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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Christine Winter, Robin Kosch, Martin Ludlow, Albert D. M. E. Osterhaus, Klaus Jung

ABSTRACT

BACKGROUND: 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. RESULTS: We 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. CONCLUSIONS: 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. More... »

PAGES

144

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12859-019-2705-9

DOI

http://dx.doi.org/10.1186/s12859-019-2705-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112775275

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30876387


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