×

Variance function estimation in quantitative mass spectrometry with application to iTRAQ labeling. (English) Zbl 1454.62359

Summary: This paper describes and compares two methods for estimating the variance function associated with iTRAQ (isobaric tag for relative and absolute quantitation) isotopic labeling in quantitative mass spectrometry based proteomics. Measurements generated by the mass spectrometer are proportional to the concentration of peptides present in the biological sample. However, the iTRAQ reporter signals are subject to errors that depend on the peptide amounts. The variance function of the errors is therefore an essential parameter for evaluating the results, but estimating it is complicated, as the number of nuisance parameters increases with sample size while the number of replicates for each peptide remains small. Two experiments that were conducted with the sole goal of estimating the variance function and its stability over time are analyzed, and the resulting estimated variance function is used to analyze an experiment targeting aberrant signaling cascades in cells harboring distinct oncogenic mutations. Methods for constructing conservative \(p\)-values and confidence intervals are discussed.

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis

Software:

R
PDF BibTeX XML Cite
Full Text: DOI arXiv Euclid

References:

[1] Aggarwal, K., Choe, L. H. and Lee, K. H. (2006). Shotgun proteomics using the iTRAQ isobaric tags. Briefings in Functional Genomics and Proteomics 5 112-120.
[2] Berger, R. L. and Boos, D. D. (1994). \(P\) values maximized over a confidence set for the nuisance parameter. J. Amer. Statist. Assoc. 89 1012-1016. · Zbl 0804.62018
[3] Blume-Jensen, P. and Hunter, T. (2001). Oncogenic kinase signalling. Nature 411 355-365.
[4] Böhning, D. (1999). Computer-Assisted Analysis of Mixtures and Applications : Meta-Analysis , Disease Mapping and Others. Monographs on Statistics and Applied Probability 81 . Chapman & Hall/CRC, Boca Raton, FL.
[5] Bruni, C. and Koch, G. (1985). Identifiability of continuous mixtures of unknown Gaussian distributions. Ann. Probab. 13 1341-1357. · Zbl 0588.60014
[6] Carroll, R. J. and Wang, Y. (2008). Nonparametric variance estimation in the analysis of microarray data: A measurement error approach. Biometrika 95 437-449. · Zbl 1437.62408
[7] Davidian, M. and Carroll, R. J. (1987). Variance function estimation. J. Amer. Statist. Assoc. 82 1079-1091. · Zbl 0648.62076
[8] Eckel-Passow, J. E., Oberg, A. L., Therneau, T. M. and Bergen, H. R. (2009). An insight into high-resolution mass-spectrometry data. Biostatistics 10 481-500.
[9] Fan, J., Feng, Y. and Niu, Y. S. (2010). Nonparametric estimation of genewise variance for microarray data. Ann. Statist. 38 2723-2750. · Zbl 1200.62133
[10] Hundertmark, C., Fischer, R., Reinl, T., May, S., Klawonn, F. and Jänsch, L. (2009). MS-specific noise model reveals the potential of iTRAQ in quantitative proteomics. Bioinformatics 25 1004-1011.
[11] Kiefer, J. and Wolfowitz, J. (1956). Consistency of the maximum likelihood estimator in the presence of infinitely many incidental parameters. Ann. Math. Statist. 27 887-906. · Zbl 0073.14701
[12] Klawonn, F., Hundertmark, C. and Jänsch, L. (2006). A maximum likelihood approach to noise estimation for intensity measurements in biology. In Proceedings of the Sixth IEEE International Conference on Data Mining Workshops 180-184. IEEE conference publications.
[13] Mandel, M., Askenazi, M., Zhang, Y. and Marto, J. A. (2013). Supplement to “Variance function estimation in quantitative mass spectrometry with application to iTRAQ labeling.” . · Zbl 1454.62359
[14] Neyman, J. and Scott, E. L. (1948). Consistent estimates based on partially consistent observations. Econometrica 16 1-32. · Zbl 0034.07602
[15] O’Malley, A. J., Smith, M. H. and Sadler, W. A. (2008). A restricted maximum likelihood procedure for estimating the variance function of an immunoassay. Aust. N. Z. J. Stat. 50 161-177. · Zbl 1145.62017
[16] R Development Core Team (2011). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. Available at .
[17] Raab, G. M. (1981). Estimation of a variance function, with application to immunoassay. Appl. Statist. 30 32-40.
[18] Ross, P. L., Huang, Y. N., Marchese, J. N., Williamson, B., Parker, K., Hattan, S., Khainovski, N., Pillai, S., Dey, S., Daniels, S., Purkayastha, S., Juhasz, P., Martin, S., Bartlet-Jones, M., He, F., Jacobson, A. and Pappin, D. J. (2004). Multiplexed protein quantitation in saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Molecular and Cellular Proteomics 3 1154-1169.
[19] Sadler, W. A. and Smith, M. H. (1986). A reliable method of estimating the variance function in immunoassay. Comput. Statist. Data Anal. 3 227-239. · Zbl 0595.62112
[20] Wang, Y., Ma, Y. and Carroll, R. J. (2009). Variance estimation in the analysis of microarray data. J. R. Stat. Soc. Ser. B Stat. Methodol. 71 425-445. · Zbl 1248.62221
[21] Zhang, Y., Askenazi, M., Jiang, J., Luckey, C. J., Griffin, J. D. and Marto, J. A. (2010). A robust error model for iTRAQ quantification reveals divergent signaling between oncogenic FLT3 mutants in acute myeloid leukemia. Mol. Cell Proteomics 9 780-790.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.