III: Small: Collaborative Research: Combinatorial Collaborative Clustering for Simultaneous Patient Stratification and Biomarker Identification

现代高通量测序（HTS）技术产生了丰富的高维生物医学数据。当研究复杂的、动态的、随机的和异质的生命和疾病系统时，样本的维度（特征的数量）通常比HTS数据中的样本的数量高得多。这些HTS数据在带来重大统计和计算挑战的同时，也为合作研究带来了独特的机会，将其转化为临床精准医学。该项目将开发新的贝叶斯方法和计算工具，用于针对两个基本生物医学应用的组合协作聚类：肿瘤分层和预测生物标志物识别。与现有的肿瘤分层和生物标志物识别的黑盒算法相比，所提出的贝叶斯组合协同聚类框架能够同时对特定肿瘤亚型进行肿瘤分层和生物标志物识别，从而可以获得对复杂疾病异质性的机制理解。捕获的分子谱模式和疾病亚型之间的相互关系可以提供对疾病细胞机制的深入了解，并具有开发个性化疾病预后和治疗策略的潜力。该项目的跨学科性质，加上计划的课程编制和推广活动，将为本科生和研究生提供极好的培训机会，该项目的核心是一个新的贝叶斯统计框架的理论和计算基础，以翻译现有的大型生物医学数据，扩展公开可用的生物医学数据集，如TCGA（癌症基因组图谱）和ICGC（国际癌症基因组联盟），以精确（个性化）疾病诊断和预后。将基于现代HTS数据为组合协作聚类（CCC）开发一类新的二进制和计数数据分析模型，以实现可重现和准确的肿瘤分层和生物标志物识别。这里，“组合”意味着每个聚类将被定义在一个特征子集上，该特征子集将经由新颖的组合分析从所有可能的特征组合中选择;并且“协作”意味着每个聚类通过其聚类成员如何表达其所选择的特征子集来协作地定义。首先，CCC不是定义聚类中心和距离度量来基于所有特征对患者进行分层，而是同时将聚类特异性特征识别为在执行患者分层时显示相似轮廓模式的生物标志物。因此，通过在从数万个特征中选择的一小部分特征上计算的患者聚类下的样本的预测可能性，它消除了“维数灾难”并大大提高了再现性。其次，它还通过将各种类型的数据链接到潜在计数来实现混合类型HTS数据的自然集成。最后，所提出的基于计数建模的推理算法仅计算非零元素，因此导致稀疏矩阵的非常有效的分析方法，通常是HTS数据的情况。除了理论和计算优点之外，CCC还提供了一个灵活的概率计算框架来识别和表征肿瘤亚型或亚克隆，这导致更有效的个性化预后和治疗设计。建议的CCC方法将首先在TCGA和ICGC数据上进行评估，然后应用于与主要研究者正在进行的癌症和免疫疾病研究的生物医学合作者的合作研究。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Modern high-throughput sequencing (HTS) technologies produce rich high-dimensional biomedical data. When studying complex, dynamic, stochastic, and heterogeneous life and disease systems, the dimensionality (number of features) of samples is typically much higher than the number of samples in HTS data. Such HTS data, while imposing significant statistical and computational challenges, bring unique opportunities for collaborative research to translate them to clinical precision medicine. This project will develop novel Bayesian methods and computational tools for combinatorial collaborative clustering targeting at two fundamental biomedical applications: tumor stratification and predictive biomarker identification. Compared to existing black-box algorithms for tumor stratification and biomarker identification, the proposed Bayesian combinatorial collaborative clustering framework enables simultaneous tumor stratification and biomarker identification for specific tumor subtypes, so that mechanistic understanding of heterogeneity of complex diseases can be obtained. The captured interrelationships between molecular profile patterns and disease subtypes may provide deep insights into disease cellular mechanisms and have the potential of developing personalized disease prognosis and therapeutic strategies. The interdisciplinary nature of this project, together with the planned curriculum development and outreach activities, will provide excellent training opportunities for both undergraduate and graduate students, preparing them with the quantitative skills in biomedical research with unprecedented big biomedical data.The core of this project is the theoretic and computational foundation of a novel Bayesian statistical framework to translate existing large-scale publicly available biomedical datasets, such as TCGA (The Cancer Genome Atlas) and ICGC (International Cancer Genome Consortium), to precision (personalized) disease diagnosis and prognosis. A new class of binary and count data analysis models will be developed for Combinatorial Collaborative Clustering (CCC) based on modern HTS data to achieve reproducible and accurate tumor stratification and biomarker identification. Here "combinatorial' means that each cluster will be defined over a subset of features, which will be selected from all possible feature combinations, via novel combinatorial analysis; and "collaborative" means that each cluster is collaboratively defined by how its cluster members express their selected subset of features. First, rather than defining cluster centers and a distance metric to stratify patients based on all features, CCC simultaneously identifies cluster-specific features as biomarkers that show similar profile patterns when performing patient stratification. Hence, with the predictive likelihood of a sample under a patient cluster calculated over a small subset of features selected from tens of thousands of them, it alleviates "the curse of dimensionality" and substantially improves reproducibility. Second, it also enables natural integration of mixed-type HTS data by linking various types of data to latent counts. Finally, the proposed count modeling based inference algorithms only compute for non-zero elements and therefore lead to extremely efficient analytic methods for sparse matrices, often the case in HTS data. In addition to the theoretic and computational merit, CCC provides a flexible probabilistic computational framework to identify and characterize tumor subtypes or subclones, which leads to more effective personalized prognosis and therapeutic design. The proposed CCC methods will be first evaluated on the TCGA and ICGC data, and then be applied to the collaborative research with the principal investigator's ongoing biomedical collaborators on cancer and immunological disease studies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.