Making sense of microarray data distributions

• David C. Hoyle

• Magnus Rattray

• Ray Jupp

• Andrew Brass

Abstract

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, 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. Contact: [e-mail] * To whom correspondence should be addressed.

Journal

• Bioinformatics

  Bioinformatics 18 (4), 576-584, 2002-04-01

  Oxford University Press (OUP)

Keywords

• Computational Mathematics
• Computational Theory and Mathematics
• Computer Science Applications
• Molecular Biology
• Biochemistry
• Statistics and Probability

Details

• CRID: 1363951793645702144
• NII Article ID: 30018395635
• DOI: 10.1093/bioinformatics/18.4.576
• ISSN: 13674811
• ISSN: 13674803
• Web Site: https://academic.oup.com/bioinformatics/article-pdf/18/4/576/48850278/bioinformatics_18_4_576.pdf
• Data Source:
  • Crossref
  • CiNii Articles