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Network meta-analysis correlates with analysis of merged independent transcriptome expression data
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6420731/ https://www.ncbi.nlm.nih.gov/pubmed/30876387 http://dx.doi.org/10.1186/s12859-019-2705-9 |
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author | Winter, Christine Kosch, Robin Ludlow, Martin Osterhaus, Albert D. M. E. Jung, Klaus |
author_facet | Winter, Christine Kosch, Robin Ludlow, Martin Osterhaus, Albert D. M. E. Jung, Klaus |
author_sort | Winter, Christine |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2705-9) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6420731 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-64207312019-03-28 Network meta-analysis correlates with analysis of merged independent transcriptome expression data Winter, Christine Kosch, Robin Ludlow, Martin Osterhaus, Albert D. M. E. Jung, Klaus BMC Bioinformatics Methodology Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2705-9) contains supplementary material, which is available to authorized users. BioMed Central 2019-03-15 /pmc/articles/PMC6420731/ /pubmed/30876387 http://dx.doi.org/10.1186/s12859-019-2705-9 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Winter, Christine Kosch, Robin Ludlow, Martin Osterhaus, Albert D. M. E. Jung, Klaus Network meta-analysis correlates with analysis of merged independent transcriptome expression data |
title | Network meta-analysis correlates with analysis of merged independent transcriptome expression data |
title_full | Network meta-analysis correlates with analysis of merged independent transcriptome expression data |
title_fullStr | Network meta-analysis correlates with analysis of merged independent transcriptome expression data |
title_full_unstemmed | Network meta-analysis correlates with analysis of merged independent transcriptome expression data |
title_short | Network meta-analysis correlates with analysis of merged independent transcriptome expression data |
title_sort | network meta-analysis correlates with analysis of merged independent transcriptome expression data |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6420731/ https://www.ncbi.nlm.nih.gov/pubmed/30876387 http://dx.doi.org/10.1186/s12859-019-2705-9 |
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