Application of meta‐analysis methods for identifying proteomic expression level differences

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.