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Bioinformatics Vol. 18 no. 4 2002
Pages 576-584
© 2002 Oxford University Press

Making sense of microarray data distributions

David C. Hoyle 1,*, Magnus Rattray 2, Ray Jupp 3 and Andrew Brass 1,2

1 School of Biological Sciences, University of Manchester, Stopford Building, Oxford Rd, Manchester M13 9PT, UK
2 Department of Computer Science, University of Manchester, Kilburn Building, Oxford Rd, Manchester M13 9PL, UK
3 Aventis Pharmaceuticals, 4041 Route 202-206, PO Box 6800, Bridgewater, NJ 08807, USA

Received on September 5, 2001 ; revised on October 31, 2001 ; accepted on November 16, 2001

Motivation: Typical analysis of microarray data has focusedon 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.

Contact: david.c.hoyle{at}man.ac.uk

* To whom correspondence should be addressed.


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