This package provides a collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control.

This package stores the data employed in the vignette of the GSVA package. These data belong to the following publications: Armstrong et al. Nat Genet 30:41-47, 2002; Cahoy et al. J Neurosci 28:264-278, 2008; Carrel and Willard, Nature, 434:400-404, 2005; Huang et al. PNAS, 104:9758-9763, 2007; Pickrell et al. Nature, 464:768-722, 2010; Skaletsky et al. Nature, 423:825-837; Verhaak et al. Cancer Cell 17:98-110, 2010; Costa et al. FEBS J, 288:2311-2331, 2021.

Cicero computes putative cis-regulatory maps from single-cell chromatin accessibility data. It also extends the monocle package for use in chromatin accessibility data.

The package disseminates mass spectrometry (MS)-based single-cell proteomics (SCP) datasets. The data were collected from published work and formatted using the `scp` data structure. The data sets contain quantitative information at spectrum, peptide and/or protein level for single cells or minute sample amounts.

This package provides a set of annotation maps describing the entire Disease Ontology.

This package provides basic functions for filtering genes from high-throughput sequencing experiments.

This package aims to bring the power and flexibility of AnnData to the R ecosystem, allowing you to effortlessly manipulate and analyze your single-cell data. This package lets you work with backed h5ad and zarr files, directly access various slots (e.g. X, obs, var), or convert the data into SingleCellExperiment and Seurat objects.

systemPipeRdata complements the systemPipeR workflow management system (WMS) by offering a collection of pre-designed data analysis workflow templates. These templates are easily accessible and can be readily loaded onto a user's system with a single command. Once loaded, the WMS can immediately utilize these templates for efficient end-to-end analysis, serving a wide range of data analysis needs.

This package implements exact and approximate methods for singular value decomposition and principal components analysis, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Where possible, parallelization is achieved using the BiocParallel framework.

The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, and Hits) are implemented in the S4Vectors package itself.

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

This package annmap provides annotation mappings for Affymetrix exon arrays and coordinate based queries to support deep sequencing data analysis. Database access is hidden behind the API which provides a set of functions such as genesInRange(), geneToExon(), exonDetails(), etc. Functions to plot gene architecture and BAM file data are also provided.

The rpx package implements an interface to proteomics data submitted to the ProteomeXchange consortium.

This package provides supporting data for the TCGAbiolinksGUI package.

GAGE is a published method for gene set (enrichment or GSEA) or pathway analysis. GAGE is generally applicable independent of microarray or RNA-Seq data attributes including sample sizes, experimental designs, assay platforms, and other types of heterogeneity. The gage package provides functions for basic GAGE analysis, result processing and presentation. In addition, it provides demo microarray data and commonly used gene set data based on KEGG pathways and GO terms. These functions and data are also useful for gene set analysis using other methods.

SGSeq is a package for analyzing splice events from RNA-seq data. Input data are RNA-seq reads mapped to a reference genome in BAM format. Genes are represented as a splice graph, which can be obtained from existing annotation or predicted from the mapped sequence reads. Splice events are identified from the graph and are quantified locally using structurally compatible reads at the start or end of each splice variant. The software includes functions for splice event prediction, quantification, visualization and interpretation.

The enrichplot package implements several visualization methods for interpreting functional enrichment results obtained from ORA or GSEA analyses. All the visualization methods are developed based on ggplot2 graphics.

This package provides a subset of BAM files untreated1.bam (single-end reads) and untreated3.bam (paired-end reads) from "Pasilla" experiment (Pasilla knock-down by Brooks et al., Genome Research 2011). See the vignette in the pasilla data package for how BAM files untreated1.bam and untreated3.bam were obtained from the RNA-Seq read sequence data that is provided by NCBI Gene Expression Omnibus under accession numbers GSM461176 to GSM461181. It also contains the DNA sequence for fly chromosome 4 to which the reads can be mapped.

This package provides a consistent C++ class interface for a variety of commonly used matrix types, including sparse and HDF5-backed matrices.

This package implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. It provides a robust toolbox supporting over 70 methods for semantic similarity analysis.

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 aims to provide a pipeline for the low-level analysis of gene expression microarray data, primarily focused on the Agilent platform, but which also provides utilities which may be useful for other platforms.

This package provides an interface to implementations of the GatingML2.0 standard to exchange gated cytometry data with other software platforms.

This package uses a Bayesian hierarchical model to detect enriched regions from ChIP-chip experiments. The common goal in analyzing this ChIP-chip data is to detect DNA-protein interactions from ChIP-chip experiments. The BAC package has mainly been tested with Affymetrix tiling array data. However, we expect it to work with other platforms (e.g. Agilent, Nimblegen, cDNA, etc.). Note that BAC does not deal with normalization, so you will have to normalize your data beforehand.