![]() The Bonferroni correction provides strong control over the Family-Wise Error Rate (FWER), which is defined as the probability of making at least one type I (false positive) error. One of the most basic corrections is the Bonferroni correction. Multiple testing corrections are designed to provide better control over the false positive rate at a given significance level. Suppose you have one truly positive category, this would imply you would have identified 5 times more false positive than true positive categories. When testing individual categories at a significance level ( a level) of say 0.05, you would expect 5 out of each 100 tested categories to be identified as being over-represented just by chance. You should only consider using the binomial test if your test set contains several thousand genes.īecause BiNGO tests all GO labels present in the test set, the number of statistical tests performed in a single analysis may amount to several hundreds. Its counterpart with replacement, the binomial test, provides only an approximate p-value but requires less calculation time. The hypergeometric test (test without replacement) provides an accurate answer to this question in the form of a p-value. 'When sampling X genes (test set) out of N genes (reference set graph or annotation), what is the probability that x or more of these genes belong to a functional category C shared by n of the N genes in the reference set.' ![]() The basic question answered by these tests is the following : To get a taste of the basic user interface, please take a look at the tutorial section.īiNGO currently provides two statistical tests for assessing over- or underrepresentation in a set of genes. This manual aims to explain the inner workings, and potential pitfalls, of BiNGO in more detail.
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