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

Nitin Jain¹ , HyungJun Cho¹ , Michael O'Connell² and Jae K Lee¹

¹Division of Biostatistics and Epidemiology, Department of Health Evaluation Sciences, University of Virginia School of Medicine, Hospital West Complex, Room. 3181, P.O. Box 800717, Charlottesville, VA 22908-0717, USA

²Insightful Corporation, 2505 Meridian Parkway Suite 175, Durham, NC 27713, USA

author email corresponding author email

BMC Bioinformatics 2005, 6:187doi:10.1186/1471-2105-6-187


Published:	22 July 2005

Abstract

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 with that of four other popular methods. Our approach outperforms the other methods both in simulated and real microarray data.

Conclusion

We found that the SAM's random shuffling and SPLOSH approaches were liberal and the other two theoretical methods were too conservative while our RIR approach provided more accurate FDR estimation than the other approaches.