TY - JOUR

T1 - Thresholding rules for recovering a sparse signal from microarray experiments

AU - Sabatti, Chiara

AU - Karsten, Stanislav L.

AU - Geschwind, Daniel H.

N1 - Funding Information:
The first author thanks Emmanuel Candes for suggesting the relevance of FDR procedures in this context; additionally she thanks organizers and participants of the IPAM session on functional genomics during the fall of 2000 for their generosity in sharing ideas. We also thank Stan Nelson and Zhun Chen of the UCLA microarray core for array fabrication. Grant support from NIHM (D.H.G.) and NIA (S.L.K. and D.H.G.) is acknowledged for microarray work.

PY - 2002

Y1 - 2002

N2 - 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 two-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.

AB - 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 two-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.

KW - False discovery rate

KW - High dimension

KW - Minimax

KW - Sparcity

KW - Thresholding

UR - http://www.scopus.com/inward/record.url?scp=0036128656&partnerID=8YFLogxK

U2 - 10.1016/S0025-5564(01)00102-X

DO - 10.1016/S0025-5564(01)00102-X

M3 - Article

C2 - 11867081

AN - SCOPUS:0036128656

VL - 176

SP - 17

EP - 34

JO - Mathematical Biosciences

JF - Mathematical Biosciences

SN - 0025-5564

IS - 1

ER -