Network meta-analysis correlates with analysis of merged independent transcriptome expression data

BMC Bioinformatics

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[1] 0000 0001 0126 6191, grid.412970.9, Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Bünteweg 17p, 30559, Hannover, Germany;0000 0001 0126 6191, grid.412970.9, Research Center for Emerging Infections and Zoonoses, University of Veterinary Medicine Hannover, Bünteweg 17p, 30559, Hannover, Germany;

关键词: Fold change;    Gene expression;    Meta-analysis;    Network meta-analysis;    Research synthesis;   

DOI  :  10.1186/s12859-019-2705-9

来源: publisher

【 摘 要 】

BackgroundUsing meta-analysis, high-dimensional transcriptome expression data from public repositories can be merged to make group comparisons that have not been considered in the original studies. Merging of high-dimensional expression data can, however, implicate batch effects that are sometimes difficult to be removed. Removing batch effects becomes even more difficult when expression data was taken using different technologies in the individual studies (e.g. merging of microarray and RNA-seq data). Network meta-analysis has so far not been considered to make indirect comparisons in transcriptome expression data, when data merging appears to yield biased results.ResultsWe demonstrate in a simulation study that the results from analyzing merged data sets and the results from network meta-analysis are highly correlated in simple study networks. In the case that an edge in the network is supported by multiple independent studies, network meta-analysis produces fold changes that are closer to the simulated ones than those obtained from analyzing merged data sets. Finally, we also demonstrate the practicability of network meta-analysis on a real-world data example from neuroinfection research.ConclusionsNetwork meta-analysis is a useful means to make new inferences when combining multiple independent studies of molecular, high-throughput expression data. This method is especially advantageous when batch effects between studies are hard to get removed.

【 授权许可】

CC BY