This package implements a variety of low-level analyses of single-cell RNA-seq data. Methods are provided for normalization of cell-specific biases, assignment of cell cycle phase, and detection of highly variable and significantly correlated genes.

This package provides negative binomial models for two-group comparisons and regression inferences from RNA-sequencing data.

This is a package for noise-robust soft clustering of gene expression time-series data (including a graphical user interface).

This package provides generic functions for interacting with the AnVIL system. Packages that use either GCP or Azure in AnVIL are built on top of AnVILBase. Extension packages will provide methods for interacting with other cloud providers.

This package provides data needed to use the ITALICS package.

This package provides raw beta values from 36 samples across 3 groups from Illumina 450k methylation arrays.

This package provides the output of running various transcript abundance quantifiers on a set of 6 RNA-seq samples from the GEUVADIS project. The quantifiers were Cufflinks, RSEM, kallisto, Salmon and Sailfish. Alevin example output is also included.

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.

Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. decoupleR is a Bioconductor package containing different statistical methods to extract these signatures within a unified framework. decoupleR allows the user to flexibly test any method with any resource. It incorporates methods that take into account the sign and weight of network interactions. decoupleR can be used with any omic, as long as its features can be linked to a biological process based on prior knowledge. For example, in transcriptomics gene sets regulated by a transcription factor, or in phospho-proteomics phosphosites that are targeted by a kinase.

This package provides full genome sequences for Drosophila melanogaster (Fly) as provided by UCSC (dm3, April 2006) and stored in Biostrings objects.

The package AlphaBeta is a computational method for estimating epimutation rates and spectra from high-throughput DNA methylation data in plants. The method has been specifically designed to:

• analyze germline epimutations in the context of multi-generational mutation accumulation lines;

• analyze somatic epimutations in the context of plant development and aging.

This package provides a package for summary and annotation of genomic intervals. Users can visualize and quantify genomic intervals over pre-defined functional regions, such as promoters, exons, introns, etc. The genomic intervals represent regions with a defined chromosome position, which may be associated with a score, such as aligned reads from HT-seq experiments, TF binding sites, methylation scores, etc. The package can use any tabular genomic feature data as long as it has minimal information on the locations of genomic intervals. In addition, it can use BAM or BigWig files as input.

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

Principal Component Analysis (PCA) extracts the fundamental structure of the data without the need to build any model to represent it. This "summary" of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated (i.e. the 'principal components'), while at the same time being capable of easy interpretation on the original data. PCAtools provides functions for data exploration via PCA, and allows the user to generate publication-ready figures. PCA is performed via BiocSingular; users can also identify an optimal number of principal components via different metrics, such as the elbow method and Horn's parallel analysis, which has relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high dimensional mass cytometry data.

This is a comprehensive package to automatically train and validate a multi-class SVM classifier based on gene expression data. It provides transparent selection of gene markers, their coexpression networks, and an interface to query the classifier.

This package exposes an annotation database generated from Ensembl.

ATAC-seq, an assay for Transposase-Accessible Chromatin using sequencing, is a rapid and sensitive method for chromatin accessibility analysis. It was developed as an alternative method to MNase-seq, FAIRE-seq and DNAse-seq. The ATACseqQC package was developed to help users to quickly assess whether their ATAC-seq experiment is successful. It includes diagnostic plots of fragment size distribution, proportion of mitochondria reads, nucleosome positioning pattern, and CTCF or other Transcript Factor footprints.

This package performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently.

This package provides mass-spectrometry based spatial proteomics data sets and protein complex separation data. It also contains the time course expression experiment from Mulvey et al. (2015).

RCAS aims to be a standalone RNA-centric annotation system that provides intuitive reports and publication-ready graphics. This package provides the R library implementing most of the pipeline's features.

The lumi package provides an integrated solution for the Illumina microarray data analysis. It includes functions of Illumina BeadStudio (GenomeStudio) data input, quality control, BeadArray-specific variance stabilization, normalization and gene annotation at the probe level. It also includes the functions of processing Illumina methylation microarrays, especially Illumina Infinium methylation microarrays.

This package provides fully Bayesian mixture models for differential gene expression.

This package provides genome wide annotations for Bovine, primarily based on mapping using Entrez Gene identifiers.

This package contains default datasets used by the Bioconductor package biscuiteer.