Bioinformatics Vol. 19 no. 13 2003
Pages 1620-1627
© 2003 Oxford University Press
The effect of replication on gene expression microarray experiments
1 Columbia Genome Center, Columbia University, 1150 St. Nicholas Avenue, New York, NY 10032, USA, 2 Department of Computer Science, Columbia University, 1214 Amsterdam Avenue, New York, NY 10027, USA and 3 Department of Genome Sciences, University of Washington, 1705 NE Pacific Street, Seattle, WA 98195, USA
Received on November 27, 2002
; revised on February 28, 2003
; accepted on March 28, 2003
Motivation: We examine the effect of replication on the detection of apparently differentially expressed genes in gene expression microarray experiments. Our analysis is based on a random sampling approach using real data sets from 16 published studies. We consider both the ability to find genes that meet particular statistical criteria as well as the stability of the results in the face of changing levels of replication.
Results: While dependent on the data source, our findings suggest that stable results are typically not obtained until at least five biological replicates have been used. Conversely, for most studies, 10–15 replicates yield results that are quite stable, and there is less improvement in stability as the number of replicates is further increased. Our methods will be of use in evaluating existing data sets and in helping to design new studies.
Supplementary information: http://microarray.cpmc.columbia.edu/pavlidis/pub/gxrep
Contact: pp175{at}columbia.edu
* To whom correspondence should be addressed.
Formerly William Noble Grundy: see www.gs.washington.edu/~noble/name-change.html
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  I. Dozmorov and I. Lefkovits Internal standard-based analysis of microarray data. Part 1: analysis of differential gene expressions Nucleic Acids Res., October 1, 2009; 37(19): 6323 - 6339. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 L. L. Elo, J. Hiissa, J. Tuimala, A. Kallio, E. Korpelainen, and T. Aittokallio Optimized detection of differential expression in global profiling experiments: case studies in clinical transcriptomic and quantitative proteomic datasets Brief Bioinform, September 1, 2009; 10(5): 547 - 555. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A.-L. Boulesteix and M. Slawski Stability and aggregation of ranked gene lists Brief Bioinform, September 1, 2009; 10(5): 556 - 568. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S.S. Brizzolara, J. Killeen, and J. Urschitz Gene expression profile in pelvic organ prolapse Mol. Hum. Reprod., January 1, 2009; 15(1): 59 - 67. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Zhang, C. Yao, Z. Guo, J. Zou, L. Zhang, H. Xiao, D. Wang, D. Yang, X. Gong, J. Zhu, et al. Apparently low reproducibility of true differential expression discoveries in microarray studies Bioinformatics, September 15, 2008; 24(18): 2057 - 2063. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. Reiner-Benaim, D. Yekutieli, N. E. Letwin, G. I. Elmer, N. H. Lee, N. Kafkafi, and Y. Benjamini Associating quantitative behavioral traits with gene expression in the brain: searching for diamonds in the hay Bioinformatics, September 1, 2007; 23(17): 2239 - 2246. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. J. Moehring, K. C. Teeter, and M. A. F. Noor Genome-Wide Patterns of Expression in Drosophila Pure Species and Hybrid Males. II. Examination of Multiple-Species Hybridizations, Platforms, and Life Cycle Stages Mol. Biol. Evol., January 1, 2007; 24(1): 137 - 145. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. K. Ng, G. J. McLachlan, K. Wang, L. Ben-Tovim Jones, and S.-W. Ng A Mixture model with random-effects components for clustering correlated gene-expression profiles Bioinformatics, July 15, 2006; 22(14): 1745 - 1752. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 L. Tian, S. A. Greenberg, S. W. Kong, J. Altschuler, I. S. Kohane, and P. J. Park Discovering statistically significant pathways in expression profiling studies PNAS, September 20, 2005; 102(38): 13544 - 13549. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 G. Colombo, S. Gatti, F. Turcatti, A. Sordi, L. R. Fassati, F. Bonino, J. M. Lipton, and A. Catania Gene Expression Profiling Reveals Multiple Protective Influences of the Peptide {alpha}-Melanocyte-Stimulating Hormone in Experimental Heart Transplantation J. Immunol., September 1, 2005; 175(5): 3391 - 3401. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. Pawitan, S. Michiels, S. Koscielny, A. Gusnanto, and A. Ploner False discovery rate, sensitivity and sample size for microarray studies Bioinformatics, July 1, 2005; 21(13): 3017 - 3024. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. M. Ozyildirim, G. J. Wistow, J. Gao, J. Wang, D. P. Dickinson, H. F. Frierson Jr, and G. W. Laurie The Lacrimal Gland Transcriptome Is an Unusually Rich Source of Rare and Poorly Characterized Gene Transcripts Invest. Ophthalmol. Vis. Sci., May 1, 2005; 46(5): 1572 - 1580. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
N. Le Meur, G. Lamirault, A. Bihouee, M. Steenman, H. Bedrine-Ferran, R. Teusan, G. Ramstein, and J. J. Leger A dynamic, web-accessible resource to process raw microarray scan data into consolidated gene expression values: importance of replication Nucleic Acids Res., October 8, 2004; 32(18): 5349 - 5358. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Zareparsi, A. Hero, D. J. Zack, R. W. Williams, and A. Swaroop Seeing the Unseen: Microarray-Based Gene Expression Profiling in Vision Invest. Ophthalmol. Vis. Sci., August 1, 2004; 45(8): 2457 - 2462. [Full Text] [PDF]
|
|