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Activity Number: 143
Type: Contributed
Date/Time: Monday, July 30, 2007 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #308552
Title: RDCurve: A Nonparametric Method To Evaluate the Stability of Selection Procedures
Author(s): Xin Lu*+
Companies: University of California, San Diego
Address: 9500 Gilman Dr, La Jolla, CA, 92093,
Keywords: gene selection ; microarray ; re-discovery rate
Abstract:

We proposed a nonparametric Re-Discovery Curve (RDCurve) method, to estimate the probability of re-discovery of gene selection from microarray data sets. Given a selection procedure and a data set, the RDCurve method applies the selection procedure repeatedly to bootstrapped data, select a subset of important genes, and then estimate the expected frequency of re-discovery of the selected subset of genes. We also proposed a permutation method to estimate the confidence band of RDCurve under Null hypothesis to justify the significance of the RDCurve. The method we proposed is a complement to traditional FDR method. It is nonparametric and model independent. With the RDCurve method, we can also estimate the signal-noise ratio of a give data set, compare the performance of two selection procedures in term of them expected rediscovery rate, or select the number of genes to be reported.

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Revised September, 2007