Graph objects from pathway topology derived from KEGG, Panther, PathBank, PharmGKB, Reactome SMPDB and WikiPathways databases.
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Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
BiocManager::install("graphite")pathways function.convertIdentifiers.prepareSPIA, runSPIA), topologyGSA (runTopologyGSA), or clipper (runClipper).cytoscapePlot.clusterProfiler or fgsea instead because they do not require graph construction.WGCNA instead because graphite retrieves curated, database-defined pathway topologies rather than inferring networks from data.DE_Colorectal) and a background list of all genes.pathways, though pathways can be cached locally.pathways.pathways.nodes, edges, and pathwayGraph.convertIdentifiers before running topological analyses.pathwayGraph function to convert pathway topologies into standard graphNEL objects to leverage external network visualization and graph operations.which = "mixed" to retrieve pathways containing both proteins and metabolites without edge propagation.pathwayDatabases function.convertIdentifiers prior to analysis.which parameter will remove metabolite nodes and propagate edges through them; fix this by setting which = "metabolites" or which = "mixed" for metabolomics data.edgeData.prepareSPIA.runClipper.runTopologyGSA.This skill is the knowledge layer — when, why, and how to use graphite. 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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