Data Analysis Core

数据分析核心

• 批准号: 10704487
• 负责人: Jeffrey M Spraggins
• 金额: $ 46.35万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10704487
• 关键词: 3-Dimensional; Address; Age; Algorithms; Atlases; Authorization documentation; Automation; Behavior; Biological Assay; Calibration; Classification; Computer software; Confidence Intervals; Cues; Data; Data Analyses; Data Set; Development; Dimensions; Disease; Eye; Funding; Glean; Goals; High Performance Computing; Histopathology; Human; Image; Ions; Link; Maps; Mathematics; Measurement; Measures; Medical Imaging; Methods; Microscopy; Mining; Modality; Modeling; Molecular; Multilingualism; Multimodal Imaging; Normal tissue morphology; Normalcy; Optical Coherence Tomography; Organ; Output; Pancreas; Patients; Phase; Play; Reporting; Resolution; Sampling; Scanning; Source; Specific qualifier value; System; Technology; Tissue imaging; Tissues; Training; Translating; Variant; Vendor; Work; analysis pipeline; authority; cell type; computerized data processing; data analysis pipeline; data exchange; data mining; data quality; data reduction; data visualization; deep learning; file format; imaging facilities; imaging modality; in vivo; in vivo imaging; inclusion criteria; microscopic imaging; multimodal data; multimodality; novel strategies; open source; parallelization; reconstruction; scaffold; spatial integration; temporal measurement; tissue mapping; whole slide imaging

PROJECT SUMMARY – Data Analysis Core. The VU-BIOMIC data analysis core (DAC) is tasked with automation of the reconstruction and subsequent analysis of the acquired multimodal eye and pancreas tissue imaging data. This is translated into four specific aims: (i) modality-specific data processing; (ii) data analysis pipeline development for 2-D and 3-D molecular tissue mapping; (iii) map construction for establishing 3-D molecular organization and function; and (iv) consortium coordination. In Aim 1, we will develop methods for preparing acquired measurement data for subsequent spatial integration, analysis, and content mining, and to remove any non-biological variation from the measurements prior to integration. In Aim 2, the DAC provides rapid cues for data quality assessment and ongoing multimodal analysis as new data is integrated into the atlases. Pre-analytically, we will develop data-derived sample inclusion criteria based on LC-MS/MS measurements, combined with gold standard histopathology, to capture what is “normal” tissue. To enable data mining of the massive 3-D multimodal spatially resolved datasets, accurate registration of multiple 2-D datasets into 3-D volumes will be essential. We will build a high-resolution mono-modal 3-D scaffold, using pre- measurement autofluorescence microscopy taken from every single tissue section. Furthermore, the 3-D data and analysis outputs, reconstructed from serial sections, will be spatially linked (by means of 3-D-to-3-D registration models) to the organ-specific in vivo and ex vivo 3-D scans to relate the acquired spectral data to more commonly encountered medical imaging modalities. Data-driven image fusion will enable the empirical discovery of potential correlative, anti-correlative, multivariate linear, and nonlinear relationships between observations in the different modalities, and also provide a framework for estimating to higher spatial resolutions as well as for out-of-sample prediction from one modality to another. The DAC will perform temporally resolved analysis of the data to find how molecular content changes with patient age. In Aim 3, the map construction phase, we will bring the third dimension to the varied data types that are measured and annotated. Data-driven image fusion will be used to advance the 3-D maps beyond what can be gleaned from one technology alone, including the application of IMS-AF-fusion-driven out-of-sample prediction. This will enable prediction of IMS observations at cutting depths where no IMS is measured. This will effectively provide predictive up-sampling of the 3-D tissue maps along the z-axis, building finer resolution 3-D volumes than would be possible with IMS alone. In Aim 4, we will develop specifications for the open file formats used in this work, multilingual parsers to ease access, and a URL-based Restful API to make (authorized) data exchange easy and accessible. We will work with the consortium to build common coordinate atlases based on in vivo images and continue the work of the currently funded project in specifying and developing easily disseminated file formats.

项目摘要-数据分析核心。VU-BIOMIC数据分析核心（DAC）的任务是 自动化所采集的多模态眼和胰腺组织的重建和后续分析 成像数据。这被转化为四个具体目标：（一）特定模式的数据处理;（二）数据分析 2-D和3-D分子组织映射的管道开发;（iii）用于建立3-D的映射构建 分子组织和功能;和（iv）财团协调。在目标1中，我们将开发方法， 准备所获取的测量数据，用于随后的空间整合、分析和内容挖掘，以及 在积分之前从测量中去除任何非生物学变化。在目标2中，发援会提供 快速提示数据质量评估和正在进行的多模态分析，因为新数据被整合到 地图集在分析前，我们将基于LC-MS/MS开发数据衍生的样品入选标准 测量，结合金标准组织病理学，以捕获什么是“正常”组织。以启用数据 海量三维多模态空间分辨数据集的挖掘，多个二维数据集的精确配准 将是至关重要的。我们将建立一个高分辨率的单峰3-D支架，使用前- 测量从每个单个组织切片获取的自体荧光显微镜。此外，三维数据 从连续切片重建的分析输出将在空间上链接（通过3D到3D 配准模型）与器官特异性体内和离体3-D扫描相关联，以将所获取的光谱数据与 更常见的医学成像模式。数据驱动的图像融合将使经验 发现潜在的相关，反相关，多元线性和非线性关系， 观测的不同方式，也提供了一个框架，估计到更高的空间分辨率 以及用于从一种模态到另一种模态的样本外预测。DAC将执行时间分辨 分析数据以发现分子含量如何随患者年龄变化。在目标3中，地图构建 阶段，我们将为测量和注释的各种数据类型带来第三个维度。数据驱动 图像融合将被用于推进三维地图，超越仅从一种技术中收集的内容， 包括IMS-AF融合驱动的样本外预测的应用。这将能够预测IMS 在没有测量IMS的切割深度处进行观察。这将有效地提供预测性上采样， 沿着z轴的3-D组织图，构建比IMS更精细的分辨率3-D体积 一个人在Aim 4中，我们将开发本工作中使用的开放文件格式的规范，多语言解析器， 易于访问，以及基于URL的Restful API，使（授权的）数据交换变得容易和可访问。我们将 与联合会合作，根据体内图像建立共同的坐标地图集，并继续开展以下工作： 目前供资的项目是指定和开发易于传播的文件格式。