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Explore and download data from the recount project available at https://jhubiostatistics.shinyapps.io/recount/. Using the recount package you can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level, the raw counts, the phenotype metadata used, the urls to the sample coverage bigWig files or the mean coverage bigWig file for a particular study. The RangedSummarizedExperiment objects can be used by different packages for performing differential expression analysis. Using http://bioconductor.org/packages/derfinder you can perform annotation-agnostic differential expression analyses with the data from the recount project as described at http://www.nature.com/nbt/journal/v35/n4/full/nbt.3838.html.

This package provides a Redis-based back-end for BiocParallel, enabling an alternative mechanism for distributed computation. The The 'manager' distributes tasks to a 'worker' pool through a central Redis server, rather than directly to workers as with other BiocParallel implementations. This means that the worker pool can change dynamically during job evaluation. All features of BiocParallel are supported, including reproducible random number streams, logging to the manager, and alternative 'load balancing' task distributions.

regioneR offers a statistical framework based on customizable permutation tests to assess the association between genomic region sets and other genomic features.

RegioneReloaded is a package that allows simultaneous analysis of associations between genomic region sets, enabling clustering of data and the creation of ready-to-publish graphs. It takes over and expands on all the features of its predecessor regioneR. It also incorporates a strategy to improve p-value calculations and normalize z-scores coming from multiple analysis to allow for their direct comparison. RegioneReloaded builds upon regioneR by adding new plotting functions for obtaining publication-ready graphs.

The ReportingTools software package enables users to easily display reports of analysis results generated from sources such as microarray and sequencing data. The package allows users to create HTML pages that may be viewed on a web browser such as Safari, or in other formats readable by programs such as Excel. Users can generate tables with sortable and filterable columns, make and display plots, and link table entries to other data sources such as NCBI or larger plots within the HTML page. Using the package, users can also produce a table of contents page to link various reports together for a particular project that can be viewed in a web browser. For more examples, please visit our site: http:// research-pub.gene.com/ReportingTools.

This package provides an interface between HDF5 and R. HDF5's main features are the ability to store and access very large and/or complex datasets and a wide variety of metadata on mass storage (disk) through a completely portable file format. The rhdf5 package is thus suited for the exchange of large and/or complex datasets between R and other software package, and for letting R applications work on datasets that are larger than the available RAM.

This package provides functionality for reading data from HDF Scalable Data Service from within R. The HSDSArray function bridges from HSDS to the user via the DelayedArray interface. Bioconductor manages an open HSDS instance graciously provided by John Readey of the HDF Group.

RLassoCox is a package that implements the RLasso-Cox model proposed by Wei Liu. The RLasso-Cox model integrates gene interaction information into the Lasso-Cox model for accurate survival prediction and survival biomarker discovery. It is based on the hypothesis that topologically important genes in the gene interaction network tend to have stable expression changes. The RLasso-Cox model uses random walk to evaluate the topological weight of genes, and then highlights topologically important genes to improve the generalization ability of the Lasso-Cox model. The RLasso-Cox model has the advantage of identifying small gene sets with high prognostic performance on independent datasets, which may play an important role in identifying robust survival biomarkers for various cancer types.

Workflow to process tandem MS files and build MassBank records. Functions include automated extraction of tandem MS spectra, formula assignment to tandem MS fragments, recalibration of tandem MS spectra with assigned fragments, spectrum cleanup, automated retrieval of compound information from Internet databases, and export to MassBank records.

RNAmodR provides classes and workflows for loading/aggregation data from high througput sequencing aimed at detecting post-transcriptional modifications through analysis of specific patterns. In addition, utilities are provided to validate and visualize the results. The RNAmodR package provides a core functionality from which specific analysis strategies can be easily implemented as a seperate package.

RNAmodR.AlkAnilineSeq implements the detection of m7G, m3C and D modifications on RNA from experimental data generated with the AlkAnilineSeq protocol. The package builds on the core functionality of the RNAmodR package to detect specific patterns of the modifications in high throughput sequencing data.

RNAmodR.RiboMethSeq implements the detection of 2'-O methylations on RNA from experimental data generated with the RiboMethSeq protocol. The package builds on the core functionality of the RNAmodR package to detect specific patterns of the modifications in high throughput sequencing data.

Provide utilities for ROC, with microarray focus.

This package provides an interface to the 'samtools', 'bcftools', and 'tabix' utilities for manipulating SAM (Sequence Alignment / Map), FASTA, binary variant call (BCF) and compressed indexed tab-delimited (tabix) files.

Links R to libsbml for SBML parsing, validating output, provides an S4 SBML DOM, converts SBML to R graph objects. Optionally links to the SBML ODE Solver Library (SOSLib) for simulating models.

A programmatic interface to the Semantic MEDLINE database. It provides functions for searching the database for concepts and finding paths between concepts. Path searching can also be tailored to user specifications, such as placing restrictions on concept types and the type of link between concepts. It also provides functions for summarizing and visualizing those paths.

A transcriptional regulatory network (TRN) consists of a collection of transcription factors (TFs) and the regulated target genes. TFs are regulators that recognize specific DNA sequences and guide the expression of the genome, either activating or repressing the expression the target genes. The set of genes controlled by the same TF forms a regulon. This package provides classes and methods for the reconstruction of TRNs and analysis of regulons.

RTNduals identifies co-regulatory loops between pairs of regulons inferred by the RTN package by evaluating their shared target genes. It infers dual regulons and tests whether regulator pairs exhibit cooperative or competitive influences on common targets.

RTNsurvival integrates regulons inferred by the RTN package with survival data. For each regulon, a two-tailed GSEA framework computes a differential Enrichment Score (dES) at the individual-sample level. The resulting dES distribution across samples is then used to evaluate survival associations within the cohort. Two primary workflows are supported: (i) Cox proportional hazards models, in which regulon activities are treated as predictors of survival time, and (ii) Kaplan–Meier analyses assessing cohort stratification based on regulon activity. All graphical outputs are customizable according to user specifications.

Extensible framework for interacting with multiple genome browsers (currently UCSC built-in) and manipulating annotation tracks in various formats (currently GFF, BED, bedGraph, BED15, WIG, BigWig and 2bit built-in). The user may export/import tracks to/from the supported browsers, as well as query and modify the browser state, such as the current viewport.

This package implements the remove unwanted variation (RUV) methods of Risso et al. (2014) for the normalization of RNA-Seq read counts between samples.

The S4Arrays package defines the Array virtual class to be extended by other S4 classes that wish to implement a container with an array-like semantic. It also provides: (1) low-level functionality meant to help the developer of such container to implement basic operations like display, subsetting, or coercion of their array-like objects to an ordinary matrix or array, and (2) a framework that facilitates block processing of array-like objects (typically on-disk objects).

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, Factor, and Hits) are implemented in the S4Vectors package itself (many more are implemented in the IRanges package and in other Bioconductor infrastructure packages).

The package is designed to classify microarray RNA-seq gene expression profiles.

saseR is a highly performant and fast framework for aberrant expression and splicing analyses. The main functions are: \itemize{ \item \code{\link{BamtoAspliCounts}} - Process BAM files to ASpli counts \item \code{\link{convertASpli}} - Get gene, bin or junction counts from ASpli SummarizedExperiment \item \code{\link{calculateOffsets}} - Create an offsets assays for aberrant expression or splicing analysis \item \code{\link{saseRfindEncodingDim}} - Estimate the optimal number of latent factors to include when estimating the mean expression \item \code{\link{saseRfit}} - Parameter estimation of the negative binomial distribution and compute p-values for aberrant expression and splicing } For information upon how to use these functions, check out our vignette at \url{https://github.com/statOmics/saseR/blob/main/vignettes/Vignette.Rmd} and the saseR paper: Segers, A. et al. (2023). Juggling offsets unlocks RNA-seq tools for fast scalable differential usage, aberrant splicing and expression analyses. bioRxiv. \url{https://doi.org/10.1101/2023.06.29.547014}.

scMultiSim simulates paired single cell RNA-seq, single cell ATAC-seq and RNA velocity data, while incorporating mechanisms of gene regulatory networks, chromatin accessibility and cell-cell interactions. It allows users to tune various parameters controlling the amount of each biological factor, variation of gene-expression levels, the influence of chromatin accessibility on RNA sequence data, and so on. It can be used to benchmark various computational methods for single cell multi-omics data, and to assist in experimental design of wet-lab experiments.

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 does k-nearest neighbor based statistics and visualizations with flow and mass cytometery data. This gives tSNE maps"fold change" functionality and provides a data quality metric by assessing manifold overlap between fcs files expected to be the same. Other applications using this package include imputation, marker redundancy, and testing the relative information loss of lower dimension embeddings compared to the original manifold.

Utility functions for manipulating, processing, and analyzing mass spectrometry-based single-cell proteomics data. The package is an extension to the 'QFeatures' package and relies on 'SingleCellExpirement' to enable single-cell proteomics analyses. The package offers the user the functionality to process quantitative table (as generated by MaxQuant, Proteome Discoverer, and more) into data tables ready for downstream analysis and data visualization.

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.

The algorithm is based on the non-negative tucker decomposition (NTD2) of nnTensor.

scTGIF connects the cells and the related gene functions without cell type label.

scTreeViz provides classes to support interactive data aggregation and visualization of single cell RNA-seq datasets with hierarchies for e.g. cell clusters at different resolutions. The `TreeIndex` class provides methods to manage hierarchy and split the tree at a given resolution or across resolutions. The `TreeViz` class extends `SummarizedExperiment` and can performs quick aggregations on the count matrix defined by clusters.

Seahtrue organizes oxygen consumption and extracellular acidification analysis data from experiments performed on an XF analyzer into structured nested tibbles.This allows for detailed processing of raw data and advanced data visualization and statistics. Seahtrue introduces an open and reproducible way to analyze these XF experiments. It uses file paths to .xlsx files. These .xlsx files are supplied by the userand are generated by the user in the Wave software from Agilent from the assay result files (.asyr). The .xlsx file contains different sheets of important data for the experiment; 1. Assay Information - Details about how the experiment was set up. 2. Rate Data - Information about the OCR and ECAR rates. 3. Raw Data - The original raw data collected during the experiment. 4. Calibration Data - Data related to calibrating the instrument. Seahtrue focuses on getting the specific data needed for analysis. Once this data is extracted, it is prepared for calculations through preprocessing. To make sure everything is accurate, both the initial data and the preprocessed data go through thorough checks.

seq.hotSPOT provides a resource for designing effective sequencing panels to help improve mutation capture efficacy for ultradeep sequencing projects. Using SNV datasets, this package designs custom panels for any tissue of interest and identify the genomic regions likely to contain the most mutations. Establishing efficient targeted sequencing panels can allow researchers to study mutation burden in tissues at high depth without the economic burden of whole-exome or whole-genome sequencing. This tool was developed to make high-depth sequencing panels to study low-frequency clonal mutations in clinically normal and cancerous tissues.

Provides useful functions for visualizing virus reassortment events.

The Seqinfo class stores the names, lengths, circularity flags, and genomes for a particular collection of sequences. These sequences are typically the chromosomes and/or scaffolds of a specific genome assembly of a given organism. Seqinfo objects are rarely used as standalone objects. Instead, they are used as part of higher-level objects to represent their seqinfo() component. Examples of such higher-level objects are GRanges, RangedSummarizedExperiment, VCF, GAlignments, etc... defined in other Bioconductor infrastructure packages.

SGSeq is a software 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.

Add a Bioconductor themed CSS loader to your shiny app. It is based on the shinycustomloader R package. Use a spinning Bioconductor note loader to enhance your shiny app loading screen. This package is intended for developer use.

Interactive tool for visualizing Illumina methylation array data. Both the 450k and EPIC array are supported.

While gene signatures are frequently used to predict phenotypes (e.g. predict prognosis of cancer patients), it it not always clear how optimal or meaningful they are (cf David Venet, Jacques E. Dumont, and Vincent Detours' paper "Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome"). Based on suggestions in that paper, SigCheck accepts a data set (as an ExpressionSet) and a gene signature, and compares its performance on survival and/or classification tasks against a) random gene signatures of the same length; b) known, related and unrelated gene signatures; and c) permuted data and/or metadata.

This package calculates metrics which quantify the level of similarity between ChIP-Seq profiles. More specifically, the package implements six pseudometrics specialized in pattern similarity detection in ChIP-Seq profiles.

Provides an interface to build a unified database of genomic annotations and their coordinates (gene, transcript and exon levels). It is aimed to be used when simple tab-delimited annotations (or simple GRanges objects) are required instead of the more complex annotation Bioconductor packages. Also useful when combinatorial annotation elements are reuired, such as RefSeq coordinates with Ensembl biotypes. Finally, it can download, construct and handle annotations with versioned genes and transcripts (where available, e.g. RefSeq and latest Ensembl). This is particularly useful in precision medicine applications where the latter must be reported.

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.

Signal-to-Noise applied to Gene Expression Experiments. Signal-to-noise ratios can be used as a proxy for quality of gene expression studies and samples. The SNRs can be calculated on any gene expression data set as long as gene IDs are available, no access to the raw data files is necessary. This allows to flag problematic studies and samples in any public data set.

The SparseArray package provides array-like containers for efficient in-memory representation of multidimensional sparse data in R (arrays and matrices). The package defines the SparseArray virtual class and two concrete subclasses: COO_SparseArray and SVT_SparseArray. Each subclass uses its own internal representation of the nonzero multidimensional data: the "COO layout" and the "SVT layout", respectively. SVT_SparseArray objects mimic as much as possible the behavior of ordinary matrix and array objects in base R. In particular, they suppport most of the "standard matrix and array API" defined in base R and in the matrixStats package from CRAN.

A suite of functions for simulating spatial patterns of cells in tissue images. Output images are multitype point data in SingleCellExperiment format. Each point represents a cell, with its 2D locations and cell type. Potential cell patterns include background cells, tumour/immune cell clusters, immune rings, and blood/lymphatic vessels.

SpatialArtifacts provides a data-driven two-step workflow to identify, classify, and handle spatial artifacts in spatial transcriptomics data. The package combines median absolute deviation (MAD)-based outlier detection with morphological image processing (fill, outline, and star patterns) to detect edge and interior artifacts. It supports multiple platforms including 10x Genomics Visium (standard and HD), allowing for consistent quality control across different spatial resolutions.

A new S4 class integrating Simple Features with the R package sf to bring geospatial data analysis methods based on vector data to spatial transcriptomics. Also implements management of spatial neighborhood graphs and geometric operations. This pakage builds upon SpatialExperiment and SingleCellExperiment, hence methods for these parent classes can still be used.

The Mass Spec Query Language (MassQL) is a domain-specific language enabling to express a query and retrieve mass spectrometry (MS) data in a more natural and understandable way for MS users. It is inspired by SQL and is by design programming language agnostic. The SpectraQL package adds support for the MassQL query language to R, in particular to MS data represented by Spectra objects. Users can thus apply MassQL expressions to analyze and retrieve specific data from Spectra objects.