Data Analysis Core

数据分析核心

基本信息

批准号：
10530866
负责人：
Jeffrey M Spraggins
金额：
$ 53.9万
依托单位：
VANDERBILT UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-15 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10530866
关键词：
3-Dimensional Age Anatomy Atlases Biological Biological Assay Cell Cycle Cells Chemicals Classification Clinical Communities Computer software Cues Custom Data Data Analyses Data Set Data Sources Development Dimensions Dropout Eye Gender Gene Expression Goals Human Human BioMolecular Atlas Program Image Imaging technology Individual Kidney Label Learning Machine Learning Magnetic Resonance Imaging Maps Masks Measurement Medical Imaging Memory Methods Microscopy Mining Mission Modality Modeling Molecular Molecular Analysis Multimodal Imaging Nature Nephrons Organ Pancreas Population Procedures Process Production Proteomics Published Comment Quality Control Reporting Research Resolution Resources Sampling Scheme Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Speed Technology Tissue Donors Tissue Sample Tissues Universities Variant Visualization Work X-Ray Computed Tomography automated segmentation base cell type cohort computerized data processing computing resources data mining demographics human tissue imaging facilities insight interest member multimodal data multimodality multiple omics open source spatial integration spatiotemporal tissue mapping transcriptomics

项目摘要

PROJECT SUMMARY – Data Analysis Core. The characterization pipelines built by the VU Biomolecular Multimodal Imaging Center (BIOMIC) Data Analysis Core (DAC) will produce a multimodal molecular atlas of kidney across multiple scales in 2-D and 3-D, comprising rich and varied molecular data from MALDI IMS, MxIF, CODEX, spatial and non-spatial proteomics/ transcriptomics, united by common spatial coordinate space. The DAC will build on our previous developments, including spatial segmentation for both acquisition and analysis, comprehensive molecular identification across modalities, automated data mining across modalities by spatial masks using clinical and temporal cues, and situating data within an “average” human kidney developed from 1000s of medical images. In Aim 1 we will further develop and scale our modality-specific processing methods to prepare the different measurement types for subsequent spatial integration, multimodal analysis, and content/cross-omic relationship mining. Aim 2 will focus on expanding and scaling our characterization pipeline, bringing together the broad array of distinct imaging and -omics measurement types individually processed by the methods in Aim 1. By integrating these varied datasets spatially, temporally, as well as content-wise, we will empirically mine them for cross-modal and molecular-functional relationships. These technologies will establish both 2-D and 3-D multimodal tissue maps that concurrently report hundreds to thousands of biomolecules at cellular resolutions, together with corresponding functional and cell type annotations. In Aim 3 we will compose all spatial and molecular information into a reference atlas by expanding the spatio-temporal mapping of molecular variation, organization, and function beyond single tissue block analyses (as in Aim 2) to multi-block and multi-donor tissue cohorts. Finally, Aim 4 will enable continued coordination with the HuBMAP consortium and HIVE team members by integrating our analyses into the HIVE’s ASCT+B tables for interactive exploration, visualization, and searchable analyses.

项目摘要 - 数据分析核心。 VU生物分子多模式成像中心（生物元）数据分析构建的表征管道核心（DAC）将在二-D和3-D中多个尺度上产生肾脏的多模式分子图集，完成来自MALDI IMS，MXIF，CODEX，空间和非空间蛋白质组学/的丰富和多样性分子数据/ 转录组学，由共同的空间坐标空间联合。 DAC将基于我们以前的发展，包括用于采集和分析的空间分割，跨越跨越的分子鉴定使用临床和临时提示，通过空间面具跨模态的模态，自动数据挖掘，以及将数据置于“平均”人类肾脏中的数据，从1000秒的医学图像发展。在目标1中，我们将进一步开发并扩展我们的特定于方式处理方法，以准备不同的测量类型为了进行随后的空间整合，多模式分析和内容/交叉摩西关系挖掘。 AIM 2意志专注于扩展和扩展我们的表征管道，将一系列广泛的独特阵列融合在一起通过AIM 1中的方法单独处理成像和 - 组的测量类型。通过整合这些方法在空间，临时以及内容方面，我们将紧急挖掘它们以进行跨模式和分子功能关系。这些技术将同时建立2-D和3-D多模式组织图同时，在细胞分辨率上同时报告了数百千分之一的生物分子相应的功能和细胞类型注释。在AIM 3中，我们将组成所有空间和分子通过扩展分子变化，组织，超出了单个组织阻滞分析（如AIM 2中），对多块和多谱系组织队列的功能。最后，AIM 4将通过Hubmap Consortium和Hive Team成员继续协调将我们的分析集成到Hive的ASCT+B表中，以进行交互式探索，可视化和可搜索分析。