gms | German Medical Science

63. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

02. - 06.09.2018, Osnabrück

Network meta-analysis as an alternative to data merging in molecular high-throughput experiments

Meeting Abstract

Search Medline for

  • Klaus Jung - Stiftung Tierärztliche Hochschule Hannover, Hannover, Deutschland

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 63. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Osnabrück, 02.-06.09.2018. Düsseldorf: German Medical Science GMS Publishing House; 2018. DocAbstr. 222

doi: 10.3205/18gmds088, urn:nbn:de:0183-18gmds0884

Published: August 27, 2018

© 2018 Jung.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at



Introduction: Network meta-analysis has been widely used in clinical research to combine the results from clinical trials and to make direct and indirect inferences between study groups. So far, network meta-analysis was not considered for making indirect inferences from multiple high-throughput gene expression data sets. We demonstrate the usability of network meta-analysis for indirect group comparisons when analyzing multiple transcriptome expression experiments. While data merging in transcriptomics become difficult when the data has been taken by different technologies (e.g. microarrays and RNA-seq), network meta-analysis uses the results from each individual study.

Methods: We employ the functionality implemented in the R-package ‘netmeta’ [1] and apply it to data retrieved from public repositories such as Gene Expression Omnibus and ArrayExpress. Specifically, we show in an example of transcriptome expression data from infection research, how meta-analysis can help to make indirect group comparisons. The uninfected control group from each individual experiments acts as the joint node that connects the experiments in the network. In addition, we study the correlation between the analysis results from merged gene expression data and the results of network meta-analysis within a simulation study.

Results: The simulation study as well as the real world data example from infection research show that the results of network meta-analysis are highly correlated to the results of merged data sets. Furthermore, network meta-analysis seems to be superior to data merging when the number of studies becomes large. Results of data merging and network meta-analysis are compared in terms of p-values and fold changes.

Discussion: Making direct and indirect inferences in transcriptome expression studies can be done by merging the individual data sets. This becomes, however, difficult, when the there are strong batch effects between the studies [2] or when the data was measured by different technologies. Network meta-analysis can then be a proper alternative to data merging.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.


Rücker G. Network meta-analysis, electrical networks and graph theory. Research Synthesis Methods. 2012;3:312–24.
Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8(1):118-127.