bioconductor-stringdb

name	bioconductor-stringdb
description	tags: [bioconductor, r, proteomics, vignette-grounded]
when_to_use	Use when: Mapping a list of differentially expressed genes to STRING database identifiers using the map method.; Computing functional enrichment (e.g., Gene Ontology, KEGG) for a set of proteins using get_enrichment.; Visualizing protein-protein interaction networks with custom node colors based on log fold-change using add_diff_exp_color and plot_network.; Retrieving specific interaction partners and exper. Not for: For offline network analysis with custom, non-STRING interaction networks, use igraph instead because STRINGdb is designed to query the online STRING database.; For primary differential expression analysis of microarray data, use limma instead becaus
user-invocable	false

STRINGdb

Dependencies & Environment

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

• Version: 2.24.0 · Bioconductor: 3.23 · R: ≥ 4.6
• Imports: png, sqldf, plyr, igraph, httr, RColorBrewer, gplots, hash, plotrix
• System requirements: URL
• Install: BiocManager::install("STRINGdb")

When to Use

• Mapping a list of differentially expressed genes to STRING database identifiers using the map method.
• Computing functional enrichment (e.g., Gene Ontology, KEGG) for a set of proteins using get_enrichment.
• Visualizing protein-protein interaction networks with custom node colors based on log fold-change using add_diff_exp_color and plot_network.
• Retrieving specific interaction partners and experimental evidence scores for proteins of interest using get_interaction_partners.
• Extracting network clusters/communities from a mapped dataset using get_clusters.

When NOT to Use

• For offline network analysis with custom, non-STRING interaction networks, use igraph instead because STRINGdb is designed to query the online STRING database.
• For primary differential expression analysis of microarray data, use limma instead because STRINGdb requires pre-analyzed differential expression results (e.g., p-values and log fold changes).

Data Requirements

• A data frame containing gene identifiers (e.g., HUGO names, Entrez GeneID, ENSEMBL proteins) and optional numeric columns like pvalue or logFC.
• NCBI taxonomy identifiers for the organism of interest (e.g., 9606 for Human, 10090 for Mouse).

Key Parameters

• version ("12.0"): The STRING database version to query.
• species (9606): NCBI taxonomy identifier of the organism.
• score_threshold (400): Minimum combined confidence score for interactions to be loaded.
• input_directory (""): Local directory path to cache downloaded STRING database files for off-line use.
• network_type ("full"): Type of network to retrieve (e.g., "full" for functional or "physical" for physical subnetworks).
• removeUnmappedRows (TRUE): Whether to remove rows corresponding to unmapped genes during the map step.
• category ("Component"): The enrichment category to display when calling get_enrichment_figure.
• target_species_id (10090): The NCBI taxonomy identifier of the target species when retrieving homologs via get_homologs_besthits.

Best Practices

• Always specify a local input_directory when initializing the STRINGdb object to cache database files and enable off-line use.
• Remove unassigned genomic positions (e.g., LOC genes) from your data frame before calling map to improve the mapping percentage.
• Call set_background with your full list of mapped identifiers before running get_enrichment to ensure p-values are correctly calibrated against your specific experiment.
• Use post_payload to upload custom color mapping (like up/down-regulation halos) to the STRING server before visualizing with plot_network.

Common Pitfalls

• Wrong enrichment p-values: Running enrichment statistics without setting the background. Fix: Call set_background with the full set of measurable proteins in your experiment before testing.
• Low mapping rate: Passing unassigned or unsupported identifiers (like LOC genes). Fix: Filter out non-gene probes before mapping, or set removeUnmappedRows = FALSE to manually inspect unmapped rows.
• Network timeouts/slow execution: Re-downloading database files every session. Fix: Provide a persistent path to input_directory in the constructor to cache files locally.

Alternatives

• igraph: For general-purpose complex network research and custom graph clustering algorithms.
• limma: For performing the initial differential expression analysis to generate the p-values and fold changes required by STRINGdb.

Citations

• Szklarczyk D, et al. (2021). The STRING database in 2021: association networks and functional enrichment analysis for genome-wide gene lists. Nucleic Acids Research.

References

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

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