Premium
Application of meta‐analysis methods for identifying proteomic expression level differences
Author(s) -
Amess Bob,
Kluge Wolfgang,
Schwarz Emanuel,
Haenisch Frieder,
Alsaif Murtada,
Yolken Robert H.,
Leweke F. Markus,
Guest Paul C.,
Bahn Sabine
Publication year - 2013
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.201300034
Subject(s) - ranking (information retrieval) , nonparametric statistics , euclidean distance , false positive paradox , expression (computer science) , fold change , rank (graph theory) , computer science , statistics , data mining , value (mathematics) , identification (biology) , meta analysis , mathematics , computational biology , artificial intelligence , biology , gene expression , medicine , gene , biochemistry , combinatorics , programming language , botany
We present new statistical approaches for identification of proteins with expression levels that are significantly changed when applying meta‐analysis to two or more independent experiments. We showed that the E uclidean distance measure has reduced risk of false positives compared to the rank product method. Our Ψ ‐ranking method has advantages over the traditional fold‐change approach by incorporating both the fold‐change direction as well as the p ‐value. In addition, the second novel method, Π ‐ranking, considers the ratio of the fold‐change and thus integrates all three parameters. We further improved the latter by introducing our third technique, Σ ‐ranking, which combines all three parameters in a balanced nonparametric approach.