Public databases featuring original, raw data from "Omics" experiments enable researchers to perform meta-analyses by combining either the raw data or the summarized results of several independent studies. In proteomics, high-throughput protein expression data is measured by diverse techniques such as mass spectrometry, 2-D gel electrophoresis or protein arrays yielding data of different scales. Therefore, direct data merging can be problematic, and combining the summarized data of the individual studies can be advantageous. A special form of meta-analysis is network meta-analysis, where studies with different settings of experimental groups can be combined. However, all studies must be linked by one experimental group that has to appear in each study. Usually that is the control group. Then, a study network is formed and indirect statistical inferences can also be made between study groups that appear not in each of the studies.In this chapter, we describe the working principle of and available software for network meta-analysis. The applicability to high-throughput protein expression data is demonstrated in an example from breast cancer research. We also describe the special challenges when applying this method.
Keywords: Batch effects; Biological databases; Data merging; Data mining; Network meta-analysis; Protein expression data; Publication guidelines; Reproducibility; Research synthesis.