This package provides a port of the matrixStats API for use with DelayedMatrix objects from the DelayedArray package. It contains high-performing functions operating on rows and columns of DelayedMatrix objects, e.g. colMedians, rowMedians, colRanks, rowRanks, colSds, and rowSds. Functions are optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized.

This package provides publicly available data from The Cancer Genome Atlas (TCGA) as MultiAssayExperiment objects. MultiAssayExperiment integrates multiple assays (e.g., RNA-seq, copy number, mutation, microRNA, protein, and others) with clinical / pathological data. It also links assay barcodes with patient identifiers, enabling harmonized subsetting of rows (features) and columns (patients / samples) across the entire multi-'omics experiment.

This package provides an annotation database of Mouse genome data. It is derived from the UCSC mm9 genome and based on the "knownGene" track. The database is exposed as a TxDb object.

This package provides the HTSlib C library for high-throughput nucleotide sequence analysis. The package is primarily useful to developers of other R packages who wish to make use of HTSlib.

This package provides ChIP-seq data for demonstration purposes in the chromstaR package.

This package provides Ion Trap positive ionization mode data in mzML file format. It includes a subset from 500-850 m/z and 1190-1310 seconds, including MS2 and MS3, intensity threshold 100.000; extracts from FTICR Apex III, m/z 400-450; a subset of UPLC - Bruker micrOTOFq data, both mzML and mz5; LC-MSMS and MRM files from proteomics experiments; and PSI mzIdentML example files for various search engines.

satuRn provides a framework for performing differential transcript usage analyses. The package consists of three main functions. The first function, fitDTU, fits quasi-binomial generalized linear models that model transcript usage in different groups of interest. The second function, testDTU, tests for differential usage of transcripts between groups of interest. Finally, plotDTU visualizes the usage profiles of transcripts in groups of interest.

Genome wide studies of translational control is emerging as a tool to study various biological conditions. The output from such analysis is both the mRNA level (e.g. cytosolic mRNA level) and the level of mRNA actively involved in translation (the actively translating mRNA level) for each mRNA. The standard analysis of such data strives towards identifying differential translational between two or more sample classes - i.e., differences in actively translated mRNA levels that are independent of underlying differences in cytosolic mRNA levels. This package allows for such analysis using partial variances and the random variance model. As 10s of thousands of mRNAs are analyzed in parallel the library performs a number of tests to assure that the data set is suitable for such analysis.

This package contains functions for building GenomicState objects from different annotation sources such as Gencode. It also provides access to these files at JHPCE.

Animalcules is an R package for utilizing up-to-date data analytics, visualization methods, and machine learning models to provide users an easy-to-use interactive microbiome analysis framework. It can be used as a standalone software package or users can explore their data with the accompanying interactive R Shiny application. Traditional microbiome analysis such as alpha/beta diversity and differential abundance analysis are enhanced, while new methods like biomarker identification are introduced by animalcules. Powerful interactive and dynamic figures generated by animalcules enable users to understand their data better and discover new insights.

This package implements the mini-batch k-means algorithm for large datasets, including support for on-disk data representation.

Monocle 3 performs clustering, differential expression and trajectory 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 3 also performs differential expression analysis, clustering, visualization, and other useful tasks on single-cell expression data. It is designed to work with RNA-Seq data, but could be used with other types as well.

This package discovers differential features in hetero- and homogeneous omic data by a two-step method including subsampling LIMMA and NSCA. DECO reveals feature associations to hidden subclasses not exclusively related to higher deregulation levels.

This package muscat provides various methods and visualization tools for DS(differential splicing) analysis in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, including cell-level mixed models and methods based on aggregated "pseudobulk" data, as well as a flexible simulation platform that mimics both single and multi-sample scRNA-seq data.

This is an R package to make it easier to import and store phylogenetic trees with associated data; and to link external data from different sources to phylogeny. It also supports exporting phylogenetic trees with heterogeneous associated data to a single tree file and can be served as a platform for merging tree with associated data and converting file formats.

This package contains useful helper functions for dealing with structural variants in VCF format. The packages contains functions for parsing VCFs from a number of popular callers as well as functions for dealing with breakpoints involving two separate genomic loci encoded as GRanges objects.

This package provides user interface and database connection code for annotation data packages using SQLite data storage.

This package provides an array-like container for convenient access and manipulation of HDF5 datasets. It supports delayed operations and block processing.

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 data from 6 samples across 2 groups from 450k methylation arrays.

Skeletal myoblasts undergo a well-characterized sequence of morphological and transcriptional changes during differentiation. In this experiment, primary human skeletal muscle myoblasts (HSMM) were expanded under high mitogen conditions (GM) and then differentiated by switching to low-mitogen media (DM). RNA-Seq libraries were sequenced from each of several hundred cells taken over a time-course of serum-induced differentiation. Between 49 and 77 cells were captured at each of four time points (0, 24, 48, 72 hours) following serum switch using the Fluidigm C1 microfluidic system. RNA from each cell was isolated and used to construct mRNA-Seq libraries, which were then sequenced to a depth of ~4 million reads per library, resulting in a complete gene expression profile for each cell.

This package provides a tool for non linear mapping (non linear regression) using a mixture of regression model and an inverse regression strategy. The methods include the GLLiM model (see Deleforge et al (2015) <DOI:10.1007/s11222-014-9461-5>) based on Gaussian mixtures and a robust version of GLLiM, named SLLiM (see Perthame et al (2016) <DOI:10.1016/j.jmva.2017.09.009>) based on a mixture of Generalized Student distributions. The methods also include BLLiM (see Devijver et al (2017) <arXiv:1701.07899>) which is an extension of GLLiM with a sparse block diagonal structure for large covariance matrices (particularly interesting for transcriptomic data).

The MsFeature package defines functionality for Mass Spectrometry features. This includes functions to group (LC-MS) features based on some of their properties, such as retention time (coeluting features), or correlation of signals across samples. This package hence can be used to group features, and its results can be used as an input for the QFeatures package which allows aggregating abundance levels of features within each group. This package defines concepts and functions for base and common data types, implementations for more specific data types are expected to be implemented in the respective packages (such as e.g. xcms).

This package aims to make NMR spectroscopy data analysis as easy as possible. It only requires a small set of functions to perform an entire analysis. Speaq offers the possibility of raw spectra alignment and quantitation but also an analysis based on features whereby the spectra are converted to peaks which are then grouped and turned into features. These features can be processed with any number of statistical tools either included in speaq or available elsewhere on CRAN.