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，这已经通过各种形式的聚类分析（监督和无监督）完成。极端子群定义为那些性质上是齐次的，但呈现极端值结果的子群。在这个项目中特别感兴趣的是开发方法来识别这样的亚组，这是极端的生存结果（例如，那些人做了异常好的癌症治疗，并可以根据高维基因组预测划定）。如果这样的亚组是真实的并且被发现，其意义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志物，以及允许早期和个性化的治疗干预。从统计学上讲，这个问题可以在稀疏生存碰撞狩猎框架内框架。我们汇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碰撞狩猎模型，以及两个国际公认的实验室作为合作者，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分析。因此，我们提出了以下具体目标：1）开发新的模型，稀疏颠簸狩猎，允许生存结果与连续和名义预测（如基因表达和SNP）。2)开发一种稀疏的生存碰撞狩猎方法，这将允许我们通过三种不同的方法-稀疏教练，碰撞表型和稀疏中介分析来整合SNP和基因表达谱数据; 3）开发这些稀疏生存碰撞狩猎模型的渐近性能的详细理论;用于研究模型验证的新的基于栅栏的方法的理论;并在详细的模拟以及我们的合作实验室提供的数据集上进行实证研究和比较性能; 4）开发基于Java的用户友好界面和R语言的命令行最终用户CRAN包，以实现我们所有的方法及其扩展。

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, this 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.