•   •   •   •

Download the Paper (2.3 MB, PDF file) - January 1, 2001 Tell a colleague about it. Printing Tips: Select 'print as image' in the Acrobat print dialog if you have trouble printing.

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.

SUGGESTED CITATION:
Chiara Sabatti, Stanislav L. Karsten, and Daniel Geschwind, "Thresholding rules for recovering a sparse signal from microarray experiments" (January 1, 2001). Department of Statistics, UCLA. Department of Statistics Papers. Paper 2001010103.
http://repositories.cdlib.org/uclastat/papers/2001010103