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结构在多样本生物数据中寻找新兴结构

基本信息

  • 批准号:
    9903563
  • 负责人:
  • 金额:
    $ 38.07万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 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

项目摘要

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生物学特性。 患者队列中复杂的健康状态(或疾病状况)谱。事实上,已经投入了很大的努力, 开发新技术,在单个细胞中提供突破性的基因组信息收集 解决方案,解锁许多潜在的进展,了解生物的进展和驱动力, states.然而,这些新的生物医学技术产生了大量的数据, 测量,并且通常以许多批次或样品收集(例如,来自不同的患者、位置或时间)。 探索和理解这些数据是具有挑战性的任务,但新发现的潜力在一个水平上, 以前不可能的证明了克服这些困难所需的相当大的努力。 在这个项目中,我们专注于多样本单细胞数据,例如,来自多患者队列,其中数据点 表示细胞,数据特征表示基因表达或蛋白质丰度,以及样本(例如,视为 单独的批次或数据集)代表患者。我们考虑一个二元性或相互作用之间构建一个 细胞的固有几何形状(例如,利用流形学习技术)并将数据特征作为信号处理 (e.g.,利用图形信号处理技术)。我们建议利用这种二元性的几个数据勘探 任务,包括数据去噪、识别噪声不变现象、聚类特征和对齐细胞 多个数据集上的特征。此外,我们期望数据点的双重多分辨率组织, 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 嵌入揭示了从细胞水平到患者水平的多尺度结构。 拟议的研究结合了数据科学前沿几个领域的最新进展,包括 几何深度学习、流形学习和谐波分析。该项目中开发的方法将提供 这些领域的每一个都有新的进展,同时也在它们之间建立了新的关系。而且 这些方法所解决的挑战是基因组研究取得新进展的基本先决条件, 更一般地在经验数据分析中,其中在变化的实验环境中收集数据。的 在这个项目中开发的算法和方法将在几个生物医学环境中得到验证,包括 描述登革热患者的寨卡免疫力,跟踪莱姆病的进展,并预测有效性 免疫疗法。

项目成果

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Matthew John Hirn其他文献

Matthew John Hirn的其他文献

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{{ truncateString('Matthew John Hirn', 18)}}的其他基金

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
  • 财政年份:
    2019
  • 资助金额:
    $ 38.07万
  • 项目类别:
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:
    10475044
  • 财政年份:
    2019
  • 资助金额:
    $ 38.07万
  • 项目类别:

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