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志物发挥主要作用。其复杂性的一个水平是由于不能在基因组水平上检测到的翻译后修饰，这使得直接测量蛋白质是可取的。最近，一些新的蛋白质微阵列技术已经开始为此目的而绽放。我们专注于反相蛋白裂解物阵列，使我们能够同时定量许多不同细胞样品中蛋白质的相对表达水平。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，蛋白质裂解物芯片的分析比DNA芯片更具挑战性，目前蛋白质裂解物芯片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芯片的信息（包括不确定性）。我们发现，这是很难的，如果在所有可能的，所有的样本与一个简单的参数家庭的响应曲线。我们提出了一个强大的方法来量化蛋白质裂解物阵列拟合单调的非参数响应曲线，所有样品在同一个阵列。所提出的方法已被证明适合数据更自适应，避免由于参数化的偏见。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次数较少的时间过程实验中实现更稳定的定量。我们还建议使用野生自助法评估蛋白质浓度估计值的不确定性，并评估这种不确定性在后续分析中的影响。完成后，我们的研究将能够更可靠地分析蛋白质裂解物阵列，并为芯片制造商提供反馈，以改进蛋白质微阵列的设计，这两者对于使裂解物阵列成为生物和医学研究中的有用工具至关重要。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具有广泛的应用，最近发现的与前列腺癌相关的信号通路分析中的靶蛋白就是证明。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.