Skip to main content
eScholarship
Open Access Publications from the University of California

Thresholding rules for recovering a sparse signal from microarray experiments

  • Author(s): Sabatti, Chiara
  • Karsten, Stanislav L.
  • Geschwind, Daniel
  • et al.
Abstract

We consider array experiments that compare expression levels of a high number of genes in two cell lines with few repetitions and with no subject effect. We develop a statistical model that illustrates under which assumptions thresholding is optimal in the analysis of such microarray data. The results of our model explain the success of the empirical rule of 2-fold change. We illustrate a thresholding procedure that is adaptive to the noise level of the experiment, the amount of genes analyzed, and the amount of genes that truly change expression level. This procedure, in a world of perfect knowledge on noise distribution, would allow reconstruction of a sparse signal, minimizing the false discovery rate. Given the amount of information actually available, the thresholding rule described provides a reasonable estimator for the change in expression of any gene in two compared cell-lines.

Main Content
Current View