Bioinformatics Advance Access originally published online on April 29, 2004
Bioinformatics 2004 20(16):2534-2544; doi:10.1093/bioinformatics/bth280
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Bioinformatics vol. 20 issue 16 © Oxford University Press 2004; all rights reserved.
Interactively optimizing signal-to-noise ratios in expression profiling: project-specific algorithm selection and detection p-value weighting in Affymetrix microarrays
1 Research Center for Genetic Medicine, Children's National Medical Center and 2 Human-Computer Interaction Lab and Department of Computer Science, University of Maryland, College Park, MD 20742 USA
Received on March 23, 2004; accepted on April 16, 2004
Advance Access Publication April 29, 2004
Motivation: The most commonly utilized microarrays for mRNA profiling (Affymetrix) include ‘probe sets’ of a series of perfect match and mismatch probes (typically 22 oligonucleotides per probe set). There are an increasing number of reported ‘probe set algorithms’ that differ in their interpretation of a probe set to derive a single normalized ‘signal’ representative of expression of each mRNA. These algorithms are known to differ in accuracy and sensitivity, and optimization has been done using a small set of standardized control microarray data. We hypothesized that different mRNA profiling projects have varying sources and degrees of confounding noise, and that these should alter the choice of a specific probe set algorithm. Also, we hypothesized that use of the Microarray Suite (MAS) 5.0 probe set detection p-value as a weighting function would improve the performance of all probe set algorithms.
Results: We built an interactive visual analysis software tool (HCE2W) to test and define parameters in Affymetrix analyses that optimize the ratio of signal (desired biological variable) versus noise (confounding uncontrolled variables). Five probe set algorithms were studied with and without statistical weighting of probe sets using the MAS 5.0 probe set detection p-values. The signal-to-noise ratio optimization method was tested in two large novel microarray datasets with different levels of confounding noise, a 105 sample U133A human muscle biopsy dataset (11 groups: mutation-defined, extensive noise), and a 40 sample U74A inbred mouse lung dataset (8 groups: little noise). Performance was measured by the ability of the specific probe set algorithm, with and without detection p-value weighting, to cluster samples into the appropriate biological groups (unsupervised agglomerative clustering with F-measure values). Of the total random sampling analyses, 50% showed a highly statistically significant difference between probe set algorithms by ANOVA [F(4,10) > 14, p < 0.0001], with weighting by MAS 5.0 detection p-value showing significance in the mouse data by ANOVA [F(1,10) > 9, p < 0.013] and paired t-test [t(9) = –3.675, p = 0.005]. Probe set detection p-value weighting had the greatest positive effect on performance of dChip difference model, ProbeProfiler and RMA algorithms. Importantly, probe set algorithms did indeed perform differently depending on the specific project, most probably due to the degree of confounding noise. Our data indicate that significantly improved data analysis of mRNA profile projects can be achieved by optimizing the choice of probe set algorithm with the noise levels intrinsic to a project, with dChip difference model with MAS 5.0 detection p-value continuous weighting showing the best overall performance in both projects. Furthermore, both existing and newly developed probe set algorithms should incorporate a detection p-value weighting to improve performance.
Availability: The Hierarchical Clustering Explorer 2.0 is available at http://www.cs.umd.edu/hcil/hce/. Murine arrays (40 samples) are publicly available at the PEPR resource (http://microarray.cnmcresearch.org/pgadatatable.asp; http://pepr.cnmcresearch.org; Chen et al., 2004).
Contact: ehoffman{at}cnmcresearch.org
* To whom correspondence should be addressed.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  M-C Kastrinaki, P Sidiropoulos, S Roche, J Ringe, S Lehmann, H Kritikos, V-M Vlahava, B Delorme, G D Eliopoulos, C Jorgensen, et al. Functional, molecular and proteomic characterisation of bone marrow mesenchymal stem cells in rheumatoid arthritis Ann Rheum Dis, June 1, 2008; 67(6): 741 - 749. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. S. Moller-Levet, C. M. West, and C. J. Miller Exploiting sample variability to enhance multivariate analysis of microarray data Bioinformatics, October 15, 2007; 23(20): 2733 - 2740. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. Tian, T. Kasuga, M. S. Sachs, and N. L. Glass Transcriptional Profiling of Cross Pathway Control in Neurospora crassa and Comparative Analysis of the Gcn4 and CPC1 Regulons Eukaryot. Cell, June 1, 2007; 6(6): 1018 - 1029. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 K. L. Price, D. A. Long, N. Jina, H. Liapis, M. Hubank, A. S. Woolf, and P. J. D. Winyard Microarray interrogation of human metanephric mesenchymal cells highlights potentially important molecules in vivo Physiol Genomics, January 17, 2007; 28(2): 193 - 202. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J.-J. Park, J. R. Berggren, M. W. Hulver, J. A Houmard, and E. P. Hoffman GRB14, GPD1, and GDF8 as potential network collaborators in weight loss-induced improvements in insulin action in human skeletal muscle Physiol Genomics, October 11, 2006; 27(2): 114 - 121. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 Y. Kotliarov, M. E. Steed, N. Christopher, J. Walling, Q. Su, A. Center, J. Heiss, M. Rosenblum, T. Mikkelsen, J. C. Zenklusen, et al. High-resolution Global Genomic Survey of 178 Gliomas Reveals Novel Regions of Copy Number Alteration and Allelic Imbalances Cancer Res., October 1, 2006; 66(19): 9428 - 9436. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. E. Clark, Q. He, Z. He, K. H. Huang, E. J. Alm, X.-F. Wan, T. C. Hazen, A. P. Arkin, J. D. Wall, J.-Z. Zhou, et al. Temporal Transcriptomic Analysis as Desulfovibrio vulgaris Hildenborough Transitions into Stationary Phase during Electron Donor Depletion Appl. Envir. Microbiol., August 1, 2006; 72(8): 5578 - 5588. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Q. He, K. H. Huang, Z. He, E. J. Alm, M. W. Fields, T. C. Hazen, A. P. Arkin, J. D. Wall, and J. Zhou Energetic Consequences of Nitrite Stress in Desulfovibrio vulgaris Hildenborough, Inferred from Global Transcriptional Analysis. Appl. Envir. Microbiol., June 1, 2006; 72(6): 4370 - 4381. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. D. Knights, J. Catania, S. D. Giovanni, S. Muratoglu, R. Perez, A. Swartzbeck, A. A. Quong, X. Zhang, T. Beerman, R. G. Pestell, et al. Distinct p53 acetylation cassettes differentially influence gene-expression patterns and cell fate J. Cell Biol., May 22, 2006; 173(4): 533 - 544. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Bakay, Z. Wang, G. Melcon, L. Schiltz, J. Xuan, P. Zhao, V. Sartorelli, J. Seo, E. Pegoraro, C. Angelini, et al. Nuclear envelope dystrophies show a transcriptional fingerprint suggesting disruption of Rb-MyoD pathways in muscle regeneration Brain, April 1, 2006; 129(4): 996 - 1013. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Seo, H. Gordish-Dressman, and E. P. Hoffman An interactive power analysis tool for microarray hypothesis testing and generation Bioinformatics, April 1, 2006; 22(7): 808 - 814. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 G. Melcon, S. Kozlov, D. A. Cutler, T. Sullivan, L. Hernandez, P. Zhao, S. Mitchell, G. Nader, M. Bakay, J. N. Rottman, et al. Loss of emerin at the nuclear envelope disrupts the Rb1/E2F and MyoD pathways during muscle regeneration Hum. Mol. Genet., February 15, 2006; 15(4): 637 - 651. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. L. Urso, P. M. Clarkson, D. Hittel, E. P. Hoffman, and P. D. Thompson Changes in Ubiquitin Proteasome Pathway Gene Expression in Skeletal Muscle With Exercise and Statins Arterioscler. Thromb. Vasc. Biol., December 1, 2005; 25(12): 2560 - 2566. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. De Biase, S. M. Knoblach, S. Di Giovanni, C. Fan, A. Molon, E. P. Hoffman, and A. I. Faden Gene expression profiling of experimental traumatic spinal cord injury as a function of distance from impact site and injury severity Physiol Genomics, August 11, 2005; 22(3): 368 - 381. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Maziarz, C. Chung, D. J. Drucker, and A. Emili Integrating Global Proteomic and Genomic Expression Profiles Generated from Islet {alpha} Cells: Opportunities and Challenges to Deriving Reliable Biological Inferences Mol. Cell. Proteomics, April 1, 2005; 4(4): 458 - 474. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Li, M. L. Spletter, and J. A. Johnson Dissecting tBHQ induced ARE-driven gene expression through long and short oligonucleotide arrays Physiol Genomics, March 21, 2005; 21(1): 43 - 58. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. Di Giovanni, A. De Biase, A. Yakovlev, T. Finn, J. Beers, E. P. Hoffman, and A. I. Faden In Vivo and in Vitro Characterization of Novel Neuronal Plasticity Factors Identified following Spinal Cord Injury J. Biol. Chem., January 21, 2005; 280(3): 2084 - 2091. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 R. R Almon, D. C DuBois, J. Y Jin, and W. J Jusko Temporal profiling of the transcriptional basis for the development of corticosteroid-induced insulin resistance in rat muscle J. Endocrinol., January 1, 2005; 184(1): 219 - 232. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 D. S. Hittel, W. E. Kraus, C. J. Tanner, J. A. Houmard, and E. P. Hoffman Exercise training increases electron and substrate shuttling proteins in muscle of overweight men and women with the metabolic syndrome J Appl Physiol, January 1, 2005; 98(1): 168 - 179. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 P. A. Horwitz, E. J. Tsai, M. E. Putt, J. M. Gilmore, J. J. Lepore, M. S. Parmacek, A. C. Kao, S. S. Desai, L. R. Goldberg, S. C. Brozena, et al. Detection of Cardiac Allograft Rejection and Response to Immunosuppressive Therapy With Peripheral Blood Gene Expression Circulation, December 21, 2004; 110(25): 3815 - 3821. [Abstract] [Full Text] [PDF]
|
|