Last modified 11 months ago Last modified on 04/03/11 17:08:41

Enrichment Widgets

Enrichment widgets are located on the list analysis page. There are a number of different types of enrichment widgets, but all list a term, a count and an associated p-value. The term can be something like a publication name or a GO term. The count is the number of times that term appears for objects in your list. The p-value is the probability that result occurs by chance, thus a lower p-value indicates greater enrichment.

Method

The p-value is calculated using the Hypergeometric distribution. Four numbers are used to calculate each p-value:

  • n = the number of objects in your list
  • N = the number of objects in the reference population
  • k = the number of objects annotated with this item in your list
  • M = the number of objects annotated with item in the reference population 
           (M choose k) (N-M choose n-k)
      P =   -----------------------------
                    N choose n

Multiple Test Correction

When multiple tests (statistical inferences)are run in parallel, the probability of false positive (Type I) errors increases. To address this issue, many multiple test corrections have been developed to take into account the number of tests being carried out and to correct the p-values accordingly. Enrichment widgets have three different multiple test corrections: Bonferroni, Holm-Bonferroni, and Benjamini Hochberg.

In enrichment widgets the number of "tests run" is the number of terms associated with objects in the "reference list". Please Note, in earlier versions of InterMine (0.95 and below) the number of "tests run" was the number of terms associated with objects in the "query list". This change has made the multiple test correction more rigorous, and will reduce the occurrence of spuriously low p-values.

Each enrichment widget has four test correction options:

None

No test correction performed, these are the raw results. These p-values will be lower (more significant) than if test correction was applied.

Bonferroni

Bonferroni is the simplest and most conservative method of multiple test correction. The number of tests run (the number of terms associated with objects in the reference list) is multiplied by the un-corrected p-value of each term to give the corrected p-value.

Holm-Bonferroni

Benjamini Hochberg

Benjamini-Hochberg test correction applied.

References

GOstat: Find statistically overrepresented Gene Ontologies within a group of genes
Beissbarth T, Speed TP
 Bioinformatics, 6.2004; 20(9): 1464-1465.
pubmed id:  14962934

GO::TermFinder--open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes.
Boyle EI, Weng S, Gollub J, Jin H, Botstein D, Cherry JM, Sherlock G.
 Bioinformatics. 2004 Dec 12;20(18):3710-5. Epub 2004 Aug 5.
pubmed id:  15297299

Controlling the false discovery rate: a practical and powerful approach to multiple testing
Benjamini, Yoav; Hochberg, Yosef
Journal of the Royal Statistical Society, 1995, Series B (Methodological) 57 (1): 289–300.

Augmentation Procedures for Control of the Generalized Family-Wise Error Rate and Tail Probabilities for the Proportion of False Positives
van der Laan, Mark J.; Dudoit, Sandrine; and Pollard, Katherine S.
 Statistical Applications in Genetics and Molecular Biology: Vol. 3 : Iss. 1, Article 15, 2004.