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

探针级数据的低秩近似及其应用于外显子平铺阵列

基本信息

批准号：
8072159
负责人：
Xuming He
金额：
$ 20.65万
依托单位：
UNIVERSITY OF TX MD ANDERSON CAN CTR
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-07-11 至 2013-05-31
项目状态：
已结题

项目摘要

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.

描述（由申请人提供）：来自人类基因组计划的发现强调了细胞调控、基因和蛋白质之间相互作用的复杂性。人们普遍认为，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。微阵列等高通量技术对于同时研究大量生物成分很有价值，但这些技术的可靠结论取决于对基因组/蛋白质组学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起作用（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平铺阵列。表达式数据矩阵的低秩近似在本研究中起着核心作用。具体目标是：(1)开发一种快速、鲁棒的低秩算法，对受离群值影响的数据矩阵进行低秩逼近；(2)开发诊断工具和统计测试，以确定低秩表示是否足以捕获基因表达谱；(3)开发非参数和基于似然的方法来标记和检测外显子平铺阵列的备选剪接。奇异值分解是实现这些具体目标的建议工作的起点。交替鲁棒（抗离群值）回归方法将用于目标(1)和(3)。基于似然和数据自适应的方法将为目标(2)和(3)发展。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单维总结可能导致重要信息的丢失。