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Can the false‐discovery rate be misleading?
Author(s) -
Barboza Rodrigo,
Cociorva Daniel,
Xu Tao,
Barbosa Valmir C.,
Perales Jonas,
Valente Richard H.,
França Felipe M. G.,
Yates John R.,
Carvalho Paulo C.
Publication year - 2011
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201100297
Subject(s) - false discovery rate , decoy , overfitting , computer science , identification (biology) , shotgun , false positive rate , reliability (semiconductor) , data mining , artificial intelligence , biology , biochemistry , power (physics) , botany , receptor , physics , quantum mechanics , gene , artificial neural network
The decoy‐database approach is currently the gold standard for assessing the confidence of identifications in shotgun proteomic experiments. Here, we demonstrate that what might appear to be a good result under the decoy‐database approach for a given false‐discovery rate could be, in fact, the product of overfitting. This problem has been overlooked until now and could lead to obtaining boosted identification numbers whose reliability does not correspond to the expected false‐discovery rate. To overcome this, we are introducing a modified version of the method, termed a semi‐labeled decoy approach, which enables the statistical determination of an overfitted result.