TY - JOUR
T1 - Estimating the false discovery rate using nonparametric deconvolution
AU - van de Wiel, M.A.
AU - Kim, K.I.
PY - 2007
Y1 - 2007
N2 - Given a set of microarray data, the problem is to detect differentially expressed genes, using a false discovery rate (FDR) criterion. As opposed to common procedures in the literature, we do not base the selection criterion on statistical significance only, but also on the effect size. Therefore, we select only those genes that are significantly more differentially expressed than some f-fold (e.g., f = 2). This corresponds to use of an interval null domain for the effect size. Based on a simple error model, we discuss a naive estimator for the FDR, interpreted as the probability that the parameter of interest lies in the null-domain (e.g., μ < log
AB - Given a set of microarray data, the problem is to detect differentially expressed genes, using a false discovery rate (FDR) criterion. As opposed to common procedures in the literature, we do not base the selection criterion on statistical significance only, but also on the effect size. Therefore, we select only those genes that are significantly more differentially expressed than some f-fold (e.g., f = 2). This corresponds to use of an interval null domain for the effect size. Based on a simple error model, we discuss a naive estimator for the FDR, interpreted as the probability that the parameter of interest lies in the null-domain (e.g., μ < log
U2 - 10.1111/j.1541-0420.2006.00736.x
DO - 10.1111/j.1541-0420.2006.00736.x
M3 - Article
VL - 63
SP - 806
EP - 815
JO - Biometrics
JF - Biometrics
SN - 0006-341X
IS - 3
ER -