Model-based clustering of high dimensional discrete data and compositional data

高维离散数据和组合数据的基于模型的聚类

• 批准号: RGPIN-2021-03812
• 负责人: Dang(Subedi), Sanjeena
• 金额: $ 1.53万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742730
• 关键词: Model; based; clustering; dimensional; discrete

Identification and analysis of population heterogeneity is the primary goal of cluster analysis. Clustering allows us to summarize data into homogenous groups or clusters of observations where observations within each cluster are more similar than between clusters. Model-based clustering, which utilizes mixture models, has been increasingly used in the last two decades. A finite mixture model assumes that the population consists of a finite mixture of subpopulations or components, each of which can be represented by a parametric model. Depending on the nature of the data, appropriate distributions are chosen to model various kinds of data. The long-term goal of the applicant's research program is to develop cutting edge statistical algorithms for biological datasets to gain a comprehensive understanding of biological processes. While there has been a big explosion in data generation in bioinformatics, efficiently analyzing these complex biological data sets still remains a challenge. During the tenure of the grant, the applicant's research will focus on developing novel model-based clustering algorithms for high dimensional and longitudinal discrete data and compositional data. Challenges with such analyses include the large scale nature of omics datasets, heterogeneity in the data types, and a lack of scalability of statistical models that capture the various characteristics of the underlying data generating mechanisms. The proposed research focuses on developing efficient and scalable statistical models for clustering high dimensional data and longitudinal omics data. In addition to these model developments, several open problems in model-based clustering will be explored. When the number of components in a mixture model is unknown, the EM algorithm or a variant thereof must be used in conjunction with a model-selection criterion so that every possible number of components is explored. This can be computationally expensive and using different model selection criteria on the same data can result in selection of different `best' fitting models. To date, most work on the model selection issue has focused on continuous data. Some work will focus on investigation of different model-selection criteria for discrete data and compositional data in general. Investigation of an efficient parameter framework will also be a key focus. The proposed work presents a major step forward in mixture model-based clustering of multivariate discrete data and compositional data. While the proposed research will utilize bioinformatics datasets for applications, they are applicable to discrete and compositional data encountered in many fields such as text analytics, sport analytics, etc. These algorithms will be made available publicly as open source user-friendly R packages. The impact of this work will be primarily in the computational statistics and bioinformatics communities.

群体异质性的识别和分析是聚类分析的主要目标。聚类允许我们将数据汇总到同质组或观察组中，其中每个组内的观察值比组间的观察值更相似。利用混合模型的基于模型的聚类在过去二十年中得到了越来越多的应用。有限混合模型假定总体由子总体或组成部分的有限混合组成，每个子总体或组成部分都可以用参数模型表示。根据数据的性质，选择适当的分布来对各种数据建模。申请人研究项目的长期目标是开发生物数据集的前沿统计算法，以全面了解生物过程。虽然生物信息学的数据生成已经有了很大的发展，但有效地分析这些复杂的生物数据集仍然是一个挑战。在资助期间，申请人的研究将集中于开发新的基于模型的聚类算法，用于高维和纵向离散数据和成分数据。这种分析的挑战包括组学数据集的大规模性质、数据类型的异质性，以及缺乏捕获底层数据生成机制的各种特征的统计模型的可扩展性。本研究的重点是开发高效、可扩展的统计模型，用于高维数据和纵向组学数据的聚类。除了这些模型的发展之外，还将探讨基于模型的聚类中的几个开放问题。当混合模型中的组件数量未知时，必须将EM算法或其变体与模型选择准则结合使用，以便探索每个可能的组件数量。这在计算上可能是昂贵的，并且在相同的数据上使用不同的模型选择标准可能导致选择不同的“最佳”拟合模型。迄今为止，大多数关于模型选择问题的工作都集中在连续数据上。一些工作将集中于研究离散数据和一般组成数据的不同模型选择标准。研究一个有效的参数框架也将是一个重点。本文提出的工作在多元离散数据和组合数据的混合模型聚类方面迈出了重要的一步。虽然拟议的研究将利用生物信息学数据集进行应用，但它们适用于许多领域中遇到的离散和组合数据，如文本分析，运动分析等。这些算法将作为开源的用户友好的R包公开提供。这项工作的影响将主要在计算统计和生物信息学社区。