Longitudinal Studies with Gene Expression Data

描述(申请人提供)：利用现有的新生物技术，对受试者(或一般生物体)样本进行的典型纵向研究可能涉及收集一个或多个时间点的数千维基因表达谱(或一般生物标记物)，随着时间的推移而改变治疗方法，以及关于临床结果(如生存)的缺失/审查数据。这类数据的一个重要问题是--除了涉及基因表达的更经典的“审查”和“混淆处理”问题之外，每个基因本身都代表着重要的参数，因此人们通常需要一次估计数千个参数。特别是，这意味着，在估计的参数比独立观测的参数多得多的情况下，需要进行统计推断。此外，需要可视化技术和复杂的监督/非监督聚类技术来发现重要的整体结构。这项研究将开发统计半参数方法来分析在收集基因表达数据的观察性纵向研究中产生的数据。这项工作的一个重要重点将是应用所提出的方法与主题专家合作分析以下纵向研究：1)空气污染对儿童哮喘自然病史的因果影响；2)休闲时间活动对老年人生存和健康的因果影响；3)癌症患者复发/存活与基因表达谱之间的因果关系；4)酵母数据集中的基因表达及其与非编码区的关系。这些方法将使人们有可能了解不同基因(从而编码的蛋白质)的表达是如何相互作用的，从而提供对生物化学途径的洞察和关于基因组水平上潜在因果机制的线索。特别是，人们认为基因表达是癌症进展的重要指标，了解哪些基因在这一过程中是活跃的，最终将导致更好的诊断和治疗策略。

DESCRIPTION (provided by applicant): With new existing biotechnologies, a typical longitudinal study following up a sample of subjects (or organisms, in general) can involve collection of multi-thousand dimensional gene expression profiles (or biomarkers, in general) at one or more points in time, change of medical treatments over time, and missing/censored data on clinical outcomes such as survival. An important issue with such data -- in addition to the more classical issues "censoring" and "confounding of treatment" involving gene expression is that each gene represents important parameters itself, so that one typically needs to estimate thousands of parameters at once. In particular, this implies that one needs statistical inference in a setting where one estimates many more parameters than one has independent observations. In addition, visualization techniques and sophisticated supervised/unsupervised clustering techniques are required to discover the significant and important overall structures. This research will develop statistical semi parametric methods for the analysis of data arising in observational longitudinal studies collecting gene-expression data. An important focus of this work will be to apply the proposed methods to analyze longitudinal studies in collaboration with subject matter experts on 1) the causal effect of air pollution on the natural history of asthma in children, 2) the causal effect of leisure time activity on survival and health in the elderly population, 3) causal relationships between recurrence/survival and gene expression profiles in cancer patients and 4) gene expression in yeast data sets and its relationship to the non-coding regions. The methods will make it possible to learn how expression of different genes (and hence the encoded proteins) interact, providing insight into biochemical pathways and clues about underlying causal mechanisms at work on the genomic level. In particular, it is believed that gene expression is an important indicator of cancer progression and that an understanding of which genes are active in this process will ultimately lead to better strategies for diagnosis and treatment.