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Multiple hypothesis testing in genomics
Author(s) -
Goeman Jelle J.,
Solari Aldo
Publication year - 2014
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6082
Subject(s) - false discovery rate , computer science , genomics , multiple comparisons problem , perspective (graphical) , statistical hypothesis testing , data mining , machine learning , artificial intelligence , statistics , genome , biology , mathematics , genetics , gene
This paper presents an overview of the current state of the art in multiple testing in genomics data from a user's perspective. We describe methods for familywise error control, false discovery rate control and false discovery proportion estimation and confidence, both conceptually and practically, and explain when to use which type of error rate. We elaborate on the assumptions underlying the methods and discuss pitfalls in the interpretation of results. In our discussion, we take into account the exploratory nature of genomics experiments, looking at selection of genes before or after testing, and at the role of validation experiments. Copyright © 2014 John Wiley & Sons, Ltd.