Latent variable modeling of complex high-dimensional data

复杂高维数据的潜变量建模

• 批准号: RGPIN-2019-05915
• 负责人: PedrosoEstevamdeSouza, Camila
• 金额: $ 1.17万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715843
• 关键词: Latent; variable; modeling; complex; dimensional

As an Assistant Professor in Data Science my research work consists of developing novel statistical methods to analyze large complex data structures so that important scientific questions can be answered. In particular, my proposed research program is focused on the development of techniques to analyze data when crucial information is absent. While my methods can be applied to a wide range of areas in the Natural Sciences and Engineering, I will consider two main application themes over the next five years: 1) analysis of electrical load data from substations and 2) DNA and RNA sequencing data from single cells. In Theme 1, I will consider aggregated energy consumption data measured over time at substations that serve a fixed number of consumers from different types (e.g., residential, commercial and industrial). The individual consumer-level energy usage curves are not observed, only the sums of individuals' energy usage. My goal is to develop novel statistical methods to infer the typical weekly energy usage curve for each type of consumer using the substation aggregated data and additional information, such as temperature and characteristics of the substations (e.g., capacity and number of low voltage feeders). In addition, I will cluster substations into different groups according to their consumer type-specific energy usage curves. This work will allow power companies around the world to better understand energy usage in order to provide adequate energy at low cost. In Theme 2, I will analyze DNA and RNA sequencing data obtained from individual cells. Because the amount of DNA/RNA material per cell is limited, the resulting single-cell sequencing data contain technical noise and a large amount of missing information. My goal is to build new statistical tools to infer the different groups of cells comprising a tissue based on their DNA/RNA sequencing data taking into account the challenges arising from this type of technology. My work on single-cell genomics will provide scientists in various areas of biology with the adequate set of statistical tools to assess the genomic composition of cells, which will lead to a better understanding of how individual cells differentiate to form tissues and how tissues work. My proposed research team consists of two MSc and two PhD students working under Theme 1 and one MSc and two PhD students working under Theme 2. All the resulting research work under Themes 1 and 2 will be submitted for publication at high-impact scientific journals, as well as presented by the students and myself at relevant conferences. In addition, all statistical methods developed will be implemented in the free software environment R and will include user-friendly tutorial guides.

作为数据科学的助理教授，我的研究工作包括开发新颖的统计方法来分析大型复杂数据结构，以便可以回答重要的科学问题。特别是，我提出的研究计划的重点是开发在缺乏关键信息时分析数据的技术。虽然我的方法可以应用于自然科学和工程中的各个领域，但我将在未来五年内考虑两个主要应用主题：1）分析来自变电站的电气负载数据以及2）DNA和RNA测序数据单细胞。在主题1中，我将考虑随着时间的推移测量的汇总能源消耗数据，这些数据可以为不同类型的固定消费者（例如住宅，商业和工业）提供固定数量的消费者。没有观察到个别消费者级的能源使用曲线，只有个人的能量使用的总和。我的目标是开发新型的统计方法，以使用变电站汇总的数据和其他信息（例如，低电压馈线的容量和数量）来推断每种类型的消费者的典型每周能量使用曲线。此外，我将根据其消费者特定的能量使用曲线将变电站聚集在不同的组中。这项工作将使世界各地的电力公司能够更好地了解能源使用情况，以便以低成本提供足够的能源。在主题2中，我将分析从单个细胞获得的DNA和RNA测序数据。由于每个细胞的DNA/RNA材料的量受到限制，因此所得的单细胞测序数据包含技术噪声和大量缺失信息。我的目标是构建新的统计工具，以根据这种技术的挑战来推断其基于其DNA/RNA测序数据组成的各组细胞。我在单细胞基因组学方面的工作将为生物学领域的科学家提供足够的统计工具，以评估细胞的基因组组成，这将使人们更好地了解单个细胞如何区分形成组织以及组织的工作方式。我提出的研究团队由两名MSC和两个在主题1和一名MSC工作的博士学位学生组成，以及在主题2下工作的两个博士学位学生。主题1和2下的所有研究工作都将在高影响力的科学期刊上提交出版，以及学生和我本人在相关会议上提出的。此外，开发的所有统计方法将在自由软件环境R中实施，并将包括用户友好的教程指南。