Can Zipf's law be adapted to normalize microarrays?

Background: Normalization is the process of removing non-biological sources of variation between array experiments. Recent investigations of data in gene expression databases for varying organisms and tissues have shown that the majority of expressed genes exhibit a power-law distribution with an exponent close to -1 (i.e. obey Zipf's law). Based on the observation that our single channel and two channel microarray data sets also followed a power-law distribution, we were motivated to develop a normalization method based on this law, and examine how it compares with existing published techniques. A computationally simple and intuitively appealing technique based on this observation is presented. Results: Using pairwise comparisons using MA plots (log ratio vs. log intensity), we compared this novel method to previously published normalization techniques, namely global normalization to the mean, the quantile method, and a variation on the loess normalization method designed specif...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Timothy Lu, Christine M. Costello, Peter J. P. Croucher, Robert Häsler, Günther Deuschl, Stefan Schreiber
• Keywords: BMCBI 2005, Law Normalization, Normalization Method, Quantile Method