Data Analysis Core

数据分析核心

基本信息

批准号：
10117950
负责人：
Jeffrey M Spraggins
金额：
$ 24.09万
依托单位：
VANDERBILT UNIVERSITY
依托单位国家：
美国
项目类别：
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/10117950
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 modality in vivo in vivo imaging inclusion criteria microscopic imaging multimodal data multimodality novel strategies open source parallelization reconstruction scaffold spatial integration whole slide imaging

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 modality in vivo in vivo imaging inclusion criteria microscopic imaging multimodal data multimodality novel strategies open source parallelization reconstruction scaffold spatial integration whole slide imaging

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项目摘要

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.