The biovizBase package is designed to provide a set of utilities, color schemes and conventions for genomic data. It serves as the base for various high-level packages for biological data visualization. This saves development effort and enc
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Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
BiocManager::install("biovizBase")colorBlindSafePal().getBioColor().GRanges objects for plotting by adding disjoint stepping levels (addStepping()) or shrinking gaps (shrinkageFun()).getIdeogram().ggbio because biovizBase only provides the low-level utilities and color schemes.visnab because biovizBase is designed as a foundational infrastructure package rather than an interactive plotting tool.GRanges objects (from the GenomicRanges package) for genomic manipulations, or character vectors of biological categories (e.g., "A", "C", "T", "G", "N") for color mapping.GRanges to use utilities like addStepping(), maxGap(), or gaps().colorBlindSafePal().getBioColor().addStepping().shrinkageFun().getIdeogram().getBioColor() instead of accessing options directly to hide internal complexity and ensure uniform color schemes across all graphics.dichromat() to ensure accessibility for deuteranopia and protanopia.plotColorLegend() or showColor() to visually verify your selected color schemes before applying them to complex genomic plots.NA or errors. Fix: Set repeatable = TRUE when calling the function returned by colorBlindSafePal().options(biovizBase = opts)) so getBioColor() returns uniform colors globally.ggbio: Built on top of biovizBase, use this for actual static genomic plotting rather than just base utilities.visnab: Built on top of biovizBase, use this for interactive genomic graphics.RColorBrewer: Provides general color palettes, but lacks the built-in biological category mappings (like nucleotides or cytobands) provided by biovizBase.dichromat: Used for colorblindness simulation, but does not provide genomic-specific color generators out of the box.This skill is the knowledge layer — when, why, and how to use biovizbase. 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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