Abstract
Over the last years miRNA microarray platforms have provided insights in the biological mechanisms underlying onset and development of several diseases and have thus become a very popular instrument for profiling thousands of miRNA simultaneously. However, because of large variety of microarray platforms available, an assessment of their performance in terms of both within-platform reliability and between-platform agreement is needful. In particular, assessment of platform concordance has been a very relevant issue in the past decade. To date, only a few studies have evaluated this problem in the field of miRNA microarray, and mostly by using improper statistical methods such as the Pearson and Spearman correlation coefficients. In this work we suggest to use a recently proposed modified version of the classical Bland-Altman approach for comparing clinical measurement methods. This modified version is useful in that allows not only to evaluate agreement between different miRNA microarray platforms, but also to assess which are the potential sources of disagreement/bias between them.
Two samples were profiled using Affymetrix, Agilent and Illumina miRNA platform using three technical replicates each, and pairwise agreement between platforms was evaluated within each sample. Our results suggest that, after bias correction, Illumina and Agilent show the best patterns of agreement for both samples involved in the experiment, whereas Affymetrix is the one which seem to ”disagree” most, suggesting that a linear relationship as that hypothesized by the measurement error model used is not able to capture the complexity of the phenomenon.
In the future it will be interesting to apply this method also to the comparison of microarray and NGS platform, a topic which is becoming more and more relevant, also by adopting non-linear measurement error models to depict relationships between platforms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Esquela-Kerscher, A., Slack, F.J.: Oncomirs - microRNAs with a role in cancer. Nature Reviews Cancer 6(4), 259–269 (2006)
Lu, J., Getz, G., Miska, E.A., Alvarez-Saavedra, E., Lamb, J., Peck, D., Sweet-Cordero, A., Ebert, B.L., Mak, R.H., Ferrando, A.A., Downing, J.R., Jacks, T., Horvitz, H.R., Golub, T.R.: MicroRNA expression profiles classify human cancers. Nature 435(7043), 834–838 (2005)
Blower, P.E., Verducci, J.S., Lin, S., Zhou, J., Chung, J., Dai, Z., Liu, C., Reinhold, W., Lorenzi, P.L., Kaldjian, E.P., Croce, C.M., Weinstein, J.N., Sadee, W.: MicroRNA expression profiles for the NCI-60 cancer cell panel. Molecular Cancer Therapeutics 6(5), 1483–1491 (2007)
Søkilde, R., Kaczkowski, B., Podolska, A., Cirera, S., Gorodkin, J., Møller, S., Litman, T.: Global microRNA analysis of the NCI-60 cancer cell panel. Molecular Cancer Therapeutics 10(3), 375–384 (2011)
Hébert, S.S., Horré, K., Nicolaï, S., Bergmans, B., Papadopoulou, A.S., Delacourte, A., De Strooper, B.: MicroRNA regulation of Alzheimer’s Amyloid precursor protein expression. Neurobiology of Disease 33(3), 422–428 (2009)
Dehwah, M.A., Huang, Q.: MicroRNAs and Type 2 Diabetes/Obesity. Journal of Genetics and Genomics 39(1), 11–18 (2012)
Paraboschi, E.M., Soldà, G., Gemmati, D., Orioli, E., Zeri, G., Benedetti, M.D., Salviati, A., Barizzone, N., Leone, M., Duga, S., Asselta, R.: Genetic Association and Altered Gene Expression of Mir-155 in Multiple Sclerosis Patients. International Journal of Molecular Sciences 12(12), 8695–8712 (2011)
Ach, R.A., Wang, H., Curry, B.: Measuring microRNAs: Comparisons of microarray and quantitative PCR measurements, and of different total RNA prep methods. BMC Biotechnology 8, 69 (2008)
Miska, E.A., Alvarez-Saavedra, E., Townsend, M., Yoshii, A., Sestan, N., Rakic, P., Constantine-Paton, M., Horvitz, H.R.: Microarray analysis of microRNA expression in the developing mammalian brain. Genome Biology 5(9), R68.1–R68.13 (2004)
Yauk, C.L., Berndt, M.L.: Review of the Literature Examining the Correlation Among DNA Microarray Technologies. Environmental and Molecular Mutagenesis 48(5), 380–394 (2007)
Sato, F., Tsuchiya, S., Terasawa, K., Tsujimoto, G.: Intra-Platform Repeatability and Inter-Platform Comparability of MicroRNA Microarray Technology. PLoS ONE 4(5), e5540 (2009)
Yauk, C.L., Rowan-Carroll, A., Stead, J.D.H., Williams, A.: Cross-platform analysis of global microRNA expression technologies. BMC Genomics 11, 330 (2010)
Chen, J.J., Hsueh, H.M., Delongchamp, R.R., Lin, C.J., Tsai, C.A.: Reproducibility of microarray data: a further analysis of microarray quality control (MAQC) data. BMC Bioinformatics 8, 412 (2007)
Bland, J.M., Altman, D.G.: Measurement error and correlation coefficients. British Medical Journal 313(7048), 41–42 (1996)
Bland, J.M., Altman, D.G.: Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 327(8476), 307–310 (1986)
Altman, D.G., Bland, J.M.: Measurement in medicine: the analysis of method comparison studies. Statistician 32, 307–317 (1983)
Liao, J.J.Z., Capen, R.: An Improved Bland-Altman Method for Concordance Assessment. The International Journal of Biostatistics 7(1), 9 (2011)
Affymetrix: Affymetrix© miRNA QC Tool guide, Santa Clara, California (2008)
Lopez-Romero, P., Gonzales, M.A., Callejas, S., Dopazo, A., Irizarry, R.A.: Processing of Agilent microRNA array data. BMC Research Note 3(18) (2010)
R Development Core Team. R: A Language and Environment for Statistical Computing, Vienna, Austria (2011) ISBN 3-900051-07-0, http://www.R-project.org/
Lopez-Romero, P.: Pre-processing and differential expression analysis of Agilent microRNA arrays using the AgiMicroRna Bioconductor library. BMC Genomics 12, 64 (2011)
Gentleman, R.C., Carey, V.J., Bates, D.M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., Hornik, K., Hothorn, T., Huber, W., Iacus, S., Irizarry, R., Leisch, F., Li, C., Maechler, M., Rossini, A.J., Sawitzki, G., Smith, C., Smyth, G., Tierney, L., Yang, J.Y., Zhang, J.: Bioconductor: Open software development for computational biology and bioinformatics. Genome Biology 5, R80 (2004)
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)
Giard, D.J., Aaronson, S.A., Todaro, G.J., Arnstein, P., Kersey, J.H., Dosik, H., Parks, W.P.: In vitro cultivation of human tumors: establishment of cell lines derived from a series of solid tumors. J. Natl. Cancer Inst. 51(5), 1417–1423 (1973)
Hua, Y.J., Tu, K., Tang, Z.Y., Li, Y.X., Xiao, H.S.: Comparison of normalization methods with microRNA microarray. Genomics 92, 122–128 (2008)
Rao, Y., Lee, Y., Jarjoura, D.: A Comparison of Normalization Techniques for MicroRNA Microarray Data. Statistical Applications in Molecular Genetics Biology 7, 22 (2008)
Pradervand, S., Weber, J., Thomas, J., Bueno, M., Wirapati, P.A., Lefort, K., Dotto, G.P., Harshman, K.: Impact of normalization on miRNA microarray expression profiling. RNA 15, 493–501 (2009)
Clopper, C., Pearson, E.S.: The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404–413 (1934)
Chung, Y., Rabe-Hesketh, S., Gelman, A., Liu, J., Dorie, V.: Avoiding Boundary Estimates in Linear Mixed Models Through Weakly Informative Priors. U.C. Berkeley Division of Biostatistics Working Paper Series, paper 284 (2012)
Carroll, R.J., Ruppert, D., Stefanski, L.A., Crainiceanu, C.M.: Measurement Error in Nonlinear Models: A Modern Perspective. Chapman & Hall/CRC, New York (2006)
Berry, S.M., Carroll, R.J., Ruppert, D.: Bayesian Smoothing and Regression Splines for Measurement Error Problems. Journal of the American Statistical Association 97(457), 160–168 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bassani, N., Ambrogi, F., Biganzoli, E. (2013). Assessing Agreement between microRNA Microarray Platforms via Linear Measurement Error Models. In: Peterson, L.E., Masulli, F., Russo, G. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2012. Lecture Notes in Computer Science(), vol 7845. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38342-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-38342-7_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38341-0
Online ISBN: 978-3-642-38342-7
eBook Packages: Computer ScienceComputer Science (R0)