Data Analysis Core

数据分析核心

• 批准号: 10480794
• 负责人: Jeffrey M Spraggins
• 金额: $ 30.77万
• 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/10480794
• 关键词: 3-Dimensional; Address; Age; Algorithms; Atlases; Automation; Behavior; Biological Assay; Calibration; Classification; Computer software; Confidence Intervals; Cues; Data; Data Analyses; Data Set; Development; Dimensions; Disease; Eye; Funding; Glean; Goals; Gold; 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; base; cell type; computerized data processing; data analysis pipeline; data exchange; data mining; data quality; 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; 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 重建和随后分析获得的多模式和胰腺组织的自动化 成像数据。这将转化为四个具体目的：（i）特定于模式的数据处理； （ii）数据分析 二-D和3-D分子组织映射的管道发展； （iii）建立3-D的地图构造 分子组织和功能； （iv）财团协调。在AIM 1中，我们将开发用于 准备获得的测量数据，以进行随后的空间集成，分析和内容挖掘，并 在集成之前，从测量结果中删除任何非生物差异。在AIM 2中，DAC提供 随着新数据集成到该数据质量评估和正在进行的多模式分析的快速提示 地图集。预先分析，我们将基于LC-MS/MS制定数据衍生的样本纳入标准 测量，结合金标准组织病理学，以捕获“正常”组织。启用数据 大规模3-D多模式的空间分辨数据集的挖掘，多个二维数据集的准确注册 将3-D体积成分至关重要。我们将使用Pre- 测量自动荧光显微镜从每个组织截面取。此外，3-D数据 从串行部分重建的分析输出将在空间上链接（通过3-D到3-D 注册模型）到特定器官特异性体内和Ex Vivo 3-D扫描，以将获得的光谱数据与 更常见的是医学成像方式。数据驱动的图像融合将实现经验 发现潜在的相关，反相关，多元线性和非线性关系 不同方式的观察，还提供了一个框架，以估算更高的空间分辨率 以及从一种形态到另一种方式的样本外预测。 DAC将执行暂时解决 分析数据以查找分子含量如何随患者年龄变化。在AIM 3中，地图构造 阶段，我们将将第三维带入测量和注释的各种数据类型。数据驱动 图像融合将用于将3-D地图推进到仅仅从一项技术中收集到的东西之外 包括应用IMS-AF融合驱动的样本外预测。这将实现IMS的预测 在未测量未进行IMS的切割深度的观察。这将有效地提供预测的上采样 沿Z轴的3-D组织图，构建最终分辨率3-D体积比IMS所能 独自的。在AIM 4中，我们将开发此工作中使用的开放文件格式的规格，多语言解析器 简化访问权限，以及基于URL的RESTFUL API，使（授权）数据交换变得容易且易于访问。我们将 与财团合作，根据体内图像建立共同的坐标图表，并继续工作 当前资助的项目指定和开发易于传播文件格式。