Tensor decomposition methods for multi-omics immunology data analysis

用于多组学免疫学数据分析的张量分解方法

• 批准号: 10655726
• 负责人: Steven H. Kleinstein
• 金额: $ 24.78万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-07 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10655726
• 关键词: Address; Algebra; Algorithms; Area; Biological; Clinical; Communities; Complex; Data; Data Analyses; Data Reporting; Data Set; Development; Dimensions; Face; Gene Expression; Genes; Goals; Immune; Immunology; Infection; Joints; Mathematics; Methodology; Methods; Multiomic Data; National Institute of Allergy and Infectious Disease; Outcome; Pathogenicity; Pattern; Platelet Activation; Principal Component Analysis; Process; Proteomics; Recovery; Research; Research Design; Research Personnel; Resolution; Risk; Sampling; Source; Structure; Technology; Testing; Time; Tissues; Vaccination; Variant; age group; algorithm development; complex data; data complexity; data integration; design; high dimensionality; improved; indexing; metabolomics; multiple omics; novel; novel strategies; patient subsets; prevent; programs; response; tool; transcriptome sequencing; two-dimensional; usability; vector

PROJECT SUMMARY/ABSTRACT Immune profiling studies continue to increase in complexity, with multi-omic designs that encompass additional dimensions such as time, tissue, and spatial profiling becoming more commonplace. Unsupervised dimensionality reduction has been a widely used and valuable approach for extracting and understanding the major sources of variation in previous studies, but popular methods such as Principal Components Analysis (PCA) and Non-negative Matrix Factorization (NMF) cannot support these increases in data complexity, nor can existing multi-omic embedding methods, which are designed for static datasets. It is critical that algorithms be developed that incorporate the complex data structures inherent in state-of-the-art immune profiling multi-omics studies that include additional dimensions (e.g., time or space) in order to capture multi-resolution components of vaccination and infection. The goal of this project is to develop algorithms based on tensor frameworks - which are extensions of matrices beyond two dimensions. Tensors naturally represent complex data without flattening on any variable, and tensor decompositions can identify multi-index patterns of variation, analogous to PCA or NMF in higher dimensions. Tensor decomposition methodology is an active area of research in the applied mathematics community, but is under-developed for application to immune profiling data, and current methods face critical challenges that prevent them from being directly applied in immunology studies. This project brings together computational immunology and applied mathematics researchers to strengthen and develop novel approaches of tensor decomposition in order to make them beneficial to the immunology community. Aim 1 will reframe a tensor decomposition problem into a regularized NMF problem, thereby allowing tools developed for matrix analysis to be used on tensor data, and furthermore will extend the new algorithm to handle multi-omics data that has a temporal or spatial component. Aim 2 will directly improve tensor decomposition approaches by developing novel metrics for tensor decomposition quality, and by extending a multi-omic embedding method into the tensor space using a novel tensor-algebra. The resulting algorithm will be able to generate components associated with data that can include both multi-omic and multi-dimensional (e.g. time, space, tissue, etc.) designs. These components can be analyzed for association with clinical features and outcomes, allowing for discovery of novel biological mechanisms. The proposed project will result in a suite of complementary algorithms that will aid the immunology community in understanding complex pathogenic and treatment/vaccination processes using the increasingly complex study designs that are becoming common to immune profiling studies.

项目概要/摘要 免疫分析研究的复杂性不断增加，多组学设计涵盖了额外的 时间、组织和空间分析等维度变得更加普遍。无监督 降维已成为一种广泛使用且有价值的方法来提取和理解 先前研究中变异的主要来源，但流行的方法（例如主成分分析） (PCA) 和非负矩阵分解 (NMF) 无法支持数据复杂性的增加，也无法支持 现有的多组学嵌入方法是为静态数据集设计的。算法至关重要 开发融合了最先进的免疫分析多组学固有的复杂数据结构 包括额外维度（例如时间或空间）以捕获多分辨率成分的研究 疫苗接种和感染。 该项目的目标是开发基于张量框架的算法 - 张量框架是矩阵的扩展 超越二维。张量自然地表示复杂的数据，无需对任何变量进行扁平化，并且张量 分解可以识别多指标变化模式，类似于更高维度的 PCA 或 NMF。 张量分解方法是应用数学界的一个活跃的研究领域，但 免疫分析数据的应用尚不成熟，当前的方法面临着严峻的挑战 阻止它们直接应用于免疫学研究。该项目汇集了计算 免疫学和应用数学研究人员加强和开发张量的新方法 分解以使它们对免疫学界有益。目标 1 将重构张量 将问题分解为正则化 NMF 问题，从而允许为矩阵分析开发的工具 用于张量数据，此外还将扩展新算法以处理具有 时间或空间成分。目标 2 将通过开发直接改进张量分解方法 张量分解质量的新颖指标，并将多组学嵌入方法扩展到张量 使用新颖的张量代数的空间。由此产生的算法将能够生成相关的组件 数据可以包括多组学和多维（例如时间、空间、组织等）设计。这些 可以分析成分与临床特征和结果的关联，从而发现新的 生物学机制。拟议的项目将产生一套补充算法，这将有助于 免疫学界使用以下方法了解复杂的致病和治疗/疫苗接种过程 日益复杂的研究设计在免疫分析研究中变得越来越常见。