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Most Downloaded Articles
- A General Framework for Weighted Gene Co-Expression Network Analysis by Zhang, Bin and Horvath, Steve
- Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments by Smyth, Gordon K
- Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates by Lund, Steven P./ Nettleton, Dan/ McCarthy, Davis J. and Smyth, Gordon K.
- Adjusting for Spurious Gene-by-Environment Interaction Using Case-Parent Triads by Shin, Ji-Hyung/ Infante-Rivard, Claire/ Graham, Jinko and McNeney, Brad
- A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics by Schäfer, Juliane and Strimmer, Korbinian
Treating Expression Levels of Different Genes as a Sample in Microarray Data Analysis: Is it Worth a Risk?
1Department of Probability and Statistics, Charles University
1University of Rochester, Rochester, NY
Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 5, Issue 1, Pages –, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1185, March 2006
- Published Online:
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.