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

生物医学数据分析中的一个基本问题是如何捕捉生物异质性并表征 患者队列中健康状况(或疾病状况)的复杂频谱。事实上，已经在以下方面投入了大量努力 开发新技术，在单个细胞上提供突破性的基因组信息收集 解决方案，释放了在理解生物学的进展和驱动力方面的许多潜在进展 各州。然而，这些新的生物医学技术产生了大量的数据，由无数 通常以许多批次或样本(例如，从不同的患者、地点或时间)收集。 探索和理解这样的数据是具有挑战性的任务，但在一定程度上可能会有新的发现 以前不可能实现的目标证明了克服这些困难所需的巨大努力是合理的。 在这个项目中，我们专注于多样本单细胞数据，例如，来自多患者队列的数据，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，被认为是 单独的批次或数据集)表示患者。我们认为构建一个 单元的固有几何(例如，使用多种学习技术)以及将数据特征作为其上的信号进行处理 (例如，使用图形信号处理技术)。我们建议将这种二元性用于几个数据探索 任务，包括数据去噪、识别噪声不变现象、集群表征和对齐蜂窝 多个数据集上的功能。此外，我们预计数据点和数据的双重多分辨率组织 允许我们计算代表患者的聚合签名的功能，然后提供新的数据 嵌入法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、流形学习和调和分析。该项目中开发的方法将提供 在每个领域都取得了新的进展，同时也在它们之间建立了新的关系。此外， 这些方法应对的挑战是基因组研究取得新进展的基本前提，以及 更普遍的是，在经验数据分析中，数据是在不同的实验环境中收集的。这个 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 登革热患者寨卡病毒免疫特征、莱姆病进展追踪及疗效预测 免疫疗法。

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.