Statistics for high-dimensional data with applications in analysis of high-throughput genomics experiments

高维数据统计及其在高通量基因组学实验分析中的应用

• 批准号: 240006-2006
• 负责人: Kustra, Rafal
• 金额: $ 0.8万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2008
• 资助国家: 加拿大
• 起止时间: 2008-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=394310
• 关键词: Statistics; dimensional; data; applications; analysis

The analysis of very high-dimensional data poses a unique set of challenges for statisticians: in designing feasible algorithms, in estimation and visualization, and in inference. In this research project I propose to continue my work in addressing some issues in all these areas, with a special emphasis on high-throughput genomic data. This includes data from expression microarray experiments, which can interrogate up to 50k genes in a single biological assay, genome-wide scans of genotypic information, such as Affymetrix GeneChip Human Mapping arrays which can interrogate up to 500k markers on a human genome (so-call Single Nucleotide Polymorphism which are locations on a genome accounting for vast majority of genetic differences), and from proteomics experiments, such as Tandem Mass Spectrometry that is being currently used to detect presence of tens or hundreds of thousands of proteins in one experiment, and is being researched for quantitative use. While the nature of each of these data examples can be very different, as well as the potential applications of such technologies (which are now widely used for both basic and clinical research), they all share a number of characteristics, that can be addressed by general statistical research. The results of this research program will benefit both the statistical community, by introducing new methods for statistical analysis and visualization of high-dimensional data and by motivating further research in this very important area, as well as biologists and clinicians by expanding the toolbox of statistical methods and software tools that can be applied to their genomic experiments. As part of this work we  will apply and validate the new methods on real data to perform  primary and secondary analysis of important clinical and biological experiments. The long term objective of this work is to connect the some results in theoretical statistics of high-dimensional data, great advances computer technology and the resulting novel approaches to statistical analysis, and the high-throughput revolution in biological research to help answer important questions in biological and  health sciences.

高维数据的分析给统计学家带来了一系列独特的挑战：设计可行的算法、估计和可视化以及推理。在这个研究项目中，我建议继续解决所有这些领域的一些问题，特别强调高通量基因组数据，这包括来自表达微阵列实验的数据，该实验可以在一次生物测定中询问多达 50k 个基因，以及基因型信息的全基因组扫描，例如 Affymetrix GeneChip Human。映射阵列可以询问人类基因组上多达 50 万个标记（所谓的单核苷酸多态性，它们是基因组上占绝大多数遗传差异的位置），并且来自蛋白质组学实验，例如目前使用的串联质谱法在一个实验中检测数万或数十万种蛋白质的存在，并且正在研究使用，尽管这些数据示例的每个定量的性质可能非常不同，以及此类技术的潜在应用（其中。现在广泛用于基础和临床研究），它们都有一些共同的特征，可以通过一般统计研究来解决。该研究计划的结果将通过引入新的统计分析方法和方法使统计界受益。高维数据的可视化，并通过扩展可应用于基因组实验的统计方法和软件工具来激发生物学家和科学家在这一非常重要领域的进一步研究。作为这项工作的一部分，我们将应用这些工具。并在实际数据上验证新方法这项工作的长期目标是将高维数据的理论统计、计算机技术的巨大进步和由此产生的统计分析的新方法以及高维数据的一些结果联系起来。 -生物研究的吞吐量革命，有助于回答生物和健康科学中的重要问题。