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

This package contains the functions to find the gene expression modules that represent the drivers of Kauffman's attractor landscape. The modules are the core attractor pathways that discriminate between different cell types of groups of interest. Each pathway has a set of synexpression groups, which show transcriptionally-coordinated changes in gene expression.

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

This package defines low-level functions for mass spectrometry data and is independent of any high-level data structures. These functions include mass spectra processing functions (noise estimation, smoothing, binning), quantitative aggregation functions (median polish, robust summarisation, etc.), missing data imputation, data normalisation (quantiles, vsn, etc.) as well as misc helper functions, that are used across high-level data structure within the R for Mass Spectrometry packages.

AS (alternative splicing) is a common mechanism of post-transcriptional gene regulation in eukaryotic organisms that expands the functional and regulatory diversity of a single gene by generating multiple mRNA isoforms that encode structurally and functionally distinct proteins. ASpli is an integrative pipeline and user-friendly R package that facilitates the analysis of changes in both annotated and novel AS events. ASpli integrates several independent signals in order to deal with the complexity that might arise in splicing patterns.

This package provides a multivariate inferential analysis method for detecting differentially expressed genes in gene expression data. It uses artificial components, close to the data's principal components but with an exact interpretation in terms of differential genetic expression, to identify differentially expressed genes while controlling the false discovery rate (FDR).

This is a package for saving matrices, arrays and similar objects into file artifacts, and loading them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

This is a package for identification of metabolites using high precision mass spectrometry. MS peaks are used to derive a ranked list of sum formulae, alternatively for a given sum formula the theoretical isotope distribution can be calculated to search in MS peak lists.

This package implements the density-preserving modification to t-SNE and UMAP described by Narayan et al. (2020) <doi:10.1101/2020.05.12.077776>. den-SNE and densMAP aim to enable more accurate visual interpretation of high-dimensional datasets by producing lower-dimensional embeddings that accurately represent the heterogeneity of the original high-dimensional space, enabling the identification of homogeneous and heterogeneous cell states. This accuracy is accomplished by including in the optimisation process a term which considers the local density of points in the original high-dimensional space. This can help to create visualisations that are more representative of heterogeneity in the original high-dimensional space.

R-escape streamlines gene set enrichment analysis for single-cell RNA sequencing. Using raw count information, Seurat objects, or SingleCellExperiment format, users can perform and visualize GSEA across individual cells.

This package provides an implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and ResultSet. MultiDataSet is designed for integrating multi omics data sets and ResultSet is a container for omics results. This package contains base classes for MEAL and rexposome packages.

BioNERO aims to integrate all aspects of biological network inference in a single package, including data preprocessing, exploratory analyses, network inference, and analyses for biological interpretations. BioNERO can be used to infer gene coexpression networks (GCNs) and gene regulatory networks (GRNs) from gene expression data. Additionally, it can be used to explore topological properties of protein-protein interaction (PPI) networks. GCN inference relies on the popular WGCNA algorithm. GRN inference is based on the "wisdom of the crowds" principle, which consists in inferring GRNs with multiple algorithms (here, CLR, GENIE3 and ARACNE) and calculating the average rank for each interaction pair. As all steps of network analyses are included in this package, BioNERO makes users avoid having to learn the syntaxes of several packages and how to communicate between them. Finally, users can also identify consensus modules across independent expression sets and calculate intra and interspecies module preservation statistics between different networks.

This package provides functionalities for downstream analysis, annotation and visualizaton of alternative splicing events generated by rMATS.

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

This package is importing data from Illumina SNP experiments and it performs copy number calculations and reports.

The package provides two frameworks. One for the differential transcript usage analysis between different conditions and one for the tuQTL analysis. Both are based on modeling the counts of genomic features (i.e., transcripts) with the Dirichlet-multinomial distribution. The package also makes available functions for visualization and exploration of the data and results.

This package implements a variety of methods for batch correction of single-cell (RNA sequencing) data. This includes methods based on detecting mutually nearest neighbors, as well as several efficient variants of linear regression of the log-expression values. Functions are also provided to perform global rescaling to remove differences in depth between batches, and to perform a principal components analysis that is robust to differences in the numbers of cells across batches.

This package works analogous to BiocManager but for Docker images. Use the BiocDockerManager package to install and manage Docker images provided by the Bioconductor project.

This package includes positive ionization mode data in NetCDF file format. Centroided subset from 200-600 m/z and 2500-4500 seconds. Data originally reported in "Assignment of Endogenous Substrates to Enzymes by Global Metabolite Profiling" Biochemistry; 2004; 43(45). It also includes detected peaks in an xcmsSet.

This is a package for saving SummarizedExperiments into file artifacts, and loading them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

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.

This package provides a new clustering algorithm, binary cut, for clustering similarity matrices of functional terms is implemented in this package. It also provides functionalities for visualizing, summarizing and comparing the clusterings.

BASiCS is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori. BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells.

This package contains a SummarizedExperiment from the Yu et al. (2013) paper that performed the rat BodyMap across 11 organs and 4 developmental stages. Raw FASTQ files were downloaded and mapped using STAR. Data is available on ExperimentHub as a data package.