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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要拟议的研究计划旨在开发创新的计算工具来分析和来自新兴空间分辨基因组技术的数据整合，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组学数据的可解释性有限，并且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作为另一个高维基因组数据类型，我们将以概率形式设计机器学习框架和深度生成模型来解决空间解析基因组数据的分析挑战重要的是整合多种数据模式。该框架将能够识别邻里模式被定义为具有独特的细胞状态组成的区域，来自于空间分析的整合 mRNA、蛋白质和组织学成像（目标 1）。我们将建立在基因调控建模的基础上网络开发第一个计算工具，用于从整合中推断空间变化的调节空间 ATAC-seq 和 RNA-seq（目标 2）。此外，我们将开发一种计算工具来推断具有不同拷贝数特征的细胞的空间分布及其相关基因程序（目标 3）。我们通过将我们的技术应用于与我们的合作者的多个生物系统。这些应用程序将提供新的见解了解人类和小鼠胚胎发育、大脑类器官空间模式的基础模型以及神经精神疾病、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。