Browsing by Author "Chen, Dung-Tsa"

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Statistical inferences based on outliers for gene expression analysis
(2017-08-01)
Lai, Yi-Hsuan
;
Chen, Hung-Chia
;
Chen, Lin-An
;
Chen, Dung-Tsa
;
Hung, Hui-Nien
Discovering differential genes through the detection of outliers in samples from disease group subjects is a new and important approach for gene expression analysis. Extending the outlier mean of Chen et al. (2010a), we develop the asymptotic distributions of the outlier least squares estimator (LSE) and the outlier proportion for the linear regression model. An optimal property of the best linear outlier mean acting as an outlier estimator version of the Gauss Markov theorem for the outlier LSE is presented. Power comparisons demonstrate that tests based on outlier estimators are competitive in detecting a shift of the parent tail distribution. An analysis of DNA microarray data from samples of pancreatic breast tumors supports the use of outlier-based methods. (C) 2017 Elsevier B.V. All rights reserved.
The distribution-based p-value for the outlier sum in differential gene expression analysis
(2010-03-01)
Chen, Lin-An
;
Chen, Dung-Tsa
;
Chan, Wenyaw
;
統計學研究所
;
Institute of Statistics
Outlier sums were proposed by Tibshirani & Hastie (2007) and Wu (2007) for detecting outlier genes where only a small subset of disease samples shows unusually high gene expression, but they did not develop their distributional properties and formal statistical inference. In this study, a new outlier sum for detection of outlier genes is proposed, its asymptotic distribution theory is developed, and the p-value based on this outlier sum is formulated. Its analytic form is derived on the basis of the large-sample theory. We compare the proposed method with existing outlier sum methods by power comparisons. Our method is applied to DNA microarray data from samples of primary breast tumors examined by Huang et al. (2003). The results show that the proposed method is more efficient in detecting outlier genes.
The distribution-based p-value for the outlier sum in differential gene expression analysis
(2010-03-01)
Chen, Lin-An
;
Chen, Dung-Tsa
;
Chan, Wenyaw
;
統計學研究所
;
Institute of Statistics
Outlier sums were proposed by Tibshirani & Hastie (2007) and Wu (2007) for detecting outlier genes where only a small subset of disease samples shows unusually high gene expression, but they did not develop their distributional properties and formal statistical inference. In this study, a new outlier sum for detection of outlier genes is proposed, its asymptotic distribution theory is developed, and the p-value based on this outlier sum is formulated. Its analytic form is derived on the basis of the large-sample theory. We compare the proposed method with existing outlier sum methods by power comparisons. Our method is applied to DNA microarray data from samples of primary breast tumors examined by Huang et al. (2003). The results show that the proposed method is more efficient in detecting outlier genes.