This package provides functions for visualizing hypergraphs.

This package contains methods for converting standard objects constructed by bioinformatics packages, especially those in Bioconductor, and converting them to tidy data. It thus serves as a complement to the broom package, and follows the same tidy, augment, glance division of tidying methods. Tidying data makes it easy to recombine, reshape and visualize bioinformatics analyses.

This package offers interactive Shiny displays for Bioconductor objects. In addition, this package empowers users to develop engaging visualizations and interfaces for working with Bioconductor data.

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.

This package provides functions for reading array comparative genomic hybridization (aCGH) data from image analysis output files and clone information files, creation of aCGH objects for storing these data. Basic methods are accessing/replacing, subsetting, printing and plotting aCGH objects.

The ggbio package extends and specializes the grammar of graphics for biological data. The graphics are designed to answer common scientific questions, in particular those often asked of high throughput genomics data. All core Bioconductor data structures are supported, where appropriate. The package supports detailed views of particular genomic regions, as well as genome-wide overviews. Supported overviews include ideograms and grand linear views. High-level plots include sequence fragment length, edge-linked interval to data view, mismatch pileup, and several splicing summaries.

This package provides repository information for the appropriate version of Bioconductor.

Quickly find motif matches for many motifs and many sequences. This package wraps C++ code from the MOODS motif calling library.

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 is a package for normalization, testing for differential variability and differential methylation and gene set testing for data from Illumina's Infinium HumanMethylation arrays. The normalization procedure is subset-quantile within-array normalization (SWAN), which allows Infinium I and II type probes on a single array to be normalized together. The test for differential variability is based on an empirical Bayes version of Levene's test. Differential methylation testing is performed using RUV, which can adjust for systematic errors of unknown origin in high-dimensional data by using negative control probes. Gene ontology analysis is performed by taking into account the number of probes per gene on the array, as well as taking into account multi-gene associated probes.

This package provides data needed to use the ITALICS package.

Linnorm is an R package for the analysis of RNA-seq, scRNA-seq, ChIP-seq count data or any large scale count data. It transforms such datasets for parametric tests. In addition to the transformtion function (Linnorm), the following pipelines are implemented:

1. Library size/batch effect normalization (Linnorm.Norm)

2. Cell subpopluation analysis and visualization using t-SNE or PCA K-means clustering or hierarchical clustering (Linnorm.tSNE, Linnorm.PCA, Linnorm.HClust)

3. Differential expression analysis or differential peak detection using limma (Linnorm.limma)

4. Highly variable gene discovery and visualization (Linnorm.HVar)

5. Gene correlation network analysis and visualization (Linnorm.Cor)

6. Stable gene selection for scRNA-seq data; for users without or who do not want to rely on spike-in genes (Linnorm.SGenes)

7. Data imputation (Linnorm.DataImput).

Linnorm can work with raw count, CPM, RPKM, FPKM and TPM. Additionally, the RnaXSim function is included for simulating RNA-seq data for the evaluation of DEG analysis methods.

This package provides a flexible representation of copy number, mutation, and other data that fit into the ragged array schema for genomic location data. The basic representation of such data provides a rectangular flat table interface to the user with range information in the rows and samples/specimen in the columns. The RaggedExperiment class derives from a GRangesList representation and provides a semblance of a rectangular dataset.

The standard index of DNA methylation (beta) is computed from methylated and unmethylated signal intensities. Betas calculated from raw signal intensities perform well, but using 11 methylomic datasets we demonstrate that quantile normalization methods produce marked improvement. The commonly used procedure of normalizing betas is inferior to the separate normalization of M and U, and it is also advantageous to normalize Type I and Type II assays separately. This package provides 15 flavours of betas and three performance metrics, with methods for objects produced by the methylumi and minfi packages.

r-pathview is a tool set for pathway based data integration and visualization. It maps and renders a wide variety of biological data on relevant pathway graphs. All users need is to supply their data and specify the target pathway. This package automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and render pathway graph with the mapped data. In addition, r-pathview also seamlessly integrates with pathway and gene set (enrichment) analysis tools for large-scale and fully automated analysis.

This package provides full genome sequences for Danio rerio (Zebrafish) as provided by UCSC (danRer10, Sep. 2014) and stored in Biostrings objects.

r-circrnaprofiler is a computational framework for a comprehensive in silico analysis of circular RNA (circRNAs). This computational framework allows combining and analyzing circRNAs previously detected by multiple publicly available annotation-based circRNA detection tools. It covers different aspects of circRNAs analysis from differential expression analysis, evolutionary conservation, biogenesis to functional analysis.

This package provides quantitative variant callers for detecting subclonal mutations in ultra-deep (>=100x coverage) sequencing experiments. The deepSNV algorithm is used for a comparative setup with a control experiment of the same loci and uses a beta-binomial model and a likelihood ratio test to discriminate sequencing errors and subclonal SNVs. The shearwater algorithm computes a Bayes classifier based on a beta-binomial model for variant calling with multiple samples for precisely estimating model parameters - such as local error rates and dispersion - and prior knowledge, e.g. from variation data bases such as COSMIC.

This package interfaces R with the graphviz library for plotting R graph objects from the graph package.

The package ANF(Affinity Network Fusion) provides methods for affinity matrix construction and fusion as well as spectral clustering. This package is used for complex patient clustering by integrating multi-omic data through affinity network fusion.

This package provides tools to import transcript-level abundance, estimated counts and transcript lengths, and to summarize them into matrices for use with downstream gene-level analysis packages. Average transcript length, weighted by sample-specific transcript abundance estimates, is provided as a matrix which can be used as an offset for different expression of gene-level counts.

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.

This package loads a TxDb object, which is an R interface to prefabricated databases contained in this package. This package provides the TxDb object of Mouse data as provided by UCSC (mm10, December 2011) based on the knownGene track.

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.