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This article is part of the supplement: Selected Proceedings of the 2010 AMIA Summit on Translational Bioinformatics

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Comparison of multiplex meta analysis techniques for understanding the acute rejection of solid organ transplants

Alexander A Morgan12*, Purvesh Khatri123, Richard Hayden Jones12, Minnie M Sarwal13 and Atul J Butte123

  • * Corresponding author: Alexander A Morgan

Author Affiliations

1 Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA

2 Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA 94305, USA

3 Lucile Packard Children’s Hospital, 725 Welch Road, Palo Alto, CA 94304, USA

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BMC Bioinformatics 2010, 11(Suppl 9):S6  doi:10.1186/1471-2105-11-S9-S6

Published: 28 October 2010



Combining the results of studies using highly parallelized measurements of gene expression such as microarrays and RNAseq offer unique challenges in meta analysis. Motivated by a need for a deeper understanding of organ transplant rejection, we combine the data from five separate studies to compare acute rejection versus stability after solid organ transplantation, and use this data to examine approaches to multiplex meta analysis.


We demonstrate that a commonly used parametric effect size estimate approach and a commonly used non-parametric method give very different results in prioritizing genes. The parametric method providing a meta effect estimate was superior at ranking genes based on our gold-standard of identifying immune response genes in the transplant rejection datasets.


Different methods of multiplex analysis can give substantially different results. The method which is best for any given application will likely depend on the particular domain, and it remains for future work to see if any one method is consistently better at identifying important biological signal across gene expression experiments.