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
Lev B Klebanov, Andrei Y Yakovlev

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.

Citations

May 31, 2011·Cancer Prevention Research·Ilana Belitskaya-LévyAlan A Arslan
Oct 17, 2007·BMC Bioinformatics·Anders SjögrenOlle Nerman
Apr 13, 2007·Biology Direct·Lev Klebanov, Andrei Yakovlev
Oct 3, 2007·PLoS Genetics·Jeffrey T Leek, John D Storey
Jun 29, 2007·Bioinformation·Lev Klebanov, Andrei Yakovlev
Feb 18, 2010·Computational Statistics & Data Analysis·Alexander GordonAndrei Yakovlev
Sep 14, 2006·Physiological Genomics·Tapan S MehtaDavid B Allison

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