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 specifically for boutique microarrays. Results indicated that, for single channel microarrays, the quantile method was superior with regard to eliminating intensity-dependent effects (banana curves), but Zipf's law normalization does minimize this effect by rotating the data distribution such that the maximal number of data points lie on the zero of the log ratio axis. For two channel boutique microarrays, the Zipf's law normalizations performed as well as, or better than existing techniques.


CONCLUSIONS
Zipf's law normalization is a useful tool where the Quantile method cannot be applied, as is the case with microarrays containing functionally specific gene sets (boutique arrays).
Lu
Tim
T
Department of Medicine, Christian-Albrechts-University, Kiel, Germany. t.lu@mucosa.de <t.lu@mucosa.de>
Costello
Christine M
CM
Croucher
Peter J P
PJ
Häsler
Robert
R
Deuschl
Günther
G
Schreiber
Stefan
S
D000465:Algorithms; D000704:Analysis of Variance; D000818:Animals; D002138:Calibration; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D020224:Expressed Sequence Tags; D015870:Gene Expression; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012313:RNA / Q000737:chemistry; D051381:Rats; D012044:Regression Analysis; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA
D012313:RNA
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