Making sense of microarray data distributions.
BACKGROUND
Typical analysis of microarray data has focused on spot by spot comparisons within a single organism. Less analysis has been done on the comparison of the entire distribution of spot intensities between experiments and between organisms.


RESULTS
Here we show that mRNA transcription data from a wide range of organisms and measured with a range of experimental platforms show close agreement with Benford's law (Benford, PROC: Am. Phil. Soc., 78, 551-572, 1938) and Zipf's law (Zipf, The Psycho-biology of Language: an Introduction to Dynamic Philology, 1936 and Human Behaviour and the Principle of Least Effort, 1949). The distribution of the bulk of microarray spot intensities is well approximated by a log-normal with the tail of the distribution being closer to power law. The variance, sigma(2), of log spot intensity shows a positive correlation with genome size (in terms of number of genes) and is therefore relatively fixed within some range for a given organism. The measured value of sigma(2) can be significantly smaller than the expected value if the mRNA is extracted from a sample of mixed cell types. Our research demonstrates that useful biological findings may result from analyzing microarray data at the level of entire intensity distributions.
Hoyle
David C
DC
School of Biological Sciences, University of Manchester, Stopford Building, Oxford Rd, Manchester M13 9PT, UK. david.c.hoyle@man.ac.uk
Rattray
Magnus
M
Jupp
Ray
R
Brass
Andrew
A
D000465:Algorithms; D000704:Analysis of Variance; D000818:Animals; D016009:Chi-Square Distribution; D030541:Databases, Genetic; D016678:Genome; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000295:instrumentation / Q000379:methods* / Q000706:statistics & numerical data*; D010363:Pattern Recognition, Automated; D012333:RNA, Messenger / Q000235:genetics; D012680:Sensitivity and Specificity
D012333:RNA, Messenger