SCONE is an R package for comparing and ranking the performance of different normalization schemes for single-cell RNA-seq and other high-throughput analyses.

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

This package provides functions for handling translating between different identifieres using the Biocore Data Team data-packages (e.g. org.Bt.eg.db).

HDCytoData contains a set of high-dimensional cytometry benchmark datasets. These datasets are formatted into SummarizedExperiment and flowSet Bioconductor object formats, including all required metadata. Row metadata includes sample IDs, group IDs, patient IDs, reference cell population or cluster labels and labels identifying spiked in cells. Column metadata includes channel names, protein marker names, and protein marker classes.

The atSNP package performs affinity tests of motif matches with the SNP (single nucleotide polymorphism) or the reference genomes and SNP-led changes in motif matches.

Explore, diagnose, and compare variant calls using filters. The VariantTools package supports a workflow for loading data, calling single sample variants and tumor-specific somatic mutations or other sample-specific variant types (e.g., RNA editing). Most of the functions operate on alignments (BAM files) or datasets of called variants. The user is expected to have already aligned the reads with a separate tool, e.g., GSNAP via gmapR.

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 supporting annotation and test data for SeSAMe package. This includes chip tango addresses, mapping information, performance annotation, and trained predictor for Infinium array data. This package provides user access to essential annotation data for working with many generations of the Infinium DNA methylation array. It currently supports human array (HM27, HM450, EPIC), mouse array (MM285) and the HorvathMethylChip40 (Mammal40) array.

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 awst (Asymmetric Within-Sample Transformation) that regularizes RNA-seq read counts and reduces the effect of noise on the classification of samples. AWST comprises two main steps: standardization and smoothing. These steps transform gene expression data to reduce the noise of the lowly expressed features, which suffer from background effects and low signal-to-noise ratio, and the influence of the highly expressed features, which may be the result of amplification bias and other experimental artifacts.

This package provides an array-like container for convenient access and manipulation of HDF5 datasets. It supports delayed operations and block processing.

This package provides an R interface to Megadepth. It is particularly useful for computing the coverage of a set of genomic regions across bigWig or BAM files. With this package, you can build base-pair coverage matrices for regions or annotations of your choice from BigWig files.

This package contains data for mapping between NCBI taxonomy ID and species. It is used by functions in the GenomeInfoDb package.

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

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 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 provides a framework for the quantification and analysis of short genomic reads. It covers a complete workflow starting from raw sequence reads, over creation of alignments and quality control plots, to the quantification of genomic regions of interest.

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

This package provides an implementation of an algorithm for determining cluster count and membership by stability evidence in unsupervised analysis.

Expedite large RNA-Seq analyses using a combination of previously developed tools. YARN is meant to make it easier for the user in performing basic mis-annotation quality control, filtering, and condition-aware normalization. YARN leverages many Bioconductor tools and statistical techniques to account for the large heterogeneity and sparsity found in very large RNA-seq experiments.

This package provides Affymetrix HG_U95A Array annotation data (chip hgu95a) assembled using data from public repositories.

R-dsb improves protein expression analysis in droplet-based single-cell studies. The package specifically addresses noise in raw protein UMI counts from methods like CITE-seq. It identifies and removes two main sources of noise—protein-specific noise from unbound antibodies and droplet/cell-specific noise. The package is applicable to various methods, including CITE-seq, REAP-seq, ASAP-seq, TEA-seq, and Mission Bioplatform data. Check the vignette for tutorials on integrating dsb with Seurat and Bioconductor, and using dsb in Python.

Volcano plots represent a useful way to visualise the results of differential expression analyses. This package provides a highly-configurable function that produces publication-ready volcano plots. EnhancedVolcano will attempt to fit as many point labels in the plot window as possible, thus avoiding clogging up the plot with labels that could not otherwise have been read. Other functionality allows the user to identify up to 4 different types of attributes in the same plot space via color, shape, size, and shade parameter configurations.

This package provides tools to display a rectangular heatmap (intensity plot) of a data matrix. By default, both samples (columns) and features (row) of the matrix are sorted according to a hierarchical clustering, and the corresponding dendrogram is plotted. Optionally, panels with additional information about samples and features can be added to the plot.