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Hoyle, DC, Rattray, M, Jupp, R and Brass, A (2002)

Making sense of microarray data distributions

Bioinformatics 18(4), pp. 576-584.

ISSN/ISBN: 1367-4803 DOI: 10.1093/bioinformatics/18.4.576

Abstract: Motivation: 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 and Zipf’s law. 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, σ2, 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 σ2 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.

@article{, title={Making sense of microarray data distributions}, author={Hoyle, David C and Rattray, Magnus and Jupp, Ray and Brass, Andrew}, journal={Bioinformatics}, volume={18}, number={4}, pages={576--584}, year={2002}, publisher={Oxford Univ Press}, ISSN={1367-4803}, DOI={10.1093/bioinformatics/18.4.576}, }

Reference Type: Journal Article

Subject Area(s): Biology, Medical Sciences, Statistics