This package implements a method to rapidly assess cell type identity using both functional and random gene sets and it allows users to quantify cell type replicability across datasets using neighbor voting. MetaNeighbor works on the basis that cells of the same type should have more similar gene expression profiles than cells of different types.

This is a package for parsing Affymetrix files (CDF, CEL, CHP, BPMAP, BAR). It provides methods for fast and memory efficient parsing of Affymetrix files using the Affymetrix' Fusion SDK. Both ASCII- and binary-based files are supported. Currently, there are methods for reading chip definition file (CDF) and a cell intensity file (CEL). These files can be read either in full or in part. For example, probe signals from a few probesets can be extracted very quickly from a set of CEL files into a convenient list structure.

Bayesian network analysis is a form of probabilistic graphical models which derives from empirical data a directed acyclic graph, DAG, describing the dependency structure between random variables. An additive Bayesian network model consists of a form of a DAG where each node comprises a generalized linear model (GLM). Additive Bayesian network models are equivalent to Bayesian multivariate regression using graphical modelling, they generalises the usual multivariable regression, GLM, to multiple dependent variables. This package provides routines to help determine optimal Bayesian network models for a given data set, where these models are used to identify statistical dependencies in messy, complex data.

This package provides an R wrapper around the popular bowtie short read aligner and around SpliceMap, a de novo splice junction discovery and alignment tool.

This package provides data needed to use the ITALICS package.

This package is focused on finding differential exon usage using RNA-seq exon counts between samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the negative binomial distribution to estimate the variance between biological replicates and generalized linear models for testing. The package also provides functions for the visualization and exploration of the results.

This package provides functions to estimate variance-mean dependence in count data from high-throughput nucleotide sequencing assays and test for differential expression based on a model using the negative binomial distribution.

This package contains classes used in model-view-controller (MVC) design.

This package provides delayed computation of a matrix of scaled and centered values. The result is equivalent to using the scale function but avoids explicit realization of a dense matrix during block processing. This permits greater efficiency in common operations, most notably matrix multiplication.

The package provides utility functions related to package development. These include functions that replace slots, and selectors for show methods. It aims to coalesce the various helper functions often re-used throughout the Bioconductor ecosystem.

This package provides tools for Bayesian integrated analysis of Affymetrix GeneChips.

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.

Enriched heatmap is a special type of heatmap which visualizes the enrichment of genomic signals on specific target regions. This type of heatmap is just a normal heatmap but with some special settings, with the functionality of ComplexHeatmap, it would be much easier to customize the heatmap as well as concatenating to a list of heatmaps to show correspondence between different data sources.

This package provides utilities for flow cytometry data.

INSPEcT (INference of Synthesis, Processing and dEgradation rates in Time-Course experiments) analyses 4sU-seq and RNA-seq time-course data in order to evaluate synthesis, processing and degradation rates and assess via modeling the rates that determines changes in mature mRNA levels.

This package stores all schemas required by various alabaster.* packages. No computation should be performed by this package, as that is handled by alabaster.base.

bioassayR is a computational tool that enables simultaneous analysis of thousands of bioassay experiments performed over a diverse set of compounds and biological targets. Unique features include support for large-scale cross-target analyses of both public and custom bioassays, generation of high throughput screening fingerprints (HTSFPs), and an optional preloaded database that provides access to a substantial portion of publicly available bioactivity data.

This package efficiently obtains count vectors from indexed bam files. It counts the number of nucleotide sequence reads in given genomic ranges and it computes reads profiles and coverage profiles. It also handles paired-end data.

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.

Managing data from large scale projects such as The Cancer Genome Atlas (TCGA) for further analysis is an important and time consuming step for research projects. Several efforts, such as Firehose project, make TCGA pre-processed data publicly available via web services and data portals but it requires managing, downloading and preparing the data for following steps. This package provides an extensible R based data client for Firehose pre-processed data.

This package reads Bruker NMR data directories both zipped and unzipped. It provides automated and efficient signal processing for untargeted NMR metabolomics. It is able to interpolate the samples, detect outliers, exclude regions, normalize, detect peaks, align the spectra, integrate peaks, manage metadata and visualize the spectra. After spectra processing, it can apply multivariate analysis on extracted data. Efficient plotting with 1-D data is also available. Basic reading of 1D ACD/Labs exported JDX samples is also available.

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 provides mappings from Entrez gene identifiers to various annotations for the genome of the model mouse Mus musculus.

This package provides datasets needed for ChAMP including a test dataset and blood controls for CNA analysis.