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
pathwaysfunction. - 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
clusterProfilerorfgseainstead because they do not require graph construction. - For de novo gene co-expression network construction, use
WGCNAinstead 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, andpathwayGraph.
Best Practices
- Always convert input gene or metabolite identifiers to match your experimental data (e.g., "SYMBOL" or "ENTREZID") using
convertIdentifiersbefore running topological analyses. - Use the
pathwayGraphfunction to convert pathway topologies into standardgraphNELobjects 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
pathwayDatabasesfunction.
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
convertIdentifiersprior to analysis. - Ignoring metabolites in metabolomics: Using the default
whichparameter will remove metabolite nodes and propagate edges through them; fix this by settingwhich = "metabolites"orwhich = "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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