Can Zipf's law be adapted to normalize microarrays?

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),

SUBMITTER: Lu T 

PROVIDER: S-EPMC555536 | biostudies-literature | 2005 Feb

REPOSITORIES: biostudies-literature