Data Analysis Core

项目摘要-数据分析核心。VU-BIOMIC数据分析核心(DAC)的任务是 获得的多模式眼和胰腺组织的重建和后续分析的自动化 成像数据。这可转化为四个具体目标：(1)针对特定模式的数据处理；(2)数据分析 2-D和3-D分子组织图谱的流水线开发；(3)建立3-D图谱 分子的组织和功能；以及(Iv)联合体的协调。在目标1中，我们将为 为后续的空间集成、分析和内容挖掘准备已获取的测量数据，并 在积分之前，从测量中去除任何非生物变化。在AIM 2中，DAC提供了 数据质量评估和持续的多模式分析的快速提示 地图集。分析前，我们将在LC-MS/MS的基础上制定数据衍生的样品包含标准 测量，结合黄金标准的组织病理学，以捕捉什么是“正常”组织。启用数据 海量三维多模式空间分辨数据集的挖掘，多个二维数据集的精确配准 变成3-D卷将是必不可少的。我们将构建一个高分辨率的单模3D支架，使用Pre- 测量每个组织切片上的自体荧光显微镜。此外，3-D数据 从连续截面重建的分析输出将在空间上链接(通过3-D到3-D 配准模型)与体内和体外器官特定的3-D扫描相关联 更常见的医学成像方式。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 在不同模式下的观测，并提供了一个估计到更高空间分辨率的框架 以及用于从一种形态到另一种形态的样本外预测。DAC将执行临时解析 分析数据以找出分子含量如何随着患者年龄的变化而变化。在目标3中，地图的构建 在此阶段，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 图像融合将被用来推动3D地图的发展，而不仅仅是一种技术所能收集到的东西， 包括IMS-AF融合驱动样本外预测的应用。这将实现对IMS的预测 在没有测量到IMS的切割深度进行观测。这将有效地提供预测性上采样 3-D组织图沿着z轴，建立了比IMS更精细的分辨率3-D体积 独自一人。在Aim 4中，我们将为这项工作中使用的开放文件格式开发规范，多语言解析器以 轻松访问，以及基于URL的RESTful API，使(授权的)数据交换变得容易和可访问。我们会 与该联盟合作，根据活体图像建立共同的坐标地图集，并继续 目前获得资助的项目是指定和开发易于传播的文件格式。

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.