Gene Expression Analysis via Multidimensional Scaling
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- Abstract
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Abstract
Expression profiling of biological samples using microarray technologies has proven to be a powerful tool for molecular classification and biomarker identification. Visualization of similarities between biological samples from their molecular signatures is essential in forming new hypotheses. Multidimensional scaling is one of the methods that converts the structure in the similarity matrix to a simple geometrical picture: the larger the dissimilarity between two samples (evaluated through gene expression profiling), the further apart the points representing the experiments in the picture should be. In this unit, we will discuss the mathematical fundamentals of this method, along with step?by?step procedures that allow users to quickly obtain the results, provided that all necessary resources are ready. Examples of applying the MDS program and the interpretation of these results are also provided in this unit
Keywords: Microarray; Gene Expression Profiling; Similarity, Multidimensional Scaling (MDS); Visualization
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- Basic Protocol 1: Using the MDS Method for GENE Expression Analysis
- Guidelines for Understanding Results
- Commentary
- Literature Cited
- Figures
- Tables
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Figure 7.11.1 Flow chart illustrating the data flow of the MDS program. View Image -
Figure 7.11.2 Data matrix format (tab‐delimited text file displayed as Microsoft Excel spreadsheet). The first row of the matrix contains the experiment names and the first column contains the IDs for each gene. It is recommended that white‐space characters be excluded from IDs and experiment names. View Image -
Figure 7.11.3 Color assignment table format (tab‐delimited text file displayed as Microsoft Excel spreadsheet). The first row has the color assigmnets (r = red, b = blue, y = yellow) and the second row has the gene ID. View Image -
Figure 7.11.4 MDS graphical user interface showing various options. View Image -
Figure 7.11.5 Three‐dimensional MDS plots. (A ) Generated from 3614 genes that passed measurement quality criterion; (B ) Color overlay with WNT5A genes' expression ratio (red = expression ratio <0.5, blue = all others); (C ) MDS plot with 276 discriminative genes (derived from Bittner et al., ). View Image
Videos
Literature Cited
| Literature Cited | |
| Bittner, M., Meltzer, P., Chen, Y., Jiang, Y., Seftor, E., Hendrix, M., Radmacher, M., Simon, R., Yakhini, Z., Ben‐Dor, A., Sampas, N., Dougherty, E., Wang, E., Marincola, F., Gooden, C., Lueders, J., Glatfelter, A., Pollock, P., Carpten, J., Gillanders, E., Leja, D., Dietrich, K., Beaudry, C., Berens, M., Alberts, D., and Sondak, V. 2000. Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 406:536‐540. | |
| Borg, I. and Groenen, P. 1997. Modern Multidimensional Scaling: Theory and Applications. Springer, New York. | |
| Cox, T.F. and Cox, M.A.A. 2000. Multidimensional Scaling, 2nd ed. CRC Press, Boca Raton, Fla. | |
| Durbin, B., Hardin, J., Hawkins D., and Rocke D. 2002. A variance‐stabilizing transformation for gene‐expression microarray data. Bioinformatics 18:105‐110. | |
| Duda, R.O., Hart, E., and Stork, D.G. 2001. Pattern Classification, 2nd ed. John Wiley & Sons, New York. | |
| Everitt, B.S. and Dunn, G., 1992. Applied Multivariate Data Analysis. Oxford University Press, New York. | |
| Green, P.E. and Rao V.R. 1972. Applied Multidimensional Scaling. Dryden Press, Hinsdale, Ill. | |
| Green, P.E., Carmone, F.J., and Smith, S.M. 1989. Multidimensional Scaling: Concepts and Applications. Allyn and Bacon, Needham Heights, Mass. | |
| Hedenfalk, I., Duggan, D., Chen, Y., Radmacher, M., Bittner, M., Simon, R., Meltzer, P., Gusterson, B., Esteller, M., Kallioniemi, O.P., Wilfond, B., Borg, A., and Trent, J. 2001. Gene expression profiles in hereditary breast cancer. N. Engl. J. Med. 344:539‐548. | |
| Huber, W., von Heydebreck, A., Sultmann, H., Poustka, A., and Vingron, M. 2002. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18:S96‐S104. | |
| Khan, J., Simon, R., Bittner, M., Chen, Y., Leighton, S.B., Pohida, T., Smith, P.D., Jiang, Y., Gooden, G.C., Trent, J.M., and Meltzer, P.S. 1998. Gene expression profiling of alveolar rhabdomyosarcoma with cDNA microarrays. Cancer Res. 58:5009‐5113. | |
| Schiffman, S.S., Reynolds, M.L., and Young, F.W. 1981. Introduction to Multidimensional Scaling: Theory, Method and Applications. Academic Press, Inc., New York. | |
| Smyth, G., 2004. Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments, Statistical Applications in Genetics and Molecular Biology Vol. 3: No. 1, Article 3. http://www.bepress.com/sagmb/vol3/iss1/art3. | |
| Tusher, V.G., Tibshirani, R., and Chu, G. 2001. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. U.S.A. 98:5116‐5121. | |
| Young, F.W. 1987. Multidimensional Scaling: History, Theory and Applications (R.M. Hamer, ed.). Lawrence Erlbaum Associates, Hillsdale, N.J. |
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