This package takes sample information in the form of the fraction of mutations in each of 96 trinucleotide contexts and identifies the weighted combination of published signatures that, when summed, most closely reconstructs the mutational profile.

This package contains a collection of 9 datasets, andrews and bakulski cord blood, blood gse35069, blood gse35069 chen, blood gse35069 complete, combined cord blood, cord bloo d gse68456, gervin and lyle cord blood, guintivano dlpfc and saliva gse48472. The data are used to estimate cell counts using Extrinsic epigenetic age acceleration (EEAA) method. It also contains a collection of 12 datasets to use with MethylClock package to estimate chronological and gestational DNA methylation with estimators to use with different methylation clocks.

Haplotype-aware Hidden Markov Model for RNA (HaHMMR) is a method for detecting copy number variations (CNVs) from bulk RNA-seq data. Additional examples, documentations, and details on the method are available at https://github.com/kharchenkolab/hahmmr/.

This is an R package to make it easier to import and store phylogenetic trees with associated data; and to link external data from different sources to phylogeny. It also supports exporting phylogenetic trees with heterogeneous associated data to a single tree file and can be served as a platform for merging tree with associated data and converting file formats.

DSS is an R library performing differential analysis for count-based sequencing data. It detects differentially expressed genes (DEGs) from RNA-seq, and differentially methylated loci or regions (DML/DMRs) from bisulfite sequencing (BS-seq). The core of DSS is a dispersion shrinkage method for estimating the dispersion parameter from Gamma-Poisson or Beta-Binomial distributions.

DeconSeq is an R package for deconvolution of heterogeneous tissues based on mRNA-Seq data. It models the expression levels from heterogeneous cell populations in mRNA-Seq as the weighted average of expression from different constituting cell types and predicted cell type proportions of single expression profiles.

This package provides full genome sequences for Homo sapiens (Human) as provided by NCBI (GRCh38, 2013-12-17) and stored in Biostrings objects.

This package provides a framework for the visualization of genome coverage profiles. It can be used for ChIP-seq experiments, but it can be also used for genome-wide nucleosome positioning experiments or other experiment types where it is important to have a framework in order to inspect how the coverage distributed across the genome.

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

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).

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

This package speeds up the derfinder package when using multiple cores. It is particularly useful when using BiocParallel and it helps reduce the time spent loading the full derfinder package when running the F-statistics calculation in parallel.

Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well.

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.

This is a package for identification of metabolites using high precision mass spectrometry. MS peaks are used to derive a ranked list of sum formulae, alternatively for a given sum formula the theoretical isotope distribution can be calculated to search in MS peak lists.

This package provides many functions for computing the nonparametric maximum likelihood estimator (NPMLE) for censored and truncated data.

This package defines an S4 class for storing data from spatial -omics experiments. The class extends SingleCellExperiment to support storage and retrieval of additional information from spot-based and molecule-based platforms, including spatial coordinates, images, and image metadata. A specialized constructor function is included for data from the 10x Genomics Visium platform.

Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments.

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.

r-pathview is a tool set for pathway based data integration and visualization. It maps and renders a wide variety of biological data on relevant pathway graphs. All users need is to supply their data and specify the target pathway. This package automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and render pathway graph with the mapped data. In addition, r-pathview also seamlessly integrates with pathway and gene set (enrichment) analysis tools for large-scale and fully automated analysis.

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

This package implements the unified Wilcoxon-Mann-Whitney Test for qPCR data. This modified test allows for testing differential expression in qPCR data.

This is an annotation package for Illumina's EPIC methylation arrays.

Animalcules is an R package for utilizing up-to-date data analytics, visualization methods, and machine learning models to provide users an easy-to-use interactive microbiome analysis framework. It can be used as a standalone software package or users can explore their data with the accompanying interactive R Shiny application. Traditional microbiome analysis such as alpha/beta diversity and differential abundance analysis are enhanced, while new methods like biomarker identification are introduced by animalcules. Powerful interactive and dynamic figures generated by animalcules enable users to understand their data better and discover new insights.