Functions for plotting genomic data
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Functions for plotting genomic data.
Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
BiocManager::install("geneplotter")cPlot and cColor.alongChrom.chromLocation objects to map experimental probe data to physical chromosome locations via buildChromLocation.Gviz or ggbio instead.ggplot2 or ComplexHeatmap instead.ExpressionSet objects (e.g., sample.ExpressionSet) or numeric matrices of genomic data.hu6800.db, hgu95av2.db) to build chromLocation objects and map probe IDs to chromosomal coordinates.cPlot.alongChrom (e.g., "cumulative").cColor or alongChrom.buildChromLocation with the appropriate Bioconductor annotation package name (e.g., "hu6800", "hgu95av2") to construct the chromLocation object.chromLocation object numerically before plotting with cPlot to ensure a logical layout.layout or par) when using cPlot to ensure chromosome labels and data points do not overlap.This skill is the knowledge layer — when, why, and how to use geneplotter. 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
The English summary is being prepared. Showing the original description.