Abstract

One of the prevailing ideas in the literature on microarray data analysis is to pool the expression measures across genes and treat them as a sample drawn from some distribution. Several universal laws were proposed to analytically describe this distribution. This idea raises a number of concerns. The expression levels of genes are not identically distributed random variables so that treating them as a sample amounts to sampling from a mixture of equally weighted distributions, each being associated with a different gene. The expression levels of different genes are heavily dependent random variables so that the law of large numbers and statistical goodness-of-fit tests are normally inapplicable to this kind of data. This dependence represents a very serious pitfall in microarray data analysis.

Submitted: October 12, 2005 · Accepted: March 17, 2006 · Published: March 24, 2006

Recommended Citation

Klebanov, Lev and Yakovlev, Andrei (2006) "Treating Expression Levels of Different Genes as a Sample in Microarray Data Analysis: Is it Worth a Risk?," Statistical Applications in Genetics and Molecular Biology: Vol. 5 : Iss. 1, Article 9.
DOI: 10.2202/1544-6115.1185
Available at: http://www.bepress.com/sagmb/vol5/iss1/art9