This package provides supporting annotation and test data for SeSAMe package. This includes chip tango addresses, mapping information, performance annotation, and trained predictor for Infinium array data. This package provides user access to essential annotation data for working with many generations of the Infinium DNA methylation array. It currently supports human array (HM27, HM450, EPIC), mouse array (MM285) and the HorvathMethylChip40 (Mammal40) array.

This package provides S4 generic functions modeled after the matrixStats API for alternative matrix implementations. Packages with alternative matrix implementation can depend on this package and implement the generic functions that are defined here for a useful set of row and column summary statistics. Other package developers can import this package and handle a different matrix implementations without worrying about incompatibilities.

The NCBI Gene Expression Omnibus (GEO) is a public repository of microarray data. Given the rich and varied nature of this resource, it is only natural to want to apply BioConductor tools to these data. GEOquery is the bridge between GEO and BioConductor.

The Seqinfo class stores the names, lengths, circularity flags, and genomes for a particular collection of sequences. These sequences are typically the chromosomes and/or scaffolds of a specific genome assembly of a given organism. Seqinfo objects are rarely used as standalone objects. Instead, they are used as part of higher-level objects to represent their seqinfo() component. Examples of such higher-level objects are GRanges, RangedSummarizedExperiment, VCF, GAlignments, etc… defined in other Bioconductor infrastructure packages.

This package provides an implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and ResultSet. MultiDataSet is designed for integrating multi omics data sets and ResultSet is a container for omics results. This package contains base classes for MEAL and rexposome packages.

This package defines an S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metadata for genes and libraries.

ASEB is an R package to predict lysine sites that can be acetylated by a specific KAT (K-acetyl-transferases) family. Lysine acetylation is a well-studied posttranslational modification on kinds of proteins. About four thousand lysine acetylation sites and over 20 lysine KATs have been identified. However, which KAT is responsible for a given protein or lysine site acetylation is mostly unknown. In this package, we use a GSEA-like (Gene Set Enrichment Analysis) method to make predictions. GSEA method was developed and successfully used to detect coordinated expression changes and find the putative functions of the long non-coding RNAs.

The bayNorm package is used for normalizing single-cell RNA-seq data. The main function is bayNorm, which is a wrapper function for gene specific prior parameter estimation and normalization. The input is a matrix of scRNA-seq data with rows different genes and columns different cells. The output is either point estimates from posterior (2D array) or samples from posterior (3D array).

This package provides mass-spectrometry based spatial proteomics data sets and protein complex separation data. It also contains the time course expression experiment from Mulvey et al. (2015).

This package contains the functions to find the gene expression modules that represent the drivers of Kauffman's attractor landscape. The modules are the core attractor pathways that discriminate between different cell types of groups of interest. Each pathway has a set of synexpression groups, which show transcriptionally-coordinated changes in gene expression.

The BADER package is intended for the analysis of RNA sequencing data. The algorithm fits a Bayesian hierarchical model for RNA sequencing count data. BADER returns the posterior probability of differential expression for each gene between two groups A and B. The joint posterior distribution of the variables in the model can be returned in the form of posterior samples, which can be used for further down-stream analyses such as gene set enrichment.

This package implements the unified Wilcoxon-Mann-Whitney Test for qPCR data. This modified test allows for testing differential expression in qPCR data.

Many modern biological datasets consist of small counts that are not well fit by standard linear-Gaussian methods such as principal component analysis. This package provides implementations of count-based feature selection and dimension reduction algorithms. These methods can be used to facilitate unsupervised analysis of any high-dimensional data such as single-cell RNA-seq.

Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well.

The objective of AGDEX is to evaluate whether the results of a pair of two-group differential expression analysis comparisons show a level of agreement that is greater than expected if the group labels for each two-group comparison are randomly assigned. The agreement is evaluated for the entire transcriptome and (optionally) for a collection of pre-defined gene-sets. Additionally, the procedure performs permutation-based differential expression and meta analysis at both gene and gene-set levels of the data from each experiment.

The SummarizedExperiment container contains one or more assays, each represented by a matrix-like object of numeric or other mode. The rows typically represent genomic ranges of interest and the columns represent samples.

This package implements functions for copy number variant calling, plotting, export and analysis from whole-genome single cell sequencing data.

This is the human disease ontology R package HDO.db, which provides the semantic relationship between human diseases. Relying on the DOSE and GOSemSim packages, this package can carry out disease enrichment and semantic similarity analyses. Many biological studies are achieved through mouse models, and a large number of data indicate the association between genotypes and phenotypes or diseases. The study of model organisms can be transformed into useful knowledge about normal human biology and disease to facilitate treatment and early screening for diseases. Organism-specific genotype-phenotypic associations can be applied to cross-species phenotypic studies to clarify previously unknown phenotypic connections in other species. Using the same principle to diseases can identify genetic associations and even help to identify disease associations that are not obvious.

This package provides RcisTarget databases: Gene-based motif rankings and annotation to transcription factors. This package contains a subset of 4.6k motifs (cisbp motifs), scored only within 500bp upstream and the TSS. See RcisTarget tutorial to download the full databases, containing 20k motifs and search space up to 10kbp around the TSS.

This package provides the headers and static library of Protocol buffers for other R packages to compile and link against.

This is a package providing tools to quantify and interpret multiple sources of biological and technical variation in gene expression experiments. It uses a linear mixed model to quantify variation in gene expression attributable to individual, tissue, time point, or technical variables. The package includes dream differential expression analysis for repeated measures.

This package provides R functions for common pre-processing steps that are applied on 1H-NMR data. It also provides a function to read the FID signals directly in the Bruker format.

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.

Given a set of genomic sites/regions (e.g. ChIP-seq peaks, CpGs, differentially methylated CpGs or regions, SNPs, etc.) it is often of interest to investigate the intersecting genomic annotations. Such annotations include those relating to gene models (promoters, 5'UTRs, exons, introns, and 3'UTRs), CpGs (CpG islands, CpG shores, CpG shelves), or regulatory sequences such as enhancers. The annotatr package provides an easy way to summarize and visualize the intersection of genomic sites/regions with genomic annotations.