Volcano plots represent a useful way to visualise the results of differential expression analyses. Here, we present a highly-configurable function that produces publication-ready volcano plots. EnhancedVolcano will attempt to fit as many po
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Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
BiocManager::install("EnhancedVolcano")EnhancedVolcano() function.selectLab parameter.encircle and shade parameters.DESeq2 (e.g., DESeq() and results()) instead, because EnhancedVolcano only visualizes the statistical output.mapIds() from AnnotationDbi instead, as EnhancedVolcano requires the labels to be pre-formatted and mapped before plotting.rownames(res)).lfcShrink() from DESeq2 to obtain moderated 'shrunk' estimates of log2FC differences before plotting.drawConnectors = TRUE to fit more labels in the plot window without overcrowding the points.parseLabels = TRUE along with italic() to parse and present labels as italicised text.gridlines.major = FALSE, gridlines.minor = FALSE) to make extra threshold lines (hline, vline) more visible.selectLab parameter to only label key variables of interest.legendPosition to 'none' to make the legend completely invisible.ggalt package is installed before using the encircle parameter.This skill is the knowledge layer — when, why, and how to use enhancedvolcano. 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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