Finding emergent structure in multi-sample biological data with the dual geometry of cells and features

利用细胞和特征的双重几何形状在多样本生物数据中寻找新兴结构

• 批准号: 10022130
• 负责人: Matthew John Hirn
• 金额: $ 35.32万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-23 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10022130
• 关键词: Address; Algorithms; Biological; Biomedical Technology; Cells; Cellular Structures; Collection; Complex; Data; Data Analyses; Data Science; Data Set; Dengue; Disease; Effectiveness; Environment; Foundations; Gene Expression; Gene Proteins; Genomics; Geometry; Graph; Health; Immunity; Immunotherapy; Learning; Location; Lyme Disease; Measurement; Methods; Noise; Patients; Research; Resolution; Sampling; Signal Transduction; Structure; Techniques; Time; ZIKA; algorithmic methodologies; biological heterogeneity; cohort; computerized data processing; data exploration; deep learning; denoising; driving force; multiple datasets; new technology; novel; patient population; signal processing

A fundamental question in biomedical data analysis is how to capture biological heterogeneity and characterize the complex spectrum of health states (or disease conditions) in patient cohorts. Indeed, much effort has been invested in developing new technologies that provide groundbreaking collections of genomic information at a single cell resolution, unlocking numerous potential advances in understanding the progression and driving forces of biological states. However, these new biomedical technologies produce large volumes of data, quantified by numerous measurements, and often collected in many batches or samples (e.g., from different patients, locations, or times). Exploration and understanding of such data are challenging tasks, but the potential for new discoveries at a level previously not possible justifies the considerable effort required to overcome these difficulties. In this project we focus on multi-sample single-cell data, e.g., from a multi-patient cohort, where data points represent cells, data features represent gene expressions or protein abundances, and samples (e.g., considered as separate batches or datasets) represent patients. We consider a duality or interaction between constructing an intrinsic geometry of cells (e.g., with manifold learning techniques) and processing data features as signals over it (e.g., with graph signal processing techniques). We propose the utilization of this duality for several data exploration tasks, including data denoising, identifying noise-invariant phenomena, cluster characterization, and aligning cellular features over multiple datasets. Furthermore, we expect the dual multiresolution organization of data points and features to allow us to compute aggregated signatures that represent patients, and then provide a novel data embedding that reveals multiscale structure from the cellular level to the patient level. The proposed research combines recent advances in several fields at the forefront of data science, including geometric deep learning, manifold learning, and harmonic analysis. The methods developed in this project will provide novel advances in each of these fields, while also establishing new relations between them. Furthermore, the challenges addressed by these methods are a foundational prerequisite for new advances in genomic research, and more generally in empirical data analysis where data is collected in varying experimental environments. The developed algorithms and methods in this project will be validated in several biomedical settings, including characterizing Zika immunity in Dengue patients, tracking progress of Lyme disease, and predicting the effectiveness of immunotherapy.

生物医学数据分析中的一个基本问题是如何捕获生物异质性并表征 患者队列中健康状态（或疾病状况）的复杂谱。确实，已经投入了很多努力 开发新技术，可在单个单元格上提供开创性的基因组信息集合 解决方案，释放了理解生物学的发展和驱动力的许多潜在进步 国家。但是，这些新的生物医学技术产生了大量数据，由许多数据量化 测量，通常在许多批次或样品中收集（例如，来自不同的患者，位置或时间）。 探索和对此类数据的理解是具有挑战性的任务，但是在一个层面上进行新发现的潜力 以前无法证明克服这些困难所需的巨大努力。 在这个项目中，我们关注多样本单细胞数据，例如，从多人队列中，数据点 表示细胞，数据特征代表基因表达或蛋白质丰度，以及样品（例如，被视为 单独的批次或数据集）代表患者。我们考虑构造一个二元性或相互作用 细胞的固有几何形状（例如，具有流动学习技术）和处理数据特征作为信号 （例如，使用图形信号处理技术）。我们建议将这种二元性利用用于多个数据探索 任务，包括数据降级，识别噪声不变现象，群集表征和对齐蜂窝 多个数据集的功能。此外，我们期望数据点的双重分辨率组织 功能使我们能够计算代表患者的聚合签名，然后提供新的数据 嵌入从细胞水平到患者水平的多尺度结构的嵌入。 拟议的研究结合了数据科学最前沿的几个领域的最新进展，包括 几何深度学习，多种学习和谐波分析。该项目中开发的方法将提供 在每个领域的每个领域都有新颖的进步，同时也建立了它们之间的新关系。此外， 这些方法提出的挑战是基因组研究新进步的基本先决条件，以及 在经验数据分析中，更普遍地在不同的实验环境中收集数据。这 该项目中开发的算法和方法将在几种生物医学设置中进行验证，包括 表征登革热患者中寨卡病毒免疫力，跟踪莱姆病的进度并预测有效性 免疫疗法。