Multidimensional local false discovery rate for microarray studies

Motivation: The false discovery rate (fdr) is a key tool for statistical
assessment of differential expression (DE) in microarray studies.
Overall control of the fdr alone, however, is not sufficient to address
the problem of genes with small variance, which generally suffer
from a disproportionally high rate of false positives. It is desirable to
have an fdr-controlling procedure that automatically accounts for gene
variability.
Methods: We generalize the local fdr as a function of multiple statistics,
combining a common test statistic for assessing DE with its standard
error information. We use a non-parametric mixture model for DE and
non-DE genes to describe the observed multi-dimensional statistics,
and estimate the distribution for non-DE genes via the permutation
method. We demonstrate this fdr2d approach for simulated and real
microarray data.
Results: The fdr2d allows objective assessment of DE as a function
of gene variability. We also show that the fdr2d performs better than
commonly used modified test statistics.
Availability: An R-package OCplus containing functions for comput-
ing fdr2d( ) and other operating characteristics of microarray data is
available at http://www.meb.ki.se/~yudpaw