Assessment of differential gene expression in human peripheral nerve injury

Yuanyuan Xiao^*¹ , Mark R Segal^*² , Douglas Rabert³ , Andrew H Ahn⁴ , Praveen Anand⁵ , Lakshmi Sangameswaran⁶ , Donglei Hu¹ and C Anthony Hunt¹

¹Department of Biopharmaceutical Sciences, University of California, San Francisco, San Francisco, CA 94143, USA

²Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94143, USA

³Neurobiology Unit, Roche Bioscience, Palo Alto, CA 94304, USA

⁴Department of Anatomy and Neurology, University of California, San Francisco, San Francisco, CA 94143, USA

⁵Peripheral Neuropathy Unit, Division of Neuroscience and Psychological Medicine, Imperial College of Science, Technology and Medicine, Hammersmith Hospital, London W12 ONN, UK

⁶Pherin Pharmaceuticals, Mountain View, CA 94043

author email corresponding author email* Contributed equally

BMC Genomics 2002, 3:28doi:10.1186/1471-2164-3-28


Published:	27 September 2002

Abstract

Background

Microarray technology is a powerful methodology for identifying differentially expressed genes. However, when thousands of genes in a microarray data set are evaluated simultaneously by fold changes and significance tests, the probability of detecting false positives rises sharply. In this first microarray study of brachial plexus injury, we applied and compared the performance of two recently proposed algorithms for tackling this multiple testing problem, Significance Analysis of Microarrays (SAM) and Westfall and Young step down adjusted p values, as well as t-statistics and Welch statistics, in specifying differential gene expression under different biological states.

Results

Using SAM based on t statistics, we identified 73 significant genes, which fall into different functional categories, such as cytokines / neurotrophin, myelin function and signal transduction. Interestingly, all but one gene were down-regulated in the patients. Using Welch statistics in conjunction with SAM, we identified an additional set of up-regulated genes, several of which are engaged in transcription and translation regulation. In contrast, the Westfall and Young algorithm identified only one gene using a conventional significance level of 0.05.

Conclusion

In coping with multiple testing problems, Family-wise type I error rate (FWER) and false discovery rate (FDR) are different expressions of Type I error rates. The Westfall and Young algorithm controls FWER. In the context of this microarray study, it is, seemingly, too conservative. In contrast, SAM, by controlling FDR, provides a promising alternative. In this instance, genes selected by SAM were shown to be biologically meaningful.