Rank-invariant resampling based estimation of false discovery rate for analysis of small sample microarray data

Background: The evaluation of statistical significance has become a critical process in identifying differentially expressed genes in microarray studies. Classical p-value adjustment methods for multiple comparisons such as family-wise error rate (FWER) have been found to be too conservative in analyzing large-screening microarray data, and the False Discovery Rate (FDR), the expected proportion of false positives among all positives, has been recently suggested as an alternative for controlling false positives. Several statistical approaches have been used to estimate and control FDR, but these may not provide reliable FDR estimation when applied to microarray data sets with a small number of replicates. Results: We propose a rank-invariant resampling (RIR) based approach to FDR evaluation. Our proposed method generates a biologically relevant null distribution, which maintains similar variability to observed microarray data. We compare the performance of our RIR-based FDR estimation...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Nitin Jain, HyungJun Cho, Michael O'Connell, Jae K. Lee
• Keywords: BMCBI 2005, False Positive, FDR Estimation, Microarray Data