This package contains whole-genome single cell sequencing data for demonstration purposes in the AneuFinder package.

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.

This package provides a set of protein ID mappings for PFAM, assembled using data from public repositories.

This package provides statistical tests for label-free LC-MS/MS data by spectral counts, to discover differentially expressed proteins between two biological conditions. Three tests are available: Poisson GLM regression, quasi-likelihood GLM regression, and the negative binomial of the edgeR package. The three models admit blocking factors to control for nuisance variables. To assure a good level of reproducibility a post-test filter is available, where we may set the minimum effect size considered biologicaly relevant, and the minimum expression of the most abundant condition.

This package provides Affymetrix HG-U133_Plus_2 array annotation data (chip hgu133plus2) assembled using data from public repositories.

This package provides functions for inferring continuous, branching lineage structures in low-dimensional data. Slingshot was designed to model developmental trajectories in single-cell RNA sequencing data and serve as a component in an analysis pipeline after dimensionality reduction and clustering. It is flexible enough to handle arbitrarily many branching events and allows for the incorporation of prior knowledge through supervised graph construction.

This package implements the gene expression anti-profiles method. Anti-profiles are a new approach for developing cancer genomic signatures that specifically take advantage of gene expression heterogeneity. They explicitly model increased gene expression variability in cancer to define robust and reproducible gene expression signatures capable of accurately distinguishing tumor samples from healthy controls.

This package provides microarray data (from the Illumina Ref-8 BeadChips platform) and phenotype-level data from an epidemiological investigation of benzene exposure, packaged using SummarizedExperiemnt, for use as an example with the biotmle R package.

Milo performs single-cell differential abundance testing. Cell states are modelled as representative neighbourhoods on a nearest neighbour graph. Hypothesis testing is performed using a negative bionomial generalized linear model.

This package provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files, which are portable across platforms with hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. The gdsfmt package offers efficient operations specifically designed for integers of less than 8 bits, since a diploid genotype, like single-nucleotide polymorphism (SNP), usually occupies fewer bits than a byte. Data compression and decompression are available with relatively efficient random access. It is also allowed to read a GDS file in parallel with multiple R processes supported by the package parallel.

This package provides methods for working with Illumina arrays using the gdsfmt package.

This is a package for semi-supervised isoform detection and annotation from both bulk and single-cell long read RNA-seq data. Flames provides automated pipelines for analysing isoforms, as well as intermediate functions for manual execution.

BiocSet displays different biological sets in a triple tibble format. These three tibbles are element, set, and elementset. The user has the ability to activate one of these three tibbles to perform common functions from the dplyr package. Mapping functionality and accessing web references for elements/sets are also available in BiocSet.

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.

The QFeatures infrastructure enables the management and processing of quantitative features for high-throughput mass spectrometry assays. It provides a familiar Bioconductor user experience to manages quantitative data across different assay levels (such as peptide spectrum matches, peptides and proteins) in a coherent and tractable format.

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 package provides tools for processing short read data from ChIPseq experiments.

This package allows importing most common specific structure (motif) types into R for use by functions provided by other Bioconductor motif-related packages. Motifs can be exported into most major motif formats from various classes as defined by other Bioconductor packages. A suite of motif and sequence manipulation and analysis functions are included, including enrichment, comparison, P-value calculation, shuffling, trimming, higher-order motifs, and others.

This package provides plotting functions, frameshift detection and parsing of genetic sequencing data from ribosome profiling experiments.

This package provides a data package containing summarized Illumina 450k array data on 2800 samples and summarized EPIC data for 2620 samples. The data can be use as a background data set in the interactive application.

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 package provides a framework for processing and visualization of chromatographically separated and single-spectra mass spectral data. It imports from AIA/ANDI NetCDF, mzXML, mzData and mzML files. It preprocesses data for high-throughput, untargeted analyte profiling.

This package defines classes for "class discovery" in the OOMPA project. Class discovery primarily consists of unsupervised clustering methods with attempts to assess their statistical significance.

This package provides a collection of functions designed for analyzing deconvolution of the bulk sample(s) using an atlas of reference omic signature profiles and a user-selected model. Users are given the option to create or extend a reference atlas and,also simulate the desired size of the bulk signature profile of the reference cell types. The package includes the cell-type-specific methylation atlas and, Illumina Epic B5 probe ids that can be used in deconvolution. Additionally, we included BSmeth2Probe, to make mapping WGBS data to their probe IDs easier.