This package provides a convenient way to analyze and visualize PICRUSt2 output with pre-defined plots and functions. It allows for generating statistical plots about microbiome functional predictions and offers customization options. It features a one-click option for creating publication-level plots, saving time and effort in producing professional-grade figures. It streamlines the PICRUSt2 analysis and visualization process.

This is a supportive data package for the software package gage. However, the data supplied here are also useful for gene set or pathway analysis or microarray data analysis in general. In this package, we provide two demo microarray dataset: GSE16873 (a breast cancer dataset from GEO) and BMP6 (originally published as an demo dataset for GAGE, also registered as GSE13604 in GEO). This package also includes commonly used gene set data based on KEGG pathways and GO terms for major research species, including human, mouse, rat and budding yeast. Mapping data between common gene IDs for budding yeast are also included.

This package provides a universal, user friendly, single-cell and bulk RNA sequencing visualization toolkit that allows highly customizable creation of color blindness friendly, publication-quality figures. dittoSeq accepts both SingleCellExperiment (SCE) and Seurat objects, as well as the import and usage, via conversion to an SCE, of SummarizedExperiment or DGEList bulk data. Visualizations include dimensionality reduction plots, heatmaps, scatterplots, percent composition or expression across groups, and more. Customizations range from size and title adjustments to automatic generation of annotations for heatmaps, overlay of trajectory analysis onto any dimensionality reduciton plot, hidden data overlay upon cursor hovering via ggplotly conversion, and many more. All with simple, discrete inputs. Color blindness friendliness is powered by legend adjustments (enlarged keys), and by allowing the use of shapes or letter-overlay in addition to the carefully selected codedittoColors().

This package efficiently obtains count vectors from indexed bam files. It counts the number of nucleotide sequence reads in given genomic ranges and it computes reads profiles and coverage profiles. It also handles paired-end data.

This package provides functions for bipartite network rewiring through N consecutive switching steps and for the computation of the minimal number of switching steps to be performed in order to maximise the dissimilarity with respect to the original network. It includes functions for the analysis of the introduced randomness across the switching steps and several other routines to analyse the resulting networks and their natural projections.

This package provides a flexible method for fitting regression models that can be used to find genes that are differentially expressed along one or multiple lineages in a trajectory. Based on the fitted models, it uses a variety of tests suited to answer different questions of interest, e.g. the discovery of genes for which expression is associated with pseudotime, or which are differentially expressed (in a specific region) along the trajectory. It fits a negative binomial generalized additive model (GAM) for each gene, and performs inference on the parameters of the GAM.

This package provides the data for the gene expression enrichment analysis conducted in the package ABAEnrichment. The package includes three datasets which are derived from the Allen Brain Atlas:

1. Gene expression data from Human Brain (adults) averaged across donors,

2. Gene expression data from the Developing Human Brain pooled into five age categories and averaged across donors, and

3. a developmental effect score based on the Developing Human Brain expression data.

All datasets are restricted to protein coding genes.

The package is able to read bead-level data (raw TIFFs and text files) output by BeadScan as well as bead-summary data from BeadStudio. Methods for quality assessment and low-level analysis are provided.

INSPEcT (INference of Synthesis, Processing and dEgradation rates in Time-Course experiments) analyses 4sU-seq and RNA-seq time-course data in order to evaluate synthesis, processing and degradation rates and assess via modeling the rates that determines changes in mature mRNA levels.

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.

This package provides a collection of tools for analyzing and visualizing bisulfite sequencing data.

This is the human disease ontology R package HDO.db, which provides the semantic relationship between human diseases. Relying on the DOSE and GOSemSim packages, this package can carry out disease enrichment and semantic similarity analyses. Many biological studies are achieved through mouse models, and a large number of data indicate the association between genotypes and phenotypes or diseases. The study of model organisms can be transformed into useful knowledge about normal human biology and disease to facilitate treatment and early screening for diseases. Organism-specific genotype-phenotypic associations can be applied to cross-species phenotypic studies to clarify previously unknown phenotypic connections in other species. Using the same principle to diseases can identify genetic associations and even help to identify disease associations that are not obvious.

This package is a collection of Strand-seq data. The main purpose is to demonstrate functionalities of the breakpointR package.

This package provides full genome sequences for Danio rerio (Zebrafish) as provided by UCSC (danRer11, May 2017) and stored in Biostrings objects.

This package provides a method for combining single-cell cytometry datasets, which increases the analytical flexibility and the statistical power of the analyses while minimizing technical noise.

This package provides classes and other infrastructure to implement filters for manipulating Bioconductor annotation resources. The filters are used by ensembldb, Organism.dplyr, and other packages.

The objective of AGDEX is to evaluate whether the results of a pair of two-group differential expression analysis comparisons show a level of agreement that is greater than expected if the group labels for each two-group comparison are randomly assigned. The agreement is evaluated for the entire transcriptome and (optionally) for a collection of pre-defined gene-sets. Additionally, the procedure performs permutation-based differential expression and meta analysis at both gene and gene-set levels of the data from each experiment.

This package provides infrastructure to store and manage all aspects related to a complete proteomics or metabolomics mass spectrometry (MS) experiment. The MsExperiment package provides light-weight and flexible containers for MS experiments building on the new MS infrastructure provided by the Spectra, QFeatures and related packages. Along with raw data representations, links to original data files and sample annotations, additional metadata or annotations can also be stored within the MsExperiment container. To guarantee maximum flexibility only minimal constraints are put on the type and content of the data within the containers.

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

This package provides full genome sequences for Caenorhabditis elegans (Worm) as provided by UCSC (ce6, May 2008) and stored in Biostrings objects.

This package provides functions and classes for de novo prediction of transcription factor binding consensus by heuristic search.

This package provides a set of annotation maps for the REACTOME database, assembled using data from REACTOME.

This package provides an SQL-based mass spectrometry (MS) data backend supporting also storage and handling of very large data sets. Objects from this package are supposed to be used with the Spectra Bioconductor package. Through the MsBackendSql with its minimal memory footprint, this package thus provides an alternative MS data representation for very large or remote MS data sets.

This package provides memory efficient string containers, string matching algorithms, and other utilities, for fast manipulation of large biological sequences or sets of sequences.