"Lu, T; Costello, CM; Croucher, PJP; Hasler, R; Deuschl, GN; Schreiber, S "Can Zipf's law be adapted to normalize microarrays?" BMC BIOINFORMATICS 6. FEB 23 2005. p.NIL_1-NIL_13

[Eugene Garfield]

Dr. Stefan Schreiber : s.schreiber at mucosa.de


FULL TEXT IS AVAILABLE AT : http://www.biomedcentral.com/1471-2105/6/37

TITLE:          Can Zipf's law be adapted to normalize microarrays? -
                art. no. 37 (Article, English)
AUTHOR:         Lu, T; Costello, CM; Croucher, PJP; Hasler, R; Deuschl,
                GN; Schreiber, S
SOURCE:         BMC BIOINFORMATICS 6. FEB 23 2005. p.NIL_1-NIL_13 BIOMED
                CENTRAL LTD, LONDON

ABSTRACT:
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.

Conclusion: 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).

AUTHOR ADDRESS: S Schreiber, Univ Kiel, Dept Med, D-24098 Kiel, Germany