Abstract
A common procedure for estimating the number of genes that are differentially expressed (DE) in two experiments involves two steps. In the first step, data from the two experiments are separately analyzed to produce a list of genes declared to be DE in each experiment. Usually, each list is produced using a method that attempts to control the false discovery rate (FDR) in each experiment at some desired level α. In the second step, the number of genes common to both lists is used as an estimate of the number of genes DE in both experiments. A problem with this approach is that the resulting estimates can vary greatly with α, and the value of α that produces the best estimate for any given pair of experiments is difficult to predict. We propose a method that uses the p-values from both experiments simultaneously to produce one estimate—which does not depend on FDR level α—for the number of genes that are DE in both experiments. We use two simulation studies (one involving independent, normally distributed data and one involving microarray data) to compare the performances of our proposed method, the commonly used method, and another method proposed in literature to test for consistency of replicate experiments. The results of the simulation studies demonstrate the advantages of our approach. We conclude the article by estimating the number of genes that are DE in both of two experiments involving gene expressions in maize leaves.
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Orr, M., Liu, P. & Nettleton, D. Estimating the Number of Genes That Are Differentially Expressed in Both of Two Independent Experiments. JABES 17, 583–600 (2012). https://doi.org/10.1007/s13253-012-0108-8
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DOI: https://doi.org/10.1007/s13253-012-0108-8