Low-rank Approximation to Probe-level Data with Application to Exon Tiling Arrays

描述(由申请人提供)：人类基因组计划的发现突出了细胞调节、基因和蛋白质之间相互作用的复杂性。一般认为，生物功能和生物活性是由与蛋白质相互作用的基因亚群以高度受控的方式控制的。高通量技术，如微阵列，对于同时研究大量生物成分是有价值的，但这些技术的合理结论取决于对基因组/蛋白质组数据的适当统计分析。这一建议的长期目标是开发适当的统计工具，以探索基因/蛋白质相互作用，并发现这些相互作用如何在生物学活动中发挥作用(例如，疾病表型的诱导)。这项建议涉及对短寡核苷酸数据的分析，如在基因芯片研究和外显子平铺阵列中。表达式数据矩阵的低阶近似在所提出的研究中起着核心作用。其具体目标是：(1)开发一种快速且健壮的低阶算法来对易受异常值影响的数据矩阵执行低阶逼近；(2)开发诊断工具和统计测试以确定低阶表示是否足以捕捉基因表达谱；(3)开发非参数和基于似然的方法来标记和检测外显子拼接阵列的选择性剪接。奇异值分解是为实现这些具体目标而提出的工作的起点。AIMS(1)和(3)将使用交替稳健(抗异常值)回归方法。将为AIMS(2)和(3)开发基于似然和数据自适应的方法。这项拟议的研究与大多数现有的微阵列数据统计工作不同，因为它侧重于探针水平的数据，而不是基因水平的数据。研究人员认为，对基因表达数据的标准单维汇总可能会导致重要信息的丢失。与公共卫生的相关性：拟议研究的成功完成将带来高效和有效的统计工具，用于分析微阵列数据，这些数据在生物医学和公共卫生研究中有广泛的应用，最近发现的宫颈癌和前列腺癌的目标基因就是明证。需要这些工具来支持微阵列技术在临床和生物医学研究中更好的应用。

DESCRIPTION (provided by applicant): Findings from the Human Genome Project highlight the intricacy of interactions between cell regulation, genes and proteins. It is generally understood that biological functions and biological activities are controlled by subsets of genes interacting with proteins in a highly controlled manner. High throughput technologies such as microarrays are valuable for studying a large number of biological components simultaneously, but sound conclusions from these technologies depend on appropriate statistical analyses of the genomic/proteomic data. The long-term objective of this proposal is to develop appropriate statistical tools to explore gene/protein interactions and to discover how these interactions function in biological activities (e.g. induction of disease phenotype). This proposal concerns the analysis of short oligonucleotide data, as in GeneChip studies and exon tiling arrays. Low-rank approximations to the expression data matrices play a central role in the proposed research. The specific aims are: (1) to develop a fast and robust low-rank algorithm to perform low-rank approximation to a data matrix that is subject to outliers; (2) to develop diagnostic tools and statistical tests for determining whether a low-rank representation is adequate to capture gene expression profiles; (3) to develop both nonparametric and likelihood-based approaches for flagging and detecting alternative splicing with exon tiling arrays. Singular value decomposition is a starting point for the proposed work towards those specific aims. Alternating robust (outlier resistant) regression methods will be used for Aims (1) and (3). Likelihood- based and data adaptive methods will be developed for Aims (2) and (3). The proposed research distinguishes itself from most of the existing statistical work on microarray data, as it focuses on probe-level rather than gene-level data. The investigators believe that the standard uni-dimensional summary of gene expression data could lead to loss of important information. PUBLIC HEALTH RELEVANCE: Successful completion of the proposed research will lead to efficient and effective statistical tools for analyzing microarray data that have wide-ranging applications in biomedical and public health research, as evidenced by the recent discovery of target genes for cervical cancer and prostate cancer. Those tools are needed to support better applications of microarray technology in clinical and biomedical research.