Nonparametric Analysis of Reverse-Phase Protein Lysate Array Data

描述（由申请人提供）：蛋白质作为生物效应物和诊断标记物发挥着重要作用。其复杂性的一个层面是由于无法在基因组水平上检测到翻译后修饰，这使得人们希望直接测量蛋白质。最近，一些新的蛋白质微阵列技术已经开始蓬勃发展。我们专注于反相蛋白裂解物阵列，使我们能够同时量化蛋白质在许多不同细胞样品中的相对表达水平。这项技术的一个优点是，它只需要少量的细胞与一种抗体结合。然而，与DNA阵列相比，蛋白质裂解物阵列的分析更具挑战性，目前，蛋白质裂解物阵列的应用仍处于探索阶段，缺乏可靠的统计工具来量化蛋白质阵列的信息（包括不确定性）。我们发现，如果可能的话，用一个简单的响应曲线参数族对所有样本进行建模是很困难的。我们提出了一种强大的方法来量化蛋白质裂解物阵列，通过拟合单调非参数响应曲线到同一阵列上的所有样品。该方法具有较强的自适应拟合能力，避免了参数化带来的误差。我们的目标是将统计学中的现代收缩思想纳入非参数方法，从而在每个时间点重复数量较小的时间过程实验中实现更稳定的量化。我们还建议使用野生自举法来评估蛋白质浓度估计的不确定性，并评估这种不确定性在后续分析中的影响。完成后，我们的研究将使蛋白质裂解物阵列的分析更加可靠，并为芯片制造商提供反馈，以改进蛋白质微阵列的设计，这两者都是使裂解物阵列成为生物和医学研究中有用工具的必要条件。公共卫生相关性：成功完成拟议的研究将导致高效和有效的统计和计算工具，用于分析蛋白质裂解物阵列数据，这些数据在生物医学和公共卫生研究中有广泛的应用，最近在与前列腺癌相关的信号通路分析中发现了目标蛋白。需要这些工具来支持蛋白质裂解物阵列技术在临床和生物医学研究中的更好应用。

DESCRIPTION (provided by applicant): Proteins play major roles as biological effectors and diagnostic markers. One level of its complexity is due to the post-translational modifications that cannot be detected at the genome level, which makes it desirable to measure proteins directly. Recently, some new protein microarray technologies have begun to bloom for this purpose. We focus on the reverse-phase protein lysate arrays that allow us to quantify the relative expression levels of a protein in many different cellular samples simultaneously. One advantage of this technology is that it requires a small amount of cells with just one antibody binding. However, it is more challenging to analyze protein lysate arrays than DNA arrays, and at the present time, the applications of protein lysate arrays are still in the exploratory stage with a lack of reliable statistical tools for quantifying the information (including the uncertainty) from protein arrays. We find that it is difficult, if at all possible, to model all the samples with a simple parametric family of response curves. We propose a robust approach to quantify the protein lysate arrays by fitting a monotone nonparametric response curve to all samples on the same array. The proposed method has been shown to fit the data more adaptively, avoiding bias due to parameterization. We aim to incorporate the modern shrinkage ideas in statistics into the nonparametric approach, leading to more stable quantification in time course experiments where the number of replicates is small at each time point. We also propose to use wild-bootstrap for assessing uncertainty of the protein concentration estimates and for assessing the influence of such uncertainties in follow-up analyses. When completed, our research will enable more reliable analysis of protein lysate arrays, and provide feedback to chip makers to improve the design of the protein microarrays, both of which are essential in making lysate arrays a useful tool in biological and medical research. PUBLIC HEALTH RELEVANCE: Successful completion of the proposed research will lead to efficient and effective statistical and computing tools for analyzing protein lysate array data that have wide-ranging applications in biomedical and public health research, as evidenced by the recent discovery of target protein in signal pathway profiling related to prostate cancer. These tools are needed to support better applications of protein lysate array technology in clinical and biomedical research.