Treating Expression Levels of Different Genes as a Sample in Microarray Data Analysis: Is it Worth a Risk?

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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.

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