Bioconductor Topgo

topGO

Dependencies & Environment

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

• Version: 2.64.0 · Bioconductor: 3.23 · R: ≥ 4.6
• Depends: BiocGenerics, graph, Biobase, GO.db, AnnotationDbi, SparseM
• Imports: lattice, matrixStats, DBI
• Install: BiocManager::install("topGO")

When to Use

• Performing Gene Ontology (GO) enrichment analysis using the runTest function on gene lists derived from differential expression.
• Accounting for the hierarchical structure and dependencies of the GO DAG using algorithms like elim, weight, or weight01.
• Testing enrichment using continuous statistical tests (e.g., Kolmogorov-Smirnov via statistic = "ks") on gene scores rather than just binary gene lists.
• Visualizing the subgraph induced by the most significant GO terms using showSigOfNodes.

When NOT to Use

• For pathway databases other than Gene Ontology (e.g., KEGG, Reactome) (use clusterProfiler because topGO is strictly designed for GO graph topologies).
• For fast, simple over-representation analysis without graph-topology correction (use gprofiler2 or enrichR because topGO builds complex local graph structures which takes more time).

Data Requirements

• A named vector of gene identifiers with associated scores (e.g., p-values) or a binary factor (0/1) indicating interesting genes.
• A mapping between gene identifiers and GO terms (e.g., via a Bioconductor annotation package like hgu95av2.db, or custom mappings read by readMappings).

Key Parameters

• ontology ("BP"): Character string specifying the ontology of interest; must be "BP", "MF", or "CC".
• allGenes (none): Named vector of type numeric or factor defining the gene universe and their scores/status.
• geneSelectionFun (none): Function to specify which genes are interesting based on the gene scores (e.g., topDiffGenes).
• nodeSize (10): Integer used to prune the GO hierarchy from terms which have fewer than this many annotated genes.
• annot (annFUN.db): Function which maps gene identifiers to GO terms (e.g., annFUN.db, annFUN.gene2GO, annFUN.org).
• algorithm ("weight01"): Method for dealing with the GO graph structure in runTest (e.g., "classic", "elim", "weight", "parentchild").
• statistic ("fisher"): The test statistic to use in runTest (e.g., "fisher", "ks", "t").

Best Practices

• Filter out genes with low expression or small variability using genefilter before defining the gene universe to improve computational efficiency.
• Compare results from multiple algorithms (e.g., classic vs elim) using GenTable to see how topology correction affects the significance of parent/child terms.
• Use inverseList to easily transform custom mappings from gene-to-GOs to GO-to-genes when preparing custom annotations.
• Use score to extract p-values from a topGOresult object for custom plotting or comparisons.

Common Pitfalls

• Providing custom annotations with excessive redundancy: Slows down the analysis significantly. Fix: Provide only the most specific GO annotations in the custom mapping file; topGO handles the ancestor propagation.
• Assuming all input genes are used: Not all genes in the provided allGenes list can be annotated to GO. Fix: Check the topGOdata summary to see the actual number of "feasible genes" used in the analysis.
• Using incompatible algorithms and statistics: Some statistical tests do not work with every method (e.g., ks with parentchild). Fix: Consult the compatibility matrix and use supported combinations like elim with ks or classic with fisher.

Alternatives

• clusterProfiler: For versatile enrichment analysis supporting multiple databases (GO, KEGG, MSigDB) and rich visualizations.
• goseq: For GO enrichment of RNA-seq data while correcting for gene length bias.
• enrichR: For fast, web-based enrichment analysis across dozens of gene set libraries.

Citations

• Alexa A, Rahnenführer J, Lengauer T (2006). "Improved scoring of functional groups from gene expression data by decorrelating GO graph structure." Bioinformatics.
• Grossmann et al. (2007). "Improved detection of overrepresentation of Gene-Ontology annotations with parent child intersection." Bioinformatics.

References

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

Run this on BioMate

This skill is the knowledge layer — when, why, and how to use topgo. To run this analysis on your own data with managed compute, automated QC, and reproducible outputs, use BioMate — free to start.

▶ Open topgo on BioMate →