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控、基因和蛋白质之间相互作用的复杂性。一般认为，生物学功能和生物学活性由与蛋白质相互作用的基因子集以高度受控的方式控制。高通量技术如微阵列对于同时研究大量生物组分是有价值的，但是来自这些技术的合理结论依赖于对基因组/蛋白质组数据的适当统计分析。该提案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活动中发挥作用（例如诱导疾病表型）。该建议涉及短寡核苷酸数据的分析，如基因芯片研究和外显子拼接阵列。表达数据矩阵的低秩近似在所提出的研究中起着核心作用。具体目标是：（1）开发一种快速而稳健的低秩算法，以执行低秩近似的数据矩阵，是受离群值;（2）开发诊断工具和统计测试，以确定是否低秩表示是足够的捕获基因表达谱;（3）开发非参数和基于似然的方法，用于标记和检测选择性剪接与外显子拼接阵列。奇异值分解是实现这些具体目标的拟议工作的起点。交替稳健（抗离群值）回归方法将用于目的（1）和（3）。将为目标（2）和（3）开发基于似然法和数据自适应方法。这项研究与大多数现有的微阵列数据统计工作不同，因为它关注的是探针水平而不是基因水平的数据。研究人员认为，基因表达数据的标准一维摘要可能导致重要信息的丢失。

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.