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.
Bibtex:
@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