title: Treating expression levels of different genes as a sample in microarray data analysis: is it worth a risk?
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.
authors.forename: Lev
authors.initials: L
authors.lastname: Klebanov
authors.forename: Andrei
authors.initials: A
authors.lastname: Yakovlev
mesh_terms: D020869:Gene Expression Profiling / Q000379:methods*; D016011:Normal Distribution; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012306:Risk; D013269:Stochastic Processes