Abstract
Data preprocessing in microarray technology is a crucial initial step before data analysis is performed. Many preprocessing methods have been proposed but none has proved to be ideal to date. Frequently, datasets are limited by laboratory constraints so that the need is for guidelines on quality and robustness, to inform further experimentation while data are yet restricted. In this paper, we compared the performance of four popular methods, namely MAS5, Li & Wong pmonly (LWPM), Li & Wong subtractMM (LWMM), and Robust Multichip Average (RMA). The comparison is based on the analysis carried out on sets of laboratory-generated data from the Bioinformatics Lab, National Institute of Cellular Biotechnology (NICB), Dublin City University, Ireland. These experiments were designed to examine the effect of Bromodeoxyuridine (5-bromo-2-deoxyuridine, BrdU) treatment in deep lamellar keratoplasty (DLKP) cells. The methodology employed is to assess dispersion across the replicates and analyze the false discovery rate. From the dispersion analysis, we found that variability is reduced more effectively by LWPM and RMA methods. From the false positive analysis, and for both parametric and nonparametric approaches, LWMM is found to perform best. Based on a complementary q-value analysis, LWMM approach again is the strongest candidate. The indications are that, while LWMM is marginally less effective than LWPM and RMA in terms of variance reduction, it has considerably improved discrimination overall.
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References
Benjamini, Y., Krieger, A., Yekutieli, D. Two-staged Linear Step-Up FDR Controlling Procedure, Technical Report, Tel-Aviv University and Department of Statistics, Wharton School, University of Pennsylvania (2001)
Bolstad, B. M., Irizarry, R. A., Astrand, M., Speed, T. P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics, 19(2), 185–193 (2003)
Cleveland, W.S. Visualizing Data, Summit, New Jersey: Hobart Press (1993)
Cope, L. M., Irizarry, R. A., Jaffee, H. A., Wu, Z., Speed, T. P. A benchmark for affymetrix genechip expression measures. Bioinformatics, 20(3), 323–331 (2004)
Gordon, A., Glazko, G., Qiu, X., Yakovlev, A. Control of the mean number of false discoveries, Bonferroni and stability of multiple testing. Ann. Appl. Statist., 1(1), 179–190 (2007)
Hubbell, E., Liu, W. M., Mei, R. Robust estimators for expression analysis. Bioinformatics, 18(12), 1585–1592 (2002)
Irizarry, R. A., Hobbs, B., Collin F., Beazer-Barclay Y.D., Antonellis K. J., Scherf U., Speed, T. P. Exploration, normalization and summaries of high density oligonucleotide array probe level data. Biostatistics, 4(2), 249–264(2003)
Mastrogianni A., Dermatas E., Bezerianos A. Robust pre-processing and noise reduction in microarray images. Proceeding (555), Biomedical Engineering (2007)
McMorrow, J. Ph.D. thesis. Dublin City University, Ireland (2006)
Novak, J. P., Kim, S. Y., Xu, J., Modlich, O. et al. : Generalization of DNA microarray dispersion properties: microarray equivalent of t-distribution. Biol Direct, 1(27), doi:10.1186/1745-6150-1-27, (2006)
Speed, T. P. Statistical Analysis of Gene Expression Microarray Data. CRC Press, ISBN 1584883278, 9781584883272
Stafford, P. (Ed.) Methods in Microarray Normalization (Drug Discovery Series), USA, CRC Press, ISBN 1420052780, 9781420052787
Storey, J. D. The positive false discovery rate: a Bayesian interpretation and the q-value. Ann. Statist., 31(6), 2013–2035 (2001)
Westfall, P. H., Young, S. S. Resampling Based Multiple Testing: Examples and Methods for p-Value Adjustment. Wiley, England (1993)
Wu, Z., Irizarry, R. A., Gentleman, R., Martinez-Murillo, F., Spencer, F. A model-based background adjustment for oligonucleotide expression arrays. J. Am. Stat. Assoc., 99(468), 909–917 (2004)
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Shakya, K., Ruskin, H.J., Kerr, G., Crane, M., Becker, J. (2010). Comparison of Microarray Preprocessing Methods. In: Arabnia, H. (eds) Advances in Computational Biology. Advances in Experimental Medicine and Biology, vol 680. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5913-3_16
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DOI: https://doi.org/10.1007/978-1-4419-5913-3_16
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