Survival Bump Hunting for Finding Informative Subgroups in High Dimensional Data.

描述(由申请人提供)：基于高维基因组数据的亚组发现可能为疾病过程提供新的见解。通常，这是通过各种形式的聚类分析(监督和非监督)来完成的。极端亚群被定义为本质上是同质的，但呈现极端有价值的结果的那些群。这个项目特别感兴趣的是开发方法来确定这些亚组在生存结果方面是极端的(例如，那些在癌症治疗中表现异常好的人，可以基于高维基因组预测因子来描述)。如果这些亚群是真实的并被发现，其影响将包括提高对疾病病因的理解，发现具有潜在治疗靶点的新生物标记物，并允许早期和个性化的治疗干预。从统计学上讲，这个问题可以被框定在一个稀疏的生存碰撞狩猎框架内。我们聚集了一个生物统计学家团队，他们开创了第一个用于连续反应的稀疏凹凸搜索模型，以及两个国际公认的实验室作为合作伙伴，他们分别致力于儿童髓母细胞瘤和非小细胞肺癌的多平台基因组图谱研究。因此，我们提出了以下具体目标：1)开发新的稀疏突起狩猎模型，允许生存结果具有连续和名义预测因子(例如，基因表达和SNP)。2)开发稀疏突起猎取方法，允许我们通过三种不同的方法--稀疏指导、突起表型和稀疏中介分析--整合SNP和基因表达谱数据；3)开发这些稀疏突起猎杀模型的渐近性能的详细理论；理论用于研究模型验证的新的栅栏方法；以及在详细模拟和我们的合作者实验室提供的数据集上进行实证研究和比较；4)开发一个基于Java的用户友好界面和用R语言编写的命令行终端用户RAN包，它将实现我们的所有方法及其扩展。

DESCRIPTION (provided by applicant): Subgroup discovery based on high dimensional genomic data can potentially provide novel insights into a disease process. Typically this has been done with various forms of cluster analysis (both supervised and unsupervised). Extreme subgroups are defined as those which are homogeneous in nature but which present extreme valued outcomes. Of particular interest in this project is to develop methodology to identify such subgroups which are extreme with respect to survival outcomes (e.g. those individuals that do unusually well on a cancer treatment and can be delineated based on high dimensional genomic predictors). If such subgroups are real and are uncovered, implications would include improved understanding of the disease etiology, discovery of new biomarkers with potential therapeutic targets, and allow early and personalized therapeutic interventions. Statistically, thi problem can be framed within a sparse survival bump hunting framework. We have brought together a team of biostatisticians who have pioneered the first sparse bump hunting models for continuous responses, as well as two internationally recognized laboratories as collaborators, who work on multi-platform genomic profiling for pediatric medulloblastoma and non-small cell lung cancer respectively. We thus propose the following specific aims: 1) To develop new models for sparse bump hunting that allow survival outcomes with both continuous and nominal predictors (e.g. gene expression and SNPs).; 2) To develop a sparse survival bump hunting approach that will allow us to integrate SNP and gene expression profile data by three different approaches - sparse coaching, bump phenotyping and sparse mediation analysis; 3) To develop detailed theory for asymptotic performance of these sparse survival bump hunting models; theory for a new fence-based methodology for studying model validation; and to empirically study and compare the performance in detailed simulations as well as on the datasets provided by our collaborator laboratories; 4) To develop a Java-based user-friendly interface and a command line end-user CRAN package in the R language that will implement all of our methodologies and its extensions.