The ReportingTools package enables users to easily display reports of analysis results generated from sources such as microarray and sequencing data. The package allows users to create HTML pages that may be viewed on a web browser, or in other formats. Users can generate tables with sortable and filterable columns, make and display plots, and link table entries to other data sources such as NCBI or larger plots within the HTML page. Using the package, users can also produce a table of contents page to link various reports together for a particular project that can be viewed in a web browser.

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 raw data objects for the Illumina 450k DNA methylation microarrays, and an object depicting which CpGs on the array are associated with cell type.

This package provides tools to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose containers for storing and manipulating genomic intervals and variables defined along a genome.

This package provides a framework for processing and visualization of chromatographically separated and single-spectra mass spectral data. It imports from AIA/ANDI NetCDF, mzXML, mzData and mzML files. It preprocesses data for high-throughput, untargeted analyte profiling.

This package implements a new RNA-Seq analysis method and integrates two modules: a basic model for pairwise comparison and a linear model for complex design. RNA-Seq quantifies gene expression with reads count, which usually consists of conditions (or treatments) and several replicates for each condition. This software infers differential expression directly by the counts difference between conditions. It assumes that the sum counts difference between conditions follow a negative binomial distribution. In addition, ABSSeq moderates the fold-changes by two steps: the expression level and gene-specific dispersion, that might facilitate the gene ranking by fold-change and visualization.

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.

The atSNP package performs affinity tests of motif matches with the SNP (single nucleotide polymorphism) or the reference genomes and SNP-led changes in motif matches.

This package provides a client for the OmniPath web service and many other resources. It also includes functions to transform and pretty print some of the downloaded data, functions to access a number of other resources. Furthermore, OmnipathR features a close integration with the NicheNet method for ligand activity prediction from transcriptomics data.

The MassSpecWavelet package aims to process Mass Spectrometry (MS) data mainly through the use of wavelet transforms. It supports peak detection based on Continuous Wavelet Transform (CWT).

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 provides tools For analyzing Illumina Infinium DNA methylation arrays. SeSAMe provides utilities to support analyses of multiple generations of Infinium DNA methylation BeadChips, including preprocessing, quality control, visualization and inference. SeSAMe features accurate detection calling, intelligent inference of ethnicity, sex and advanced quality control routines.

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

The rpx package implements an interface to proteomics data submitted to the ProteomeXchange consortium.

This package provides a package to analyze oligonucleotide arrays (expression/SNP/tiling/exon) at probe-level. It currently supports Affymetrix (CEL files) and NimbleGen arrays (XYS files).

The ASAFE package contains a collection of functions that can be used to carry out an EM (Expectation–maximization) algorithm to estimate ancestry-specific allele frequencies for a bi-allelic genetic marker, e.g. an SNP (single nucleotide polymorphism) from genotypes and ancestry pairs.

r-kegggraph is an interface between Kegg Pathway database and graph object as well as a collection of tools to analyze, dissect and visualize these graphs. It parses the regularly updated kgml (Kegg XML) files into graph models maintaining all essential pathway attributes. The package offers functionalities including parsing, graph operation, visualization and etc.

This package provides tools to create and plot diffusion maps.

This package provides high level functions for reading Affy .CEL files, phenotypic data, and then computing simple things with it, such as t-tests, fold changes and the like. It makes heavy use of the affy library. It also has some basic scatter plot functions and mechanisms for generating high resolution journal figures.

The package ABarray is designed to work with Applied Biosystems whole genome microarray platform, as well as any other platform whose data can be transformed into expression data matrix. Functions include data preprocessing, filtering, control probe analysis, statistical analysis in one single function. A graphical user interface (GUI) is also provided. The raw data, processed data, graphics output and statistical results are organized into folders according to the analysis settings used.

This package provides tools to visualize read coverage from sequencing experiments together with genomic annotations (genes, transcripts, peaks). Introns of long transcripts can be rescaled to a fixed length for better visualization of exonic read coverage.

This package aggregateBioVar contains tools to summarize single cell gene expression profiles at the level of subject for single cell RNA-seq data collected from more than one subject (e.g. biological sample or technical replicates). A SingleCellExperiment object is taken as input and converted to a list of SummarizedExperiment objects, where each list element corresponds to an assigned cell type. The SummarizedExperiment objects contain aggregate gene-by-subject count matrices and inter-subject column metadata for individual subjects that can be processed using downstream bulk RNA-seq tools.

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

The purpose of this package is to identify traits in a dataset that can separate groups. This is done on two levels. First, clustering is performed, using an implementation of sparse K-means. Secondly, the generated clusters are used to predict outcomes of groups of individuals based on their distribution of observations in the different clusters. As certain clusters with separating information will be identified, and these clusters are defined by a sparse number of variables, this method can reduce the complexity of data, to only emphasize the data that actually matters.