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This article is part of the supplement: Proceedings from the Great Lakes Bioinformatics Conference 2011

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Fold change and p-value cutoffs significantly alter microarray interpretations

Mark R Dalman1*, Anthony Deeter2, Gayathri Nimishakavi2 and Zhong-Hui Duan2

Author affiliations

1 Department of Biology and program in Integrative Bioscience, University of Akron, Akron, OH, USA

2 Department of Computer Science, University of Akron, OH, USA

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Citation and License

BMC Bioinformatics 2012, 13(Suppl 2):S11  doi:10.1186/1471-2105-13-S2-S11

Published: 13 March 2012

Abstract

Background

As context is important to gene expression, so is the preprocessing of microarray to transcriptomics. Microarray data suffers from several normalization and significance problems. Arbitrary fold change (FC) cut-offs of >2 and significance p-values of <0.02 lead data collection to look only at genes which vary wildly amongst other genes. Therefore, questions arise as to whether the biology or the statistical cutoff are more important within the interpretation. In this paper, we reanalyzed a zebrafish (D. rerio) microarray data set using GeneSpring and different differential gene expression cut-offs and found the data interpretation was drastically different. Furthermore, despite the advances in microarray technology, the array captures a large portion of genes known but yet still leaving large voids in the number of genes assayed, such as leptin a pleiotropic hormone directly related to hypoxia-induced angiogenesis.

Results

The data strongly suggests that the number of differentially expressed genes is more up-regulated than down-regulated, with many genes indicating conserved signalling to previously known functions. Recapitulated data from Marques et al. (2008) was similar but surprisingly different with some genes showing unexpected signalling which may be a product of tissue (heart) or that the intended response was transient.

Conclusions

Our analyses suggest that based on the chosen statistical or fold change cut-off; microarray analysis can provide essentially more than one answer, implying data interpretation as more of an art than a science, with follow up gene expression studies a must. Furthermore, gene chip annotation and development needs to maintain pace with not only new genomes being sequenced but also novel genes that are crucial to the overall gene chips interpretation.