An improved procedure for gene selection from microarray experiments using false discovery rate criterion |
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Authors: | James J Yang Mark CK Yang |
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Institution: | (1) Biostatistics and Research Epidemiology, Henry Ford Health Sciences Center, Detroit, Michigan, USA;(2) Department of Statistics, University of Florida, Gainesville, Florida, USA |
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Abstract: | Background A large number of genes usually show differential expressions in a microarray experiment with two types of tissues, and the
p-values of a proper statistical test are often used to quantify the significance of these differences. The genes with small
p-values are then picked as the genes responsible for the differences in the tissue RNA expressions. One key question is what
should be the threshold to consider the p-values small. There is always a trade off between this threshold and the rate of false claims. Recent statistical literature
shows that the false discovery rate (FDR) criterion is a powerful and reasonable criterion to pick those genes with differential
expression. Moreover, the power of detection can be increased by knowing the number of non-differential expression genes.
While this number is unknown in practice, there are methods to estimate it from data. The purpose of this paper is to present
a new method of estimating this number and use it for the FDR procedure construction. |
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Keywords: | |
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