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

This package implements methods to remove unwanted variation (RUV) of Risso et al. (2014) for the normalization of RNA-Seq read counts between samples.

This package provides full genome sequences for Mus musculus (Mouse) as provided by UCSC (mm9, Jul. 2007) and stored in Biostrings objects. The sequences are the same as in BSgenome.Mmusculus.UCSC.mm9, except that each of them has the 4 following masks on top: (1) the mask of assembly gaps (AGAPS mask), (2) the mask of intra-contig ambiguities (AMB mask), (3) the mask of repeats from RepeatMasker (RM mask), and (4) the mask of repeats from Tandem Repeats Finder (TRF mask). Only the AGAPS and AMB masks are "active" by default.

This package is a visualization and analysis toolbox for short time course data which includes dimensionality reduction, clustering, two-sample differential expression testing and gene ranking techniques. The package also provides methods for retrieving enriched pathways.

This package provides functions for bipartite network rewiring through N consecutive switching steps and for the computation of the minimal number of switching steps to be performed in order to maximise the dissimilarity with respect to the original network. It includes functions for the analysis of the introduced randomness across the switching steps and several other routines to analyse the resulting networks and their natural projections.

This package provides a visual and interactive web application using RStudio's shiny package. Bad quality samples are detected using sample-dependent and sample-independent controls present on the array and user adjustable thresholds. In depth exploration of bad quality samples can be performed using several interactive diagnostic plots of the quality control probes present on the array. Furthermore, the impact of any batch effect provided by the user can be explored.

The package xmapbridge can plot graphs in the X:Map genome browser. X:Map uses the Google Maps API to provide a scrollable view of the genome. It supports a number of species, and can be accessed at http://xmap.picr.man.ac.uk. This package exports plotting files in a suitable format. Graph plotting in R is done using calls to the functions xmap.plot and xmap.points, which have parameters that aim to be similar to those used by the standard plot methods in R. These result in data being written to a set of files (in a specific directory structure) that contain the data to be displayed, as well as some additional meta-data describing each of the graphs.

This package provides raw data objects to be used for blood cell proportion estimation in minfi and similar packages. The FlowSorted.Blood.EPIC object is based in samples assayed by Brock Christensen and colleagues; for details see Salas et al. 2018. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE110554.

This package provides functions for pathway analysis based on the REACTOME pathway database. It implements enrichment analysis, gene set enrichment analysis and several functions for visualization.

This package provides full genome sequences for Caenorhabditis elegans (Worm) as provided by UCSC (ce6, May 2008) and stored in Biostrings objects.

This is a package to perform the zFPKM transform on RNA-seq FPKM data. This algorithm is based on the publication by Hart et al., 2013 (Pubmed ID 24215113).

This package is a collection of Strand-seq data. The main purpose is to demonstrate functionalities of the breakpointR package.

This package contains default datasets used by the Bioconductor package biscuiteer.

This package provides a manifest for Illumina's 450k array data.

This package wires together large collections of single-cell RNA-seq datasets, which allows for both the identification of recurrent cell clusters and the propagation of information between datasets in multi-sample or atlas-scale collections. Conos focuses on the uniform mapping of homologous cell types across heterogeneous sample collections. For instance, users could investigate a collection of dozens of peripheral blood samples from cancer patients combined with dozens of controls, which perhaps includes samples of a related tissue such as lymph nodes.

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

This package provides a collection of tools for cancer genomic data clustering analyses, including those for single cell RNA-seq. Cell clustering and feature gene selection analysis employ Bayesian (and maximum likelihood) non-negative matrix factorization (NMF) algorithm. Input data set consists of RNA count matrix, gene, and cell bar code annotations. Analysis outputs are factor matrices for multiple ranks and marginal likelihood values for each rank. The package includes utilities for downstream analyses, including meta-gene identification, visualization, and construction of rank-based trees for clusters.

This package provides a pipeline for analysing Capture Hi-C data.

Genomic data analyses requires integrated visualization of known genomic information and new experimental data. Gviz uses the biomaRt and the rtracklayer packages to perform live annotation queries to Ensembl and UCSC and translates this to e.g. gene/transcript structures in viewports of the grid graphics package. This results in genomic information plotted together with your data.

Expression levels of mRNA molecules are regulated by different processes, comprising inhibition or activation by transcription factors and post-transcriptional degradation by microRNAs. birta (Bayesian Inference of Regulation of Transcriptional Activity) uses the regulatory networks of transcription factors and miRNAs together with mRNA and miRNA expression data to predict switches in regulatory activity between two conditions. A Bayesian network is used to model the regulatory structure and Markov-Chain-Monte-Carlo is applied to sample the activity states.

MultiBaC is a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. MultiBaC is able to remove batch effects across different omics generated within separate batches provided that at least one common omic data type is included in all the batches considered.

This package provides the data that were used in the http://quinlanlab.org/tutorials/bedtools/bedtools.html. It includes a subset of the DnaseI hypersensitivity data from "Maurano et al. Systematic Localization of Common Disease-Associated Variation in Regulatory DNA. Science. 2012. Vol. 337 no. 6099 pp. 1190-1195." The rest of the tracks were originally downloaded from the UCSC table browser. See the HelloRanges vignette for a port of the bedtools tutorial to R.

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

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.