Abstract
Microarray gene expression data contains informative features that reflect the critical processes controlling prominent biological functions. Feature selection algorithms have been used in previous biomedical research to find the “marker” genes whose expression value change corresponds to the most eminent difference between specimen classes. One problem encountered in such analysis is the imbalance between very large numbers of genes versus relatively fewer specimen samples. A common concern, therefore, is “overfitting” the data and deriving a set of marker genes with low stability over the entire set of possible specimens. To address this problem, we propose a new test environment in which synthetic data is perturbed to simulate possible variations in gene expression values. The goal is for the generated data to have appropriate properties that match natural data, and that are appropriate for use in testing the sensitivity of feature selection algorithms and validating the robustness of selected marker genes. In this paper, we evaluate a statistically-based resampling approach and a Principal Components Analysis (PCA)-based linear noise distribution approach. Our results show that both methods generate reasonable synthetic data and that the signal/noise rate (with variation weights at 5%, 10%, 20% and 30%) measurably impacts the classification accuracy and the marker genes selected. Based on these results, we identify the most appropriate marker gene selection and classification techniques for each type and level of noise we modeled.
This is a preview of subscription content, log in via an institution to check access.
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
Medvedovic, M., Yeung, K., Bumgarner, R.E.: Bayesian mixture model based clustering of replicated microarray data. Bioinformatics 20(8), 1222–1232 (2004)
Dougherty, E.R., Barrera, J., Brun, M., Kim, S., Cesar, R.M., Chen, Y., Bittner, M., Trent, J.M.: Inference from clustering with application to gene-expression microarrays. J. Comput. Biol. 9, 105–126 (2002)
Yeung, K.Y., Fraley, C., Murua, A., Raftery, A.E., Ruzzo, W.L.: Model-based clustering and data transformations for gene expression data. Bioinformatics 17, 977–987 (2001)
Kerr, M.k., Churchill, G.A.: Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments. Proc. Natl. Acad. Sci. USA 98, 8961–8965 (2001)
Pollard, K.S., Van der Laan, M.J.: Multiple testing for gene expression data: an investigation of null distributions with consequences for the permutation test. In: Proceedings of the 2003 International MultiConference in Computer Science and Engineering, METMBS 2003 Conference, pp. 3–9 (2003)
Ge, Y., Dudoit, S., Speed, T.P.: Resampling-based multiple testing for microarray data analysis. Technical Report 633, Department of Statistics, UC Berkeley (2003)
Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., Altman, R.B.: Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520–525 (2001)
Zhou, X., Wang, X., Dougherty, E.R.: Missing-value estimation using linear and non-linear regression with Bayesian gene selection. Bioinformatics 19(17), 2302–2307 (2003)
Kim, H., Golub, G., Park, H.: Missing value estimation for DNA microarray gene expression data: local least squares imputation. Bioinformatics 21(2), 187–198 (2005)
McShane, L., Radmacher, M., Freidlin, B., Yu, R., Li, M., Simon, R.: Methods for assessing reproducibility of clustering patterns observerd in analyses of microarray data. Bioinformatics 18(11), 1462–1469 (2002)
Fu, L., Youn, E.: Improving Reliability of Gene Selection From Microarray Functional Genomics Data. IEEE Transactions on Information Technology in Biomedicine 7(3), 191–196 (2003)
Kohavi, R., John, G.: Wrapper for feature subset selection. Artifical Intelligence 97(1-2), 273–324 (1997)
Kononenko, I.: Estimating attributes: analysis and extensions of relief. In: Proceedings of the European conference on machine learning on Machine Learning, pp. 171–182. Springer, New York (1994)
Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)
Liu, H., Setiono, R.: Chi2: feature selection and discretization of numeric attributes. In: Proceedings of the Seventh International Conference on Tools with Artifical Intelligence, pp. 388–391 (1995)
Wang, Y., Makedon, F., Ford, J., Pearlman, J.: HykGene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data. Bioinformatics 21(8), 1530–1537 (2004)
Massart, D., Vandeginste, B., Deming, S., Michotte, Y., Kaufman, L.: The k-nearest neighbor method. In: Chemometrics: A Textbook (Data Handling in Science and Technology, vol. 2, pp. 395–397. Elsevier Science, New York (1988)
Vapnik, V.: Statistical Learning Theory. Wiley Interscience, New York (1998)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Stone, M.: Cross-Validatory choice and assessment of statistical predictions. J. R. Stat. Soc. B36(1), 111–147 (1974)
Jolliffe, I.: Principal Component Analysis, 2nd edn. Springer, New York (2002)
Gordon, G., Jensen, R., Hsiao, L., Gullans, S., Blumenstock, J., Ramaswamy, S., Richards, W., Sugarbaker, D., Bueno, R.: Translation of Microarray Data into Clinically Relevant Cancer Diagnostic Tests Using Gege Expression Ratios in Lung Cancer And Mesothelioma. Cancer Research 62, 4963–4967 (2002)
Spira, A., Beane, J., Shah, V., Liu, G., Schembri, F., Yang, X., Palma, J., Brody, J.: Effects of cigarette smoke on the human airway epithelial cell transcriptome. Proc. Natl. Acad. Sci. USA 101(27), 10143–10148 (2004)
Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xiong, F., Huang, H., Ford, J., Makedon, F.S., Pearlman, J.D. (2005). A New Test System for Stability Measurement of Marker Gene Selection in DNA Microarray Data Analysis. In: Bozanis, P., Houstis, E.N. (eds) Advances in Informatics. PCI 2005. Lecture Notes in Computer Science, vol 3746. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573036_41
Download citation
DOI: https://doi.org/10.1007/11573036_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29673-7
Online ISBN: 978-3-540-32091-3
eBook Packages: Computer ScienceComputer Science (R0)