Using 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.
Researchers at the University of Veterinary Medicine Hannover 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, the researchers also demonstrate the practicability of network meta-analysis on a real-world data example from neuroinfection research.
Schemes of study networks
Networks were either simulated or represent the infection example. Top: two studies are connected by a similar control group. (This scenario is evaluated in simulations no. 1a and no. 1b and by the infection example.). Bottom: the edge representing the comparison between treatment A and control is supported by three independent studies (This scenario is evaluated in simulation no. 2.)
Network 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.