基于多基因组合选择模型的结肠癌特征基因选取

通过基因的Bhattacharyya距离指标过滤掉大部分无关基因,然后探索性的提出了一种建立多基因组合选择模型的统计方法.从候选特征基因中选取了8个可能的结肠癌特征基因集合,判别分析的结果证明了该方法的可行性.     

Some Results on the Control of the False Discovery Rate under Dependence

Abstract.  Controlling the false discovery rate (FDR) is a powerful approach to multiple testing, with procedures developed with applications in many areas. Dependence among the test statistics is a common problem, and many attempts have been made to extend the procedures. In this paper, we show that a certain degree of dependence is allowed among the test statistics, when the number of tests is large, with no need for any correction. We then suggest a way to conservatively estimate the proportion of false nulls, both under dependence and independence, and discuss the advantages of using such estimators when controlling the FDR.     

基于SPR相位检测的蛋白质芯片信号获取与处理研究

表面等离子体共振传感器有可能发展成为一种灵敏的高通量检测的蛋白质组学研究工具,而信号获取和数据处理是其关键之一。本文利用数值模拟全面研究了微阵列相位检测的分辨率与空间采样频率、采样周期数以及ADC位数等关系,详细分析了相关、正弦拟合和傅立叶变换(FTP)等算法对相位检测的精度和误差等影响。研究结果表明,在理想状况下,4个CCD像素就能满足分辨率对光电转换的要求,而在有噪音的情况下,分辨率随着空间采样频率的增加而提高;信号周期数对分辨率影响不大,但有利于克服噪声影响,提高精度;ADC对分辨率影响最大,高分辨率需要选择多位ADC,8位ADC时分辨率约为0.15°,10位时约为0.05°,而12位时约为0.01°。     

A comparison of regularization methods applied to the linear discriminant function with high-dimensional microarray data

Classification of gene expression microarray data is important in the diagnosis of diseases such as cancer, but often the analysis of microarray data presents difficult challenges because the gene expression dimension is typically much larger than the sample size. Consequently, classification methods for microarray data often rely on regularization techniques to stabilize the classifier for improved classification performance. In particular, numerous regularization techniques, such as covariance-matrix regularization, are available, which, in practice, lead to a difficult choice of regularization methods. In this paper, we compare the classification performance of five covariance-matrix regularization methods applied to the linear discriminant function using two simulated high-dimensional data sets and five well-known, high-dimensional microarray data sets. In our simulation study, we found the minimum distance empirical Bayes method reported in Srivastava and Kubokawa [Comparison of discrimination methods for high dimensional data, J. Japan Statist. Soc. 37(1) (2007), pp. 123–134], and the new linear discriminant analysis reported in Thomaz, Kitani, and Gillies [A Maximum Uncertainty LDA-based approach for Limited Sample Size problems – with application to Face Recognition, J. Braz. Comput. Soc. 12(1) (2006), pp. 1–12], to perform consistently well and often outperform three other prominent regularization methods. Finally, we conclude with some recommendations for practitioners.     

Estimating prediction error in microarray classification: Modifications of the 0.632+ bootstrap when ${\bf n} < {\bf p}$

We are interested in estimating prediction error for a classification model built on high dimensional genomic data when the number of genes (p) greatly exceeds the number of subjects (n). We examine a distance argument supporting the conventional 0.632+ bootstrap proposed for the $n > p$ scenario, modify it for the $n < p$ situation and develop learning curves to describe how the true prediction error varies with the number of subjects in the training set. The curves are then applied to define adjusted resampling estimates for the prediction error in order to achieve a balance in terms of bias and variability. The adjusted resampling methods are proposed as counterparts of the 0.632+ bootstrap when $n < p$ , and are found to improve on the 0.632+ bootstrap and other existing methods in the microarray study scenario when the sample size is small and there is some level of differential expression. The Canadian Journal of Statistics 41: 133–150; 2013 © 2012 Statistical Society of Canada