16 Analysis with Other Knowledge Bases

16.1 Chapter overview

Aspect	External and supplementary knowledge sources
Questions	How can WikiPathways and third-party enrichment services complement standard resources, and how can service results enter the common object workflow?
Input	Organism-specific WikiPathways annotations or gene lists/tables accepted by services such as DAVID.
Methods	WikiPathways ORA/GSEA; external service enrichment and result import/visualization. GSON packaging is covered separately.
Main functions	`enrichWP()`, `gseWP()`, `get_wp_organisms()`, `enrichDAVID()`; see also import helpers in `enrichplot-import.qmd`.
Output	Canonical enrichment objects where supported, or service-specific result tables for remote workflows.
Main limitations	Remote availability, limits, schemas, and update cadence can change. Preserve queries/background details and cite the service; DAVID update cadence is a known limitation.

16.2 WikiPathways analysis

WikiPathways is a continuously updated pathway database curated by a community of researchers and pathway enthusiasts. WikiPathways produces monthly releases of GMT files for supported organisms at data.wikipathways.org. The clusterProfiler package (Yu et al. 2012) supports enrichment analysis (either ORA or GSEA) for WikiPathways using the enrichWP() and gseWP() functions. These functions will automatically download and parse the latest WikiPathways GMT file for the selected organism.

Supported organisms can be listed with get_wp_organisms(). This query is network-backed, so it is disabled during the routine book build; run it explicitly when refreshing the supported-organism list.

library(clusterProfiler)
get_wp_organisms()

16.2.1 WikiPathways over-representation analysis

enrichWP() and gseWP() download the WikiPathways GMT file over the network, and that download can fail transiently, so these calls are wrapped in retry() (defined in _common.R); feel free to drop the wrapper in your own analysis.

data(geneList, package="DOSE")
gene <- names(geneList)[abs(geneList) > 2]

retry(enrichWP(gene, organism = "Homo sapiens"))

16.2.2 WikiPathways gene set enrichment analysis

retry(gseWP(geneList, organism = "Homo sapiens"))

If your input gene ID type is not Entrez gene ID, you can use the bitr() function to convert gene ID. If you want to convert the gene IDs in output result to gene symbols, you can use the setReadable() function, see also Section 18.2.

16.3 DAVID functional analysis

DAVID is a popular bioinformatics resource for functional annotation and enrichment analysis (Huang et al. 2007). A dedicated R interface to its web service exists in RDAVIDWebService (Fresno and Fernández 2013), while clusterProfiler provides enrichDAVID for the same service. Although we recommend using enrichGO and enrichKEGG which use up-to-date data maintained by Bioconductor, enrichDAVID supports DAVID analysis for users who prefer it.

The DAVID web service host was unreachable when this edition was built

enrichDAVID() calls a remote web service, and it needs both a registered account and a reachable server. As of October 2026 the host david.ncifcrf.gov did not resolve in DNS, so every enrichDAVID() example below is marked eval: false and no link to the service is given here. The examples are kept as reference material, but the calling convention follows the installed clusterProfiler — check formals(enrichDAVID) before adapting them. Check that the host resolves before building an analysis on top of this route; the Bioconductor-backed enrichGO() and enrichKEGG() are unaffected and are what this book uses elsewhere.

Users need to register an account on DAVID website and use the email address to access the web service.

require(clusterProfiler)
data(geneList, package="DOSE")
gene = names(geneList)[abs(geneList) > 2]
david = enrichDAVID(gene = gene, 
                    idType="ENTREZ_GENE_ID", 
                    annotation="KEGG_PATHWAY",
                    david.user = "clusterProfiler@hku.hk")

The result can be visualized using the same functions as other enrichment results.

barplot(david)
cnetplot(david, foldChange=geneList)

barplot(david) above is the ordinary barplot() generic: because david is an enrichResult, it dispatches to the barplot.enrichResult() method supplied by enrichplot, which is what draws the enrichment bar chart. (The automatic code link on this page sends barplot to graphics::barplot because the class of david is not statically known in the chunk; the method that actually runs is barplot.enrichResult().)

16.3.1 DAVID Web Service Limitations

DAVID Web Service has the following limitations:

A job with more than 3000 genes to generate gene or term cluster report will not be handled by DAVID due to resource limit.
No more than 200 jobs in a day from one user or computer.
DAVID Team reserves right to suspend any improper uses of the web service without notice.

For more details, refer to the web service documentation linked from the DAVID home page once the host is reachable again.

16.3.2 Custom Background

The enrichDAVID function also supports a custom background (universe) using the universe parameter.

enrichDAVID(gene = gene,
            universe = names(geneList),
            idType = "ENTREZ_GENE_ID",
            annotation = "KEGG_PATHWAY")

16.3.3 Comparing DAVID Functional Profiles

Comparison of functional profiles among different gene clusters is also supported via compareCluster.

data(gcSample)
x=compareCluster(gcSample, fun="enrichDAVID", annotation="KEGG_PATHWAY")
plot(x)

16.3.4 Note on Data Updates

As highlighted in a Nature Methods study (Wadi et al. (2016)), DAVID has a very low update frequency, often going more than 5 years between updates, leading to a continuous decline in knowledge completeness. This means analysis results may only reflect outdated biological knowledge, which could introduce biases in current research. As shown in previous comparisons, using clusterProfiler with Bioconductor annotation data often yields more comprehensive results (more annotated genes and enriched pathways) compared to DAVID.

16.4 GSON knowledge packaging

The generalized enrichment interface is now documented in the dedicated GSON chapter. That chapter treats GSON as the knowledge representation and exchange layer: it explains how to create or download GSON objects, subset and validate them, freeze resources for reproducibility, combine sources either in parallel with gsonList() or into one merged table with gson_union(), resolve gene sets to their source pages with gson_url(), and pass them to the enrichment and visualization layers.

This chapter focuses on the remaining external databases and result-import workflows. Use GSON when the goal is to make a knowledge resource reusable, versioned, shareable, or combinable with other resources; use the external-resource sections below when the goal is to compare or import a result from a third-party service.

16.5 Next steps

For reusable, versioned resources, see GSON.
For network failures, service limits, and provenance records, see external-service troubleshooting.
To adapt external result schemas, see result import.
For custom annotations beyond these connectors, see universal enrichment.

References

Fresno, Cristóbal, and Elmer A. Fernández. 2013. “RDAVIDWebService: A Versatile r Interface to DAVID.” Bioinformatics 29 (21): 2810–11. https://doi.org/10.1093/bioinformatics/btt487.

Huang, Da, Brad T Sherman, Qina Tan, et al. 2007. “The DAVID Gene Functional Classification Tool: A Novel Biological Module-Centric Algorithm to Functionally Analyze Large Gene Lists.” Genome Biology 8 (9): R183. https://doi.org/10.1186/gb-2007-8-9-r183.

Wadi, Lina, Mona Meyer, Joel Weiser, Lincoln D. Stein, and Jüri Reimand. 2016. “Impact of Outdated Gene Annotations on Pathway Enrichment Analysis.” Nature Methods 13 (9): 705–6. https://doi.org/10.1038/nmeth.3963.

Yu, Guangchuang, Li-Gen Wang, Yanyan Han, and Qing-Yu He. 2012. “clusterProfiler: An r Package for Comparing Biological Themes Among Gene Clusters.” OMICS: A Journal of Integrative Biology 16 (5): 284–87. https://doi.org/10.1089/omi.2011.0118.