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和的创新计算工具 从新兴的空间分辨基因组技术中整合数据，这些技术有可能发现 与环境相互作用在正常发育和疾病中的作用。现有的分析工具 分析空间OMIC数据的可解释性有限，并且无法整合多模式 数据。 利用我们在单细胞数据的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 以及深层生成模型，以应对空间分辨的基因组数据和 重要的是整合了多种数据模式。该框架将使社区识别 定义为具有细胞状态独特组成区域的模式，从空间分析的整合 mRNA，蛋白质和组织学成像（AIM 1）。我们将基于建模基因调节的基础 网络开发第一个从空间变化的调节的计算工具从 空间ATAC-SEQ和RNA-SEQ（AIM 2）。此外，我们将开发一种计算工具来推断 具有不同拷贝数谱及其相关基因程序的细胞的空间分布（AIM 3）。 我们通过应用我们的技术在 与我们的合作者一起多个生物系统。这些应用将提供新颖的见解 了解人和小鼠胚胎发育中的空间模式的基础，脑器官 模型以及诸如神经精神疾病，胶质母细胞瘤和乳腺癌等疾病系统。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 解锁有关细胞类型的空间组织，它们的相互作用和机制的新颖见解 各种生物系统。