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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/30900
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/30900

    Title: 應用存活分析在微陣列資料的基因表面定型之探討
    Gene Expression Profiling with Survival Analysis on Microarray Data
    Authors: 張仲凱
    Contributors: 郭訓志
    Keywords: 基因表現資料
    Gene expression data
    Censored survival data
    Cox proportional hazards model
    Rasmpling based Peto-Peto test
    Threshold gradient directed regularization
    Date: 2005
    Issue Date: 2009-09-14
    Abstract: 如何藉由DNA微陣列資料跟存活資料的資訊來找出基因表現定型一直是個重要的議題。這些研究的主要目標是從大量的基因中找出那些真正跟存活時間或其它重要的臨床結果有顯著關係的小部分。Threshold Gradient Directed Regularization (TGDR)是ㄧ種已經被應用在高維度迴歸問題中能同時處理變數選取以及模型配適的演算法。然而,TGDR採用一種梯度投影型態的演算法使得收斂速率緩慢。在本篇論文中,我們建議新的包含Newton-Raphson求解演算法類型的改良版TGDR方法。我們建議的方法有類似TGDR的特性但卻有比較快的收斂速率。文中並利用一筆附有設限存活時間的真實微陣列癌症資料來做示範。
    Analyzing censored survival data with high-dimensional covariates arising from the microarray data has been an important issue. The main goal is to find genes that have pivotal influence with patient's survival time or other important clinical outcomes. Threshold Gradient Directed Regularization (TGDR) method has been used for simultaneous variable selection and model building in high-dimensional regression problems. However, the TGDR method adopts a gradient-projection type of method and would have slow convergence rate. In this thesis, we proposed Modified TGDR algorithms which incorporate Newton-Raphson type of search algorithm. Our proposed approaches have the similar characteristics with TGDR but faster convergence rates. A real cancer microarray data with censored survival times is used for demonstration.
    The second part of this thesis is about a proposed resampling based Peto-Peto test for survival functions on interval censored data. The proposed resampling based Peto-Peto test can evaluate the power of survival function estimation methods, such as Turnbull’s Procedure and Kaplan-Meier estimate. The test shows that the power based on Kaplan-Meier estimate is lower than that based on Turnbull’s estimation on interval censored data. This proposed test is demonstrated on simulated data and a real interval censored data from a breast cancer study.
    Reference: 1. Alizadeh A. A., Eisen M. B., Eric Davis R., Ma C., Lossos I. S., Rosenwald A., Boldrick J. C., Sabet H., Tran T., Yu X., Powell J. I., Yang L., Marti G. E., Moore T., Hudson J. Jr, Lu L., Lewis D. B., Tibshirani R., Sherlock G., Chan W. C., Greiner T. C., Weisenburger D. D., Armitage J. O., Warnke R., Levy R., Wilson W., Grever M. R., Byrd J. C., Botstein D., Brown P. O., and Staudt L. M. (2000). Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature, 403, 503-511.
    2. Beadle, G., Come, S., Henderson, C., Silver, B., and Hellman, S. (1984). The effect of adjuvent chemotherapy on the cosmetic results after primary radiation treatment for early stage breast cancer. International Journal of Radiation Oncology, Biology and Physics, 10, 2131-2137.
    3. Bertsekas, D. P. (1982). Projected Newton methods for optimization problems with simple constraints. SIAM Control and Optimization, 20, 221-246.
    4. Cox, D. R. (1972). Regression models and life-tables. Journal of Royal Statistical Society, Series B, 34, 187-220
    5. Craig, B. A., Black, M. A. and Doerge, R. W. (2003). Gene expression data: The technology and statistical analysis. Journal of Agricultural, Biological, and Environmental Statistic, 8, 1-28.
    6. Dykstra, R. L. and Kuo, H. C. (2003). Constrained non-parametric estimation under arbitrarily grouped, censored, and truncated data. A thesis submitted in partial fulfillment of the requirement for the Doctor of Philosophy degree in Statistics in the Graduate College of The University of Iowa.
    7. Friedman, J. H. and Popescu, B. E. (2004). Gradient directed regularization for linear regression and classification. Technical report, Department of Statistics, Stanford University. http://www-stat.stanford.edu/~jhf/PathSeeker.html
    8. Gui, J. and Li, H. (2005). Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data. Bioinformatics, In press.
    9. Huang, Y. W. (2004). The comparison of parameter estimation with application to Massachusetts heath care panel study. A thesis submitted in partial fulfillment of the requirement for the Master Science degree in Mathematic in National Sun Yat-Sen University.
    10. Jolliffe I.T. (1986). Principal component analysis. New York: Springer-Verlag.
    11. Ma, S. and Huang, J. (2005). Clustered threshold gradient directed regularization: with applications to survival analysis using microarray data. Technical Report No. 348, Department of Statistics and Actuarial Science, University of Iowa.
    12. Pan W. (1997). Extending the iterative convex minorant algorithm to the Cox model. Report 1997-013, Division of Biostatistics, University of Minnesota.
    13. Park P. J., Tian L. and Kohane I. S. (2002). Linking gene expression data with patient survival times using partial least squares. Bioinformatics, 18, S120-S127.
    14. Petroni, G. R. and Wolfe, R. A. (1994). A two-sample test for stochastic ordering with interval-censored data. Biometrics, 50, 77-87.
    15. Tibshirani, R. (1996). Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society, B, 58, 267-288.
    16. Turnbull, B. W. (1976). The empirical distribution function with arbitrarily grouped, censored, and truncated data. Journal of the Royal Statistical Society, B, 38, 290-295.
    17. Wold, H. (1966). Estimation of principal components and related models by iterative least squares. In Multivariate Analysis, Ed. P.R. Krishnaiah, New York: Academic Press, 391-420.
    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0093354012
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

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