  Motivation: In microarray analysis, special consideration must be given to
the issues of multiple statistical tests and typically p-values are adjusted to
control family-wise error rate (FWER) or false discovery rate (FDR). FDR
metrics have been suggested for controlling false positives, however, genes
with p-values close to the threshold typically have a higher chance of being
false positives than genes with very low p-values. The local FDR (fdr) metric
gives the number of false positives in the vicinity of a test statistic. We
propose a new fdr estimator that uses windows instead of binsand define
heuristics that use the fluctuations in the estimator to determine robust
thresholds for classifying differential expression.
  Results: Our fdr approach estimates the false discovery rate within a window
of p-values. We present heuristics that derive robust fdr thresholds such that
a significant change in the fdr threshold yields a small change in the number
of rejected hypotheses. We compare these thresholds with thresholds from other
approaches using two simulated datasets and one cancer microarray dataset. In
the latter, our estimator finds two robust thresholds. Since our fdr estimator
is an extension of the FDR metric, it can be used with many FDR estimation
methods.
  Availability: An R function implementing the proposed estimator is available
at http://www.dbi.tju.edu/dbi/tools/fdr
  Contact<EMAIL_ADDRESS>  Supplementary Information: Supplementary figures and code are available at
http://www.dbi.tju.edu/dbi/tools/fdr
