Treating expression levels of different genes as a sample in microarray data analysis: is it worth a risk?

Lev Klebanov; Andrei Yakovlev

doi:10.2202/1544-6115.1185

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Treating expression levels of different genes as a sample in microarray data analysis: is it worth a risk?

Lev Klebanov·Andrei Yakovlev

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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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Gene Expression Profiling Normal Distribution Oligonucleotide Array Sequence Analysis Risk Stochastic Processes Evaluation Study Letter Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

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Lev Klebanov, Andrei Yakovlev. 2006-03-24. Treating expression levels of different genes as a sample in microarray data analysis: is it worth a risk?. https://doi.org/10.2202/1544-6115.1185

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