Genomic data analyses requires integrated visualization of known genomic information and new experimental data. Gviz uses the biomaRt and the rtracklayer packages to perform live annotation queries to Ensembl and UCSC and translates this to
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Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
BiocManager::install("Gviz")plotTracks.DataTrack.GenomeAxisTrack.IdeogramTrack.GRanges, IRanges, or data.frame objects.chr7 on mm9) for fetching online annotation data.BSgenome package (e.g., BSgenome.Hsapiens.UCSC.hg19) for SequenceTrack.plotTracks.DataTrack (e.g., "histogram", "dot").GenomeAxisTrack.availableDisplayPars to discover which display parameters control the appearance of a specific track class.addScheme and options(Gviz.scheme = "myScheme") to avoid repetitive typing.options(ucscChromosomeNames=FALSE).from and to arguments in plotTracks.reverseStrand = TRUE globally in plotTracks rather than per track.GenomeGraphs: An older package that Gviz is loosely based on, but Gviz offers increased performance and flexibility.rtracklayer: Used for importing/exporting genomic annotations and connecting to browsers, but lacks the native R grid graphics plotting capabilities of Gviz.This skill is the knowledge layer — when, why, and how to use gviz. To run this analysis on your own data with managed compute, automated QC, and reproducible outputs, use BioMate — free to start.
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The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
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This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte
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This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, En
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apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.
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ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
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AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched with
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