The 'enrichplot' package implements several visualization methods for interpreting functional enrichment results obtained from ORA or GSEA analysis. It is mainly designed to work with the 'clusterProfiler' package suite. All the visualizati
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Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
BiocManager::install("enrichplot")clusterProfiler, DOSE, meshes, or ReactomePA.cnetplot to show gene-concept networks, emapplot for enrichment map plots).gseaplot2) showing the distribution of enrichment scores across ranked gene lists.treeplot) or heatplots (heatplot).clusterProfiler or fgsea instead because enrichplot is strictly a visualization package.ggraph or igraph directly to avoid enrichment-specific object constraints.enrichResult or gseaResult from clusterProfiler or related packages).cnetplot or emapplot.cnetplot or heatplot.gseaplot2.pairwise_termsim() before running emapplot() or treeplot().cnetplot to identify hub genes that contribute to multiple enriched pathways.showCategory parameter to avoid overcrowded plots when dealing with many enriched terms.ggplot2 functions (e.g., theme(), labs()) to customize the output plots since all enrichplot figures are standard ggplot objects.emapplot or treeplot throws an error about missing term similarity; fix this by running x <- pairwise_termsim(x) on your enrichment object first.cex_label_category or using ggrepel options if supported.cnetplot are displayed as cryptic Entrez IDs; fix this by using setReadable() from clusterProfiler to translate IDs to gene symbols before plotting.This skill is the knowledge layer — when, why, and how to use enrichplot. 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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概要と使いどころ
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
日本語の概要は準備中です。原文の説明を表示しています。