehensive package to automatically train and validate a multi-class SVM classifier based on gene expression data. It provides transparent selection of gene markers, their coexpression networks, and an interface to query the classifier.

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.

MultiAssayExperiment harmonizes data management of multiple assays performed on an overlapping set of specimens. It provides a familiar Bioconductor user experience by extending concepts from SummarizedExperiment, supporting an open-ended mix of standard data classes for individual assays, and allowing subsetting by genomic ranges or rownames.

The sparse nature of single cell epigenomics data can be overruled using probabilistic modelling methods such as Latent Dirichlet Allocation (LDA). This package allows the probabilistic modelling of cis-regulatory topics (cisTopics) from single cell epigenomics data, and includes functionalities to identify cell states based on the contribution of cisTopics and explore the nature and regulatory proteins driving them.

Explore, diagnose, and compare variant calls using filters. The VariantTools package supports a workflow for loading data, calling single sample variants and tumor-specific somatic mutations or other sample-specific variant types (e.g., RNA editing). Most of the functions operate on alignments (BAM files) or datasets of called variants. The user is expected to have already aligned the reads with a separate tool, e.g., GSNAP via gmapR.

The dada2 package infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, and outputs the sequence variants and their sample-wise abundances after removing substitution and chimera errors. Taxonomic classification is available via a native implementation of the RDP naive Bayesian classifier, and species-level assignment to 16S rRNA gene fragments by exact matching.

This package provides user interface and database connection code for annotation data packages using SQLite data storage.

The mia package implements tools for microbiome analysis based on the SummarizedExperiment, SingleCellExperiment and TreeSummarizedExperiment infrastructure. Data wrangling and analysis in the context of taxonomic data is the main scope. Additional functions for common task are implemented such as community indices calculation and summarization.

Latent variable modeling with Principal Component Analysis (PCA) and Partial Least Squares (PLS) are powerful methods for visualization, regression, classification, and feature selection of omics data where the number of variables exceeds the number of samples and with multicollinearity among variables. Orthogonal Partial Least Squares (OPLS) enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation. While performing similarly to PLS, OPLS facilitates interpretation.

This package provides imlementations of PCA, PLS, and OPLS for multivariate analysis and feature selection of omics data. In addition to scores, loadings and weights plots, the package provides metrics and graphics to determine the optimal number of components (e.g. with the R2 and Q2 coefficients), check the validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients).

This package provides Affymetrix Human Genome U95 Set annotation data (hgu95av2) assembled using data from public data repositories.

This package is to find SNV/Indel differences between two bam files with near relationship in a way of pairwise comparison through each base position across the genome region of interest. The difference is inferred by Fisher test and euclidean distance, the input of which is the base count (A,T,G,C) in a given position and read counts for indels that span no less than 2bp on both sides of indel region.

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.