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Validation tests and genewise variance estimation for microarray data.

机译：芯片数据的验证测试和基因方差估计。

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摘要

This dissertation explores two statistical problems arising from microarray data analysis. The first one relates to selection and validation tests of normalization methods. Normalization of microarray data is essential for obtaining meaningful biological results. Fundamental problems arise whether they have effectively normalized the data and how to choose a method to most appropriately normalize a given array. We approach the problem by constructing statistics to test whether there are any systematic biases in the expression profiles among replicated spots within an array. P-values are estimated based on a normal or chi-square approximation. With estimated P-values, we can choose a most appropriate method to normalize a specific array and assess the extent to which the systematic biases due to the variations of experimental conditions have been removed. The effectiveness and validity of the proposed methods are convincingly illustrated by a simulation study and three real data applications.;The second one is genewise variance estimation. It is motivated by two important applications: selecting significantly differentially expressed genes and validation tests of normalization. We introduce a two-way nonparametric model, which is an extension of the famous Neyman-Scott model. The problem itself poses interesting challenges because the number of nuisance parameters is proportional to the sample size and it is not obvious how the variance function can be estimated when measurements are correlated. In such a high-dimensional nonparametric problem, we proposed two novel nonparametric estimators for genewise variance function and semiparametric estimators for measurement correlation, via solving a system of nonlinear equations. Their asymptotic normality is established. The finite sample property is demonstrated by simulation studies. The estimators also improve the power of the tests for detecting statistically differentially expressed genes. The methodology is illustrated by the data from MicroArray Quality Control (MAQC) project.
机译：本文探讨了微阵列数据分析产生的两个统计问题。第一个涉及归一化方法的选择和验证测试。微阵列数据的标准化对于获得有意义的生物学结果至关重要。根本问题出现了，即它们是否已有效地对数据进行规范化，以及如何选择一种最合适地对给定数组进行规范化的方法。我们通过构建统计数据来测试该问题，以测试阵列中复制点之间的表达谱中是否存在任何系统性偏差。 P值是基于法线或卡方近似值估算的。利用估计的P值，我们可以选择一种最合适的方法来对特定数组进行归一化，并评估消除由于实验条件的变化而导致的系统偏差的程度。通过仿真研究和三个实际数据应用，令人信服地说明了所提方法的有效性和有效性。第二个是基因方差估计。它受到两个重要应用程序的激励：选择显着差异表达的基因和标准化的验证测试。我们引入了双向非参数模型，该模型是著名的Neyman-Scott模型的扩展。这个问题本身带来了有趣的挑战，因为麻烦参数的数量与样本大小成正比，并且当测量值相关时如何估计方差函数尚不明显。在这样一个高维非参数问题中，我们通过求解非线性方程组，提出了两种新颖的用于基因方差函数的非参数估计器和用于测量相关性的半参数估计器。建立了它们的渐近正态性。仿真研究证明了有限样本的性质。估计器还提高了检测统计差异表达基因的能力。 MicroArray质量控制（MAQC）项目的数据说明了该方法。

著录项

  • 作者

    Niu, Yue.;

  • 作者单位

    Princeton University.;

  • 授予单位 Princeton University.;
  • 学科 Biology Bioinformatics.;Engineering System Science.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 92 p.
  • 总页数 92
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 系统科学;
  • 关键词

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