Machine learning methods for interpreting spatial multi-omics data

用于解释空间多组学数据的机器学习方法

• 批准号: 10585386
• 负责人: Elham Azizi
• 金额: $ 45.26万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-17 至 2028-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10585386
• 关键词: ATAC-seq; Address; Area; Bayesian learning; Brain; Cell Communication; Cells; Chromatin; Collaborations; Communities; Complex; Computer Models; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Dependence; Development; Disease; Disease Progression; Dissociation; Dropout; Embryo; Embryonic Development; Environment; Foundations; Future; Gene Expression; Gene Expression Profile; Genes; Genome; Genomic Segment; Genomics; Glioblastoma; Goals; Histology; Human; Image; In Situ; Joints; Knowledge; Location; Maps; Measurement; Messenger RNA; Methods; Modality; Modeling; Molecular; Morphologic artifacts; Multiomic Data; Mus; Mutation; Neighborhoods; Noise; Normal Range; Organoids; Outcome; Pattern; Physiology; Proteins; Proteomics; Regulation; Regulator Genes; Research; Resolution; Role; Spatial Distribution; Statistical Models; System; Techniques; Technology; Tissue imaging; Tissues; Tonsil; Work; analytical tool; biological systems; brain tissue; cell type; computer framework; computerized tools; data integration; design; epigenomics; experience; flexibility; functional genomics; gene regulatory network; genetic manipulation; genomic data; high dimensionality; histological image; innovation; insight; machine learning framework; machine learning method; machine learning model; malignant breast neoplasm; multidimensional data; multimodal data; neuropsychiatric disorder; novel; open source; programs; spatial integration; supervised learning; tool; transcriptome sequencing; transcriptomics; user friendly software

PROJECT SUMMARY The proposed research program aims to develop innovative computational tools for the analysis and integration of data from emerging spatially-resolved genomic technologies, which have the potential to uncover the role of interactions with the environment in normal development and disease. Existing analytical tools for analyzing spatial omics data are limited in their interpretability and are not capable of integrating multi-modal data. Leveraging our extensive experience in computational modeling of single-cell data as another high-dimensional genomic data type, we will design machine learning frameworks in the form of probabilistic and deep generative models to tackle the analytical challenges of spatially-resolved genomic data and importantly integrate multiple data modalities. This framework will enable the identification of neighborhood patterns defined as regions with a unique composition of cell states, from the integration of spatial profiling of mRNAs, proteins, and histological imaging (Aim 1). We will build on a foundation of modeling gene regulatory networks to develop the first computational tool for inferring spatially-varying regulation from the integration of spatial ATAC-seq and RNA-seq (Aim 2). Additionally, we will develop a computational tool for inferring the spatial distribution of cells with distinct copy number profiles, and their associated gene programs (Aim 3). We highlight the versatility and generalizability of our computational methods by applying our techniques in multiple biological systems with our collaborators. These applications will provide novel insights in understanding the basis of spatial patterns in human and mouse embryonic development, brain organoid models, as well as disease systems such as neuropsychiatric disorders, glioblastoma and breast cancer. Our goal is to disseminate our computational toolbox as open-source software to the broader genomics community to unlock novel insights about the spatial organization of cell types, their interactions, and mechanisms in various biological systems.

项目摘要 拟议的研究计划旨在开发用于分析的创新计算工具， 整合来自新兴的空间分辨基因组技术的数据，这些技术有可能揭示 在正常发育和疾病中与环境相互作用的作用。现有的分析工具， 分析空间组学数据的可解释性有限，无法整合多模态 数据 利用我们在单细胞数据计算建模方面的丰富经验， 高维基因组数据类型，我们将以概率的形式设计机器学习框架 和深度生成模型，以应对空间分辨基因组数据的分析挑战， 重要是集成了多种数据形式。这个框架将使识别邻里 模式被定义为具有独特的细胞状态组成的区域，从空间分布的整合， mRNA、蛋白质和组织学成像（Aim 1）。我们将建立在基因调控模型的基础上， 网络开发的第一个计算工具，用于推断空间变化的监管，从整合 空间ATAC-seq和RNA-seq（目的2）。此外，我们将开发一个计算工具，用于推断 具有不同拷贝数谱的细胞的空间分布及其相关基因程序（Aim 3）。 我们强调的通用性和普遍性，我们的计算方法，通过应用我们的技术， 与我们的合作者一起研究多个生物系统。这些应用程序将提供新的见解， 了解人类和小鼠胚胎发育中空间模式的基础，脑类器官 模型，以及疾病系统，如神经精神疾病，胶质母细胞瘤和乳腺癌。我们 我们的目标是将我们的计算工具箱作为开源软件传播给更广泛的基因组学社区 解开关于细胞类型的空间组织，它们的相互作用和机制的新见解， 各种生物系统。