This package provides basic utility functions for performing single-cell analyses, focusing on simple normalization, quality control and data transformations. It also provides some helper functions to assist development of other packages.

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

ggtreeExtra extends the method for mapping and visualizing associated data on phylogenetic tree using ggtree. These associated data can be presented on the external panels to circular layout, fan layout, or other rectangular layout tree built by ggtree with the grammar of ggplot2.

This package contains class definitions, validity checks, and initialization methods for classes used by the oligo and crlmm packages.

This package provides tools to accurately estimate cell type abundances from heterogeneous bulk expression. A reference-based method utilizes single-cell information to generate a signature matrix and transformation of bulk expression for accurate regression based estimates. A marker-based method utilizes known cell-specific marker genes to measure relative abundances across samples.

Many modern biological datasets consist of small counts that are not well fit by standard linear-Gaussian methods such as principal component analysis. This package provides implementations of count-based feature selection and dimension reduction algorithms. These methods can be used to facilitate unsupervised analysis of any high-dimensional data such as single-cell RNA-seq.

This is an annotation package for Illumina's EPIC methylation arrays.

This package implements transcript quantification import from Salmon and alevin with automatic attachment of transcript ranges and release information, and other associated metadata. De novo transcriptomes can be linked to the appropriate sources with linkedTxomes and shared for computational reproducibility.

This package exposes an annotation database generated from Ensembl.

This is a package for de novo identification and extraction of differentially methylated regions (DMRs) from the human genome using Whole Genome Bisulfite Sequencing (WGBS) and Illumina Infinium Array (450K and EPIC) data. It provides functionality for filtering probes possibly confounded by SNPs and cross-hybridisation. It includes GRanges generation and plotting functions.

DeconSeq is an R package for deconvolution of heterogeneous tissues based on mRNA-Seq data. It models the expression levels from heterogeneous cell populations in mRNA-Seq as the weighted average of expression from different constituting cell types and predicted cell type proportions of single expression profiles.

This package implements low-level utilities for single-cell trajectory analysis, primarily intended for re-use inside higher-level packages. It includes a function to create a cluster-level minimum spanning tree and data structures to hold pseudotime inference results.

This package can be used for the analysis of gene expression studies, especially the use of linear models for analysing designed experiments and the assessment of differential expression. The analysis methods apply to different technologies, including microarrays, RNA-seq, and quantitative PCR.

Exploratory data analysis to assess the quality of a set of LC-MS/MS experiments, and visualize de influence of the involved factors.

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 provides functionality for running and comparing many different clusterings of single-cell sequencing data or other large mRNA expression data sets.

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.

This package implements the GENIE3 algorithm for inferring gene regulatory networks from expression data.

This package provides an alternative interface to Bioconductor annotation resources, in particular the gene identifier mapping functionality of the org packages (e.g., org.Hs.eg.db) and the genome coordinate functionality of the TxDb packages (e.g., TxDb.Hsapiens.UCSC.hg38.knownGene).

This package provides basic features for the automated analysis of Affymetrix arrays.

The package provides functions to create and use transcript-centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, the ensembldb package also provides a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes.

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 package provides tools for the identification of differentially expressed genes and estimation of the False Discovery Rate (FDR) using both the Significance Analysis of Microarrays (SAM) and the Empirical Bayes Analyses of Microarrays (EBAM).

This package AMARETTO represents an algorithm that integrates copy number, DNA methylation and gene expression data to identify a set of driver genes by analyzing cancer samples and connects them to clusters of co-expressed genes, which we define as modules. AMARETTO can be applied in a pancancer setting to identify cancer driver genes and their modules on multiple cancer sites. AMARETTO captures modules enriched in angiogenesis, cell cycle and EMT, and modules that accurately predict survival and molecular subtypes. This allows AMARETTO to identify novel cancer driver genes directing canonical cancer pathways.