Abstract
With the proliferation of statistical algorithms developed for analyzing microarray data, high throughput molecular biology has shifted focus to highly numerical data exploration. We are increasingly aware that, although appealing, complicated statistical algorithms cannot remedy all discordance in microarray data, if such exist. In this chapter we explain how significant biological insight can be obtained from a carefully designed microarray experiment where intuition can often replace the need for statistics. We discuss analysis of gene expression data in the context of the FDA spearheaded MicroArray Quality Control project, a comprehensive public effort with its first round of results published in Nature Biotechnology in September 2006. We base our analysis on an understanding of commercial probe-design philosophies and comprehensive probe mapping. With a concrete example, we illustrate the rich biology often overlooked in microarray research and we discuss the merits of cross-platform comparison in clinical setting.
Finally, I would like to thank Josh Epstein and Peggy Brenner for critical reading of this chapter and their comments.
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Herman, D. (2008). Keep it simple: microarray cross-platform comparison without statistics. In: Bosio, A., Gerstmayer, B. (eds) Microarrays in Inflammation. Progress in Inflammation Research. Birkhäuser Basel. https://doi.org/10.1007/978-3-7643-8334-3_15
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DOI: https://doi.org/10.1007/978-3-7643-8334-3_15
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