This package provides tools for analysis of ChIP-seq and other functional sequencing data.

This package provides an alternative interface to Bioconductor annotation resources, in particular the gene identifier mapping functionality of the org packages (e.g., org.Hs.eg.db) and the genome coordinate functionality of the TxDb packages (e.g., TxDb.Hsapiens.UCSC.hg38.knownGene).

This is an R package for interfacing with the BIOM format. This package includes basic tools for reading biom-format files, accessing and subsetting data tables from a biom object (which is more complex than a single table), as well as limited support for writing a biom-object back to a biom-format file. The design of this API is intended to match the Python API and other tools included with the biom-format project, but with a decidedly "R flavor" that should be familiar to R users. This includes S4 classes and methods, as well as extensions of common core functions/methods.

Linnorm is an R package for the analysis of RNA-seq, scRNA-seq, ChIP-seq count data or any large scale count data. It transforms such datasets for parametric tests. In addition to the transformtion function (Linnorm), the following pipelines are implemented:

1. Library size/batch effect normalization (Linnorm.Norm)

2. Cell subpopluation analysis and visualization using t-SNE or PCA K-means clustering or hierarchical clustering (Linnorm.tSNE, Linnorm.PCA, Linnorm.HClust)

3. Differential expression analysis or differential peak detection using limma (Linnorm.limma)

4. Highly variable gene discovery and visualization (Linnorm.HVar)

5. Gene correlation network analysis and visualization (Linnorm.Cor)

6. Stable gene selection for scRNA-seq data; for users without or who do not want to rely on spike-in genes (Linnorm.SGenes)

7. Data imputation (Linnorm.DataImput).

Linnorm can work with raw count, CPM, RPKM, FPKM and TPM. Additionally, the RnaXSim function is included for simulating RNA-seq data for the evaluation of DEG analysis methods.

Managing data from large scale projects such as The Cancer Genome Atlas (TCGA) for further analysis is an important and time consuming step for research projects. Several efforts, such as Firehose project, make TCGA pre-processed data publicly available via web services and data portals but it requires managing, downloading and preparing the data for following steps. This package provides an extensible R based data client for Firehose pre-processed data.

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.

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 an integrated web interface for doing microarray analysis using several of the Bioconductor packages. It is intended to be deployed as a centralized bioinformatics resource for use by many users. Currently only Affymetrix oligonucleotide analysis is supported.

Single-cell RNA-seq (scRNA-seq) is widely used to investigate the composition of complex tissues since the technology allows researchers to define cell-types using unsupervised clustering of the transcriptome. However, due to differences in experimental methods and computational analyses, it is often challenging to directly compare the cells identified in two different experiments. scmap is a method for projecting cells from a scRNA-seq experiment onto the cell-types or individual cells identified in a different experiment.

This package identifies regions of ChIP experiments with high signal in the input, that lead to spurious peaks during peak calling.

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

tRNAdbImport imports the entries of the tRNAdb and mtRNAdb as GRanges object.

Genome-wide association studies (GWAS) are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. The R package SNPRelate provides a binary format for single-nucleotide polymorphism (SNP) data in GWAS utilizing CoreArray Genomic Data Structure (GDS) data files. The GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. SNPRelate is also designed to accelerate two key computations on SNP data using parallel computing for multi-core symmetric multiprocessing computer architectures: Principal Component Analysis (PCA) and relatedness analysis using Identity-By-Descent measures. The SNP GDS format is also used by the GWASTools package with the support of S4 classes and generic functions. The extended GDS format is implemented in the SeqArray package to support the storage of single nucleotide variations (SNVs), insertion/deletion polymorphism (indel) and structural variation calls in whole-genome and whole-exome variant data.

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 tools for normalizing and analyzing of GeneChip Mapping 100K and 500K Set. Affymetrix GeneChip Human Mapping 100K and 500K Set allows the DNA copy number mea- surement of respectively 2× 50K and 2× 250K SNPs along the genome. Their high density allows a precise localization of genomic alterations and makes them a powerful tool for cancer and copy number polymorphism study.

This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang, respectively, for measuring semantic similarities among Disease ontology (DO) terms and gene products. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented for discovering disease associations of high-throughput biological data.

This package provides a collection of reference expression datasets with curated cell type labels, for use in procedures like automated annotation of single-cell data or deconvolution of bulk RNA-seq.

FlowSOM offers visualization options for cytometry data, by using self-organizing map clustering and minimal spanning trees.

This package provides a differential abundance analysis for the comparison of two or more conditions. Useful for analyzing data from standard RNA-seq or meta-RNA-seq assays as well as selected and unselected values from in-vitro sequence selections. Uses a Dirichlet-multinomial model to infer abundance from counts, optimized for three or more experimental replicates. The method infers biological and sampling variation to calculate the expected false discovery rate, given the variation, based on a Wilcoxon Rank Sum test and Welch's t-test, a Kruskal-Wallis test, a generalized linear model, or a correlation test. All tests report p-values and Benjamini-Hochberg corrected p-values. ALDEx2 also calculates expected standardized effect sizes for paired or unpaired study designs.

This package implements functions for simulation-based inference. In particular, it implements functions to perform likelihood inference from data summaries whose distributions are simulated. The package implements more advanced methods than the ones first described in: Rousset, Gouy, Almoyna and Courtiol (2017) <doi:10.1111/1755-0998.12627>.

This package contains genome-wide annotations for Human, primarily based on mapping using Entrez Gene identifiers.

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.

This is a package to support identification of markers of rare cell types by looking at genes whose expression is confined in small regions of the expression space.

This package provides functions to ease the transition between Rmarkdown and LaTeX documents when authoring a Bioconductor Workflow.