| name | bioconductor-graphite |
| description | 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
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