bioconductor-graphite

name: bioconductor-graphitedescription: Graph objects from pathway topology derived from KEGG, Panther, PathBank, PharmGKB, Reactome SMPDB and WikiPathways databases.when_to_use: "Use when: Retrieving pathway topologies as directed/undirected graph objects from major databases (KEGG, Reactome, WikiPathways, Panther, PathBank, PharmGKB) using the pathways function.; Converting pathway identifiers (e.g., from UNIPROT to SYMBOL or ENTREZID) to match experimental data using convertIdentifiers.; Preparing and running topology-aware pathway enrichment analyses like SPIA (prepareSPIA, runSP. Not for: For simple over-representation analysis (ORA) or gene set enrichment analysis (GSEA) that ignores pathway topology, use clusterProfiler or fgsea instead because they do not require graph construction.; For de novo gene co-expression network construct"user-invocable: false

graphite

Dependencies & Environment

Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

• Version: 1.58.0 · Bioconductor: 3.23 · R: ≥ 4.6
• Imports: AnnotationDbi, graph, httr, rappdirs, rlang, lifecycle, purrr, dir.expiry
• Install: BiocManager::install("graphite")

When to Use

• Retrieving pathway topologies as directed/undirected graph objects from major databases (KEGG, Reactome, WikiPathways, Panther, PathBank, PharmGKB) using the pathways function.
• Converting pathway identifiers (e.g., from UNIPROT to SYMBOL or ENTREZID) to match experimental data using convertIdentifiers.
• Preparing and running topology-aware pathway enrichment analyses like SPIA (prepareSPIA, runSPIA), topologyGSA (runTopologyGSA), or clipper (runClipper).
• Exporting pathway network structures to Cytoscape for visualization using cytoscapePlot.

When NOT to Use

• For simple over-representation analysis (ORA) or gene set enrichment analysis (GSEA) that ignores pathway topology, use clusterProfiler or fgsea instead because they do not require graph construction.
• For de novo gene co-expression network construction, use WGCNA instead because graphite retrieves curated, database-defined pathway topologies rather than inferring networks from data.

Data Requirements

• Input format: Gene identifiers (e.g., Entrez IDs, Ensembl IDs, or UniProt accessions) or metabolite identifiers (e.g., CHEBI).
• For enrichment: A vector of differentially expressed genes/metabolites with their log-fold changes (e.g., DE_Colorectal) and a background list of all genes.
• Database versioning: Requires active internet access for initial database downloads via pathways, though pathways can be cached locally.

Key Parameters

• species: Character; the target organism (e.g., "hsapiens", "mmusculus") used in pathways.
• database: Character; the pathway database to query (e.g., "kegg", "reactome", "wikipathways") used in pathways.
• which: Character; specifies whether to retrieve "proteins", "metabolites", or "mixed" networks in functions like nodes, edges, and pathwayGraph.

Best Practices

• Always convert input gene or metabolite identifiers to match your experimental data (e.g., "SYMBOL" or "ENTREZID") using convertIdentifiers before running topological analyses.
• Use the pathwayGraph function to convert pathway topologies into standard graphNEL objects to leverage external network visualization and graph operations.
• When analyzing both transcriptomic and metabolomic data, use which = "mixed" to retrieve pathways containing both proteins and metabolites without edge propagation.
• Check available databases for your species of interest using the pathwayDatabases function.

Common Pitfalls

• Identifier mismatch: Passing gene symbols to a database structured around UNIPROT IDs will result in zero mapped nodes; fix this by translating identifiers using convertIdentifiers prior to analysis.
• Ignoring metabolites in metabolomics: Using the default which parameter will remove metabolite nodes and propagate edges through them; fix this by setting which = "metabolites" or which = "mixed" for metabolomics data.
• Losing edge attributes: Treating directed biological interactions as simple undirected graphs can dilute statistical power; fix this by preserving edge attributes (e.g., "Binding", "Control") visible via edgeData.

Alternatives

• SPIA: Specifically designed for Signaling Pathway Impact Analysis, which is natively supported by graphite-derived topologies via prepareSPIA.
• clipper: A package for topological analysis that can be used to study metabolomics data, natively integrated via runClipper.
• topologyGSA: A package for topological gene set analysis, natively integrated via runTopologyGSA.

Citations

• Sales G, Calura E, Romualdi C (2012). "graphite: a Bioconductor package for pathway topology alignments and network-based analyses." BMC Bioinformatics.

References

• Homepage: https://bioconductor.org/packages/graphite
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/graphite/inst/doc/graphite.pdf

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