New Change-point Problems in Genomic Profiling

DNA拷贝数数据衡量基因组片段的增益和损失，是理解遗传变异和临床研究的重要数据类型。DNA拷贝数数据的分析引发了新的统计问题，特别是在变点检测和高维数据分析领域。本提案确定了这些问题，制定了统计模型，并提出了解决这些问题的方法。主题包括不规则高维模型的模型选择，大量对齐序列的同时变化点检测以及部分观测序列的分割。这些统计方法的发展是对斯坦福基因组技术中心和癌症基因组图谱项目当前分析需求的直接回应，开源软件将提供给这些和更广泛的社区。癌症和其他遗传疾病对基因组科学家来说并不陌生：高通量技术和统计分析一直承诺提供系统水平的研究。疾病的遗传和发展的观点。近年来，基因组学和统计学的新进展，包括更高效的高通量数据收集方法，更大的患者样本集，更开放的合作氛围，以及更复杂的研究设计和数据分析，使我们在研究遗传疾病方面取得了重大的新进展。尽管有这样的承诺，但仍有许多工作要做。特别是，用于全基因组分析数据分析的统计方法缺乏复杂性，无法处理现代数据收集方案中出现的许多问题。这些问题包括高维、缺失观察和大量患者样本的同时推断。在这个提案中，研究者和她的同事对这些新问题进行了阐述，并提出了具有实际解决方案的模型。这些统计方法的发展是对斯坦福基因组技术中心和癌症基因组图谱项目当前分析需求的直接回应，开源软件将提供给这些和更广泛的社区。

DNA copy number data, which measures gains and losses of segments of genomes, is an important data type for understanding genetic variation and for clinical research. The analysis of DNA copy number data motivates new statistical problems, especially in the areas of change-point detection and high dimensional data analysis. This proposal identies these problems, formulates statistical models, and proposes methods for their solution. The topics covered include model selection for irregular high dimensional models, simultaneous change-point detection in a large number of aligned sequences, and segmentation of partially observed sequences. These developments in statistical methodology are a direct response to the current analysis needs at the Stanford Genome Technology Center and in the Cancer Genome Atlas Project, and open source software will be made available to these and broader communities.Cancer and other genetic diseases are no stranger to genome scientists: high-throughput technologies and statistical analyses have always promised to provide a systems level?s view of disease inheritance and progression. In recent years, new concurrent advances in genomics and statistics, including more efficient high throughput data-collection methods, larger patient sample sets, the atmosphere of more open collaboration, and greater sophistication in study design and data analysis have positioned us to make major new advances in studying genetic disease. Despite this promise, there is still much waiting to be done. In particular, statistical methods for the analysis of genome-wide profiling data lacks the sophistication to deal with the many issues that arises in modern data collection schemes. These issues include high dimensionality, missing observations and simultaneous inference in a large number of patient samples. In this proposal, the investigator and her colleagues formulate these new problems and put forth models with practical solutions. These developments in statistical methodology are a direct response to the current analysis needs at the Stanford Genome Technology Center and in the Cancer Genome Atlas Project, and open source software will be made available to these and broader communities.