The AffyRNADegradation package helps with the assessment and correction of RNA degradation effects in Affymetrix 3 expression arrays. The parameter d gives a robust and accurate measure of RNA integrity. The correction removes the probe positional bias, and thus improves comparability of samples that are affected by RNA degradation.

This package provides basic plotting, data manipulation and processing of mass spectrometry based proteomics data.

This package provides an interactive tool for visualizing Illumina methylation array data. Both the 450k and EPIC array are supported.

This package is an R implementation for fully unsupervised deconvolution of complex tissues. DebCAM provides basic functions to perform unsupervised deconvolution on mixture expression profiles by CAM and some auxiliary functions to help understand the subpopulation- specific results. It also implements functions to perform supervised deconvolution based on prior knowledge of molecular markers, S matrix or A matrix. Combining molecular markers from CAM and from prior knowledge can achieve semi-supervised deconvolution of mixtures.

This package generates microarray quality metrics reports for data in Bioconductor microarray data containers (ExpressionSet, NChannelSet, AffyBatch). One and two color array platforms are supported.

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 provides an R interface to the HISAT2 spliced short-read aligner by Kim et al. (2015). The package contains wrapper functions to create a genome index and to perform the read alignment to the generated index.

This package provides a set of protein ID mappings for PFAM, assembled using data from public repositories.

This package provides a package to perform differential network analysis, differential node analysis (differential coexpression analysis), network and metabolic pathways view.

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.

The tRNA package allows tRNA sequences and structures to be accessed and used for subsetting. In addition, it provides visualization tools to compare feature parameters of multiple tRNA sets and correlate them to additional data. The tRNA package uses GRanges objects as inputs requiring only few additional column data sets.

Graphite provides networks derived from eight public pathway databases, and automates the conversion of node identifiers (e.g. from Entrez IDs to gene symbols).

Independent Surrogate Variable Analysis is an algorithm for feature selection in the presence of potential confounding factors (see Teschendorff AE et al 2011, <doi: 10.1093/bioinformatics/btr171>).

MetagenomeSeq is designed to determine features (be it OTU, species, etc.) that are differentially abundant between two or more groups of multiple samples. This package is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations.

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.

CopywriteR extracts DNA copy number information from targeted sequencing by utilizing off-target reads. It allows for extracting uniformly distributed copy number information, can be used without reference, and can be applied to sequencing data obtained from various techniques including chromatin immunoprecipitation and target enrichment on small gene panels. Thereby, CopywriteR constitutes a widely applicable alternative to available copy number detection tools.

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

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

This package provides an R interface to Megadepth. It is particularly useful for computing the coverage of a set of genomic regions across bigWig or BAM files. With this package, you can build base-pair coverage matrices for regions or annotations of your choice from BigWig files.

This package awst (Asymmetric Within-Sample Transformation) that regularizes RNA-seq read counts and reduces the effect of noise on the classification of samples. AWST comprises two main steps: standardization and smoothing. These steps transform gene expression data to reduce the noise of the lowly expressed features, which suffer from background effects and low signal-to-noise ratio, and the influence of the highly expressed features, which may be the result of amplification bias and other experimental artifacts.

This package provides a collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control.

Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner.

This R package enables the user to read pfam predictions into R. Most human protein domains exist as multiple distinct variants termed domain isotypes. This R package enables the identification and classification of such domain isotypes from pfam data.

This package provides full genome sequences for Homo sapiens from 1000genomes phase2 reference genome sequence (hs37d5), based on NCBI GRCh37.