This is a supportive data package for the software package gage. However, the data supplied here are also useful for gene set or pathway analysis or microarray data analysis in general. In this package, we provide two demo microarray dataset: GSE16873 (a breast cancer dataset from GEO) and BMP6 (originally published as an demo dataset for GAGE, also registered as GSE13604 in GEO). This package also includes commonly used gene set data based on KEGG pathways and GO terms for major research species, including human, mouse, rat and budding yeast. Mapping data between common gene IDs for budding yeast are also included.

Phyloseq provides a set of classes and tools to facilitate the import, storage, analysis, and graphical display of microbiome census data.

This package provides summarized MinION sequencing data for Salmonella Typhi published by Ashton et al. in 2015. Three replicate runs are each provided as Fast5Summary objects.

This is a package for the assessment and comparison of the performance of risk prediction (survival) models.

This is a package for segmentation of allele-specific DNA copy number data and detection of regions with abnormal copy number within each parental chromosome. Both tumor-normal paired and tumor-only analyses are supported.

This package provides infrastructure for parallel computations distributed by file or by range. User defined mapper and reducer functions provide added flexibility for data combination and manipulation.

This package provides an implementation of an algorithm for determining cluster count and membership by stability evidence in unsupervised analysis.

The epigenomics road map describes locations of epigenetic marks in DNA from a variety of cell types. Of interest are locations of histone modifications, sites of DNA methylation, and regions of accessible chromatin. This package presents a selection of elements of the road map including metadata and outputs of the ChromImpute procedure applied to ENCODE cell lines by Ernst and Kellis.

This is a package for saving SingleCellExperiment into file artifacts, and loading them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

It has been shown that both DNA methylation and RNA transcription are linked to chronological age and age related diseases. Several estimators have been developed to predict human aging from DNA level and RNA level. Most of the human transcriptional age predictor are based on microarray data and limited to only a few tissues. To date, transcriptional studies on aging using RNASeq data from different human tissues is limited. The aim of this package is to provide a tool for across-tissue and tissue-specific transcriptional age calculation based on GTEx RNASeq data.

The package ASGSCA (Association Study using Generalized Structured Component Analysis) provides tools to model and test the association between multiple genotypes and multiple traits, taking into account the prior biological knowledge. Genes, and clinical pathways are incorporated in the model as latent variables.

This package contains an implementation of AIMS -- Absolute Intrinsic Molecular Subtyping. It contains necessary functions to assign the five intrinsic molecular subtypes (Luminal A, Luminal B, Her2-enriched, Basal-like, Normal-like). Assignments could be done on individual samples as well as on dataset of gene expression data.

This package provides tools for quality control, analysis and visualization of Illumina DNA methylation array data.

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

This package contains the basic methods needed to generate interactive Shiny-based display methods for Bioconductor objects.

This package provides methods to convert between Python AnnData objects and SingleCellExperiment objects. These are primarily intended for use by downstream Bioconductor packages that wrap Python methods for single-cell data analysis. It also includes functions to read and write H5AD files used for saving AnnData objects to disk.

The package implements an algorithm for fast gene set enrichment analysis. Using the fast algorithm makes more permutations and gets more fine grained p-values, which allows using accurate standard approaches to multiple hypothesis correction.

This package provides an annotation database of Mouse genome data. It is derived from the UCSC mm9 genome and based on the "knownGene" track. The database is exposed as a TxDb object.

This is a package for saving GenomicRanges, IRanges and related data structures into file artifacts, and loading them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

This package exposes an annotation databases generated from UCSC by exposing these as TxDb objects.

This package implements a method to analyze single-cell RNA-seq data utilizing flexible Dirichlet Process mixture models. Genes with differential distributions of expression are classified into several interesting patterns of differences between two conditions. The package also includes functions for simulating data with these patterns from negative binomial distributions.

The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, and Hits) are implemented in the S4Vectors package itself.

This package implements `import()` and `export()` standard generics for importing and exporting biological data formats. `import()` supports whole-file as well as chunk-wise iterative import. The `import()` interface optionally provides a standard mechanism for 'lazy' access via `filter()` (on row or element-like components of the file resource), `select()` (on column-like components of the file resource) and `collect()`. The `import()` interface optionally provides transparent access to remote (e.g. via https) as well as local access. Developers can register a file extension, e.g., `.loom` for dispatch from character-based URIs to specific `import()` / `export()` methods based on classes representing file types, e.g., `LoomFile()`.

This package defines an S4 class for storing data from spatial -omics experiments. The class extends SingleCellExperiment to support storage and retrieval of additional information from spot-based and molecule-based platforms, including spatial coordinates, images, and image metadata. A specialized constructor function is included for data from the 10x Genomics Visium platform.