This package provides classes for storing very large GWAS data sets and annotation, and functions for GWAS data cleaning and analysis.

This package provides a collection of software tools for calculating distance measures.

This package provides functionalities for downstream analysis, annotation and visualizaton of alternative splicing events generated by rMATS.

This is a package for the automated analysis of Affymetrix arrays. It is used for preprocessing the arrays.

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 platform design info for Affymetrix Mapping50K_Hind240.

This package facilitates phyloseq exploration and analysis of taxonomic profiling data. This package provides tools for the manipulation, statistical analysis, and visualization of taxonomic profiling data. In addition to targeted case-control studies, microbiome facilitates scalable exploration of population cohorts. This package supports the independent phyloseq data format and expands the available toolkit in order to facilitate the standardization of the analyses and the development of best practices.

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.

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 discovers differential features in hetero- and homogeneous omic data by a two-step method including subsampling LIMMA and NSCA. DECO reveals feature associations to hidden subclasses not exclusively related to higher deregulation levels.

This package provides tools for Bayesian integrated analysis of Affymetrix GeneChips.

This package contains gene-level counts for a collection of public scRNA-seq datasets, provided as SingleCellExperiment objects with cell- and gene-level metadata.

This package provides functions for annotation-agnostic differential expression analysis of RNA-seq data. Two implementations of the DER Finder approach are included in this package:

1. single base-level F-statistics and

2. DER identification at the expressed regions-level.

The DER Finder approach can also be used to identify differentially bounded ChIP-seq peaks.

This package provides a suite of methods for powerful and robust microbiome data analysis addressing zero-inflation, phylogenetic structure and compositional effects. The methods can be applied to the analysis of other (high-dimensional) compositional data arising from sequencing experiments.

BioNERO aims to integrate all aspects of biological network inference in a single package, including data preprocessing, exploratory analyses, network inference, and analyses for biological interpretations. BioNERO can be used to infer gene coexpression networks (GCNs) and gene regulatory networks (GRNs) from gene expression data. Additionally, it can be used to explore topological properties of protein-protein interaction (PPI) networks. GCN inference relies on the popular WGCNA algorithm. GRN inference is based on the "wisdom of the crowds" principle, which consists in inferring GRNs with multiple algorithms (here, CLR, GENIE3 and ARACNE) and calculating the average rank for each interaction pair. As all steps of network analyses are included in this package, BioNERO makes users avoid having to learn the syntaxes of several packages and how to communicate between them. Finally, users can also identify consensus modules across independent expression sets and calculate intra and interspecies module preservation statistics between different networks.

This package provides Affymetrix HG_U95A Array annotation data (chip hgu95a) assembled using data from public repositories.

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

This package performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently.

This package is devoted to analyzing high-throughput data (e.g. gene expression microarray, DNA methylation microarray, RNA-seq) from complex tissues. Current functionalities include

1. detect cell-type specific or cross-cell type differential signals

2. tree-based differential analysis

3. improve variable selection in reference-free deconvolution

4. partial reference-free deconvolution with prior knowledge.

This package provides functions for calculation and visualization of performance metrics for evaluation of ranking and binary classification (assignment) methods. It also contains a Shiny application for interactive exploration of results.

The package alpine helps to model bias parameters and then using those parameters to estimate RNA-seq transcript abundance. Alpine is a package for estimating and visualizing many forms of sample-specific biases that can arise in RNA-seq, including fragment length distribution, positional bias on the transcript, read start bias (random hexamer priming), and fragment GC-content (amplification). It also offers bias-corrected estimates of transcript abundance in FPKM(Fragments Per Kilobase of transcript per Million mapped reads). It is currently designed for un-stranded paired-end RNA-seq data.

GAGE is a published method for gene set (enrichment or GSEA) or pathway analysis. GAGE is generally applicable independent of microarray or RNA-Seq data attributes including sample sizes, experimental designs, assay platforms, and other types of heterogeneity. The gage package provides functions for basic GAGE analysis, result processing and presentation. In addition, it provides demo microarray data and commonly used gene set data based on KEGG pathways and GO terms. These functions and data are also useful for gene set analysis using other methods.

This package provides a framework for allele-specific expression investigation using RNA-seq data.

This package provides a framework to perform Non-negative Matrix Factorization (NMF). The package implements a set of already published algorithms and seeding methods, and provides a framework to test, develop and plug new or custom algorithms. Most of the built-in algorithms have been optimized in C++, and the main interface function provides an easy way of performing parallel computations on multicore machines.