Data Analysis Core

数据分析核心

• 批准号: 10201588
• 负责人: Jeffrey M Spraggins
• 金额: $ 62.31万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-24 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10201588
• 关键词: 3-Dimensional; Address; Algorithms; Atlases; Automation; Biological; Biological Assay; Calibration; Complex; Computer software; Confidence Intervals; Cues; Data; Data Analyses; Data Set; Development; Dimensions; Disease; Documentation; Feedback; Glean; Goals; High Performance Computing; Human BioMolecular Atlas Program; Image; Imaging technology; Ions; Lead; Link; Magnetic Resonance Imaging; Maps; Mass Spectrum Analysis; Mathematics; Measurement; Measures; Medical Imaging; Memory; Methodology; Methods; Microscopy; Mining; Mission; Modality; Molecular; Molecular Analysis; Multilingualism; Multimodal Imaging; Normal tissue morphology; Phase; Play; Process; Reporting; Research Personnel; Resolution; Sampling; Source; Specific qualifier value; System; Technology; Tissues; Training; Universities; Variant; Vendor; Visualization; Work; base; cell type; computational platform; computerized data processing; computing resources; data analysis pipeline; data exchange; data mining; data quality; data visualization; driving force; experience; file format; high dimensionality; human tissue; inclusion criteria; insight; microscopic imaging; molecular imaging; multimodality; novel strategies; open source; parallelization; reconstruction; scaffold; spatial integration; spectrograph

Project Summary The VU-BIOMIC Data Analysis Core (DAC) is tasked with both pre-analytical and analytical challenges. The investigators of the DAC have broad range of experience working with high-dimensional imaging mass spectrometry data sets, even leading the field in new methodologies to process such data. In addition, the DAC team has pioneered methods to combine high spatial resolution microscopy and 3-dimensional data sets with IMS. However, BIOMIC will require that the DAC architect a platform that integrate all of the pre-analytical and analytical computational approaches into a complete computational platform capable of processing the data generated by the Characterization pipeline efficiently. A primary driving force behind all work carried out will be automation of the reconstruction and subsequent analysis. This will allow plug-and-play analysis of new datasets as they are generated. As the experimental workflow is expansive, it is important that the DAC provide rapid feedback of metrics to track data quality and provide analyses that lead to biological insight.

项目总结