Table 2.
Categorization of enrichment analysis tools
| Tool category | Description | Indication and limitation | Sub-type of algorithms | Methods | Example tool |
|---|---|---|---|---|---|
| Class I: singular enrichment analysis (SEA) | Enrichment P-value is calculated on each term from the pre-selected interesting gene list. Then, enriched terms are listed in a simple linear text format. This strategy is the most traditional algorithm. It is still dominantly used by most of the enrichment analysis tools. | Capable of analyzing any gene list, which could be selected from any high-throughput biological studies/technologies (e.g. Microarray, ChIP-on-CHIP, ChIP-on-sequence, SNP array, EXON array, large scale sequence, etc.). However, the deeper inter-relationships among the terms may not be fully captured in linear format report. | Global reference background Local reference background Neural network | Fisher's exact hypergeometric chi-square binomial Fisher's Exact hypergeometric chi-square binomial Bayesian | GoStat, GoMiner, GOTM, BinGO, GOtoolBox, GFinder, etc. DAVID, Onto-Express, GARBAN, FatiGO, etc. BayGO |
| Class II: gene set enrichment analysis (GSEA) | Entire genes (without pre-selection) and associated experimental values are considered in the enrichment analysis. The unique features of this strategy are: (i) No need to pre-select interesting genes, as opposed to Classes I and II; (ii) Experimental values integrated into P-value calculation. | Suitable for pair-wide biological studies (e.g. disease versus control). Currently, may be difficult to be applied to the diverse data structures derived by a complex experimental design and some of the new technologies (e.g. SNP, EXON, Promoter arrays). | Based on ranked gene list Based on continuous gene values | Kolmogorov–Smirnov-like t-Test permutation Z-score | GSEA, CapMap, etc. FatiScan, ADGO, ermineJ, PAGE, iGA, GO-Mapper, GOdist, FINA, T-profiler, MetaGP, etc. |
| Class III: modular enrichment analysis (MEA) | This strategy inherits key spirit of SEA. However, the term–term/gene–gene relationships are considered into enrichment P-value calculation. The advantage of this strategy is that term–term/gene–gene relationship might contain unique biological meaning that is not held by a single term or gene. Such network/modular analysis is closer to the nature of biological data structure. | Capable of analyzing any gene lists, which could be selected from any high-throughput biological studies/technologies, like Class I. Emphasis on network relationships during analysis. ‘Orphan’ gene/term (with little relationships to other genes/terms), that sometimes could be very interesting, too, may be left out from the analysis. | Composite annotations DAG Structure Global annotation relationship | Measure enrichment on joint terms Measure enrichment by considering parents-child relationships Measure term–term global similarity with Kappa Statistics Czekanowski-Dice Pearson's correlation | ADGO, GeneCodis, ProfCom, etc. topGO, Ontologizer, POSOC, etc. DAVID, GoToolBox, etc. |