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Data Analysis, Integration, and Sharing Core

数据分析、整合、共享核心

基本信息

批准号：
10531006
负责人：
Robert michael Angelo
金额：
$ 46.44万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2026-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10531006
关键词：
Age Algorithms Anatomy Atlases Biological Assay Blood Vessels Bone Marrow Cell Communication Cell Differentiation process Cells Censuses Classification Collaborations Complex Data Data Analyses Erythroid FAIR principles Gender Gene set enrichment analysis Genes Genetic Transcription Hematopoiesis Hematopoietic Histologic Human Human BioMolecular Atlas Program Image Length Link Location Lymphatic Lymphoid Manuals Metadata Mitotic Modality Morphology Myelogenous Neighborhoods Organ Patients Peer Review Phagocytes Phenotype Polysaccharides Population Principal Component Analysis Quality Control Race Resolution Sampling Shapes Spatial Distribution Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Structure Tissue atlas Tissue imaging Tissues Work base bone cell type cohort computerized data processing computerized tools data pipeline data quality data sharing deep learning algorithm demographics differential expression feature extraction human tissue interoperability large datasets multimodality nano-string progenitor programs protein expression stem tissue mapping tool transcriptome transcriptomics

项目摘要

Project Summary - Data Analysis Core In close collaboration with the bone marrow Tissue Mapping Core Organ Specific Project (OSP), the data analysis core (DAC) will oversee sample metadata curation, low level data processing and quality control, spatial feature extraction, cross-assay integration, tissue atlas construction, and HIVE data delivery. We will leverage vetted, peer-reviewed data pipelines for scalable processing and analysis of multiplexed tissue imaging data previously that adhere to FAIR data sharing standards and have been constructed to prioritize interoperability and transparency. The DAC will use these tools to construct a multiscale, multimodal atlas of human bone marrow that is contextualized by age, gender, race, and anatomic site. Highly multiplexed subcellular imaging using MIBI-TOF will provide a comprehensive census of bone marrow structure, cell types, and functional states that will be coregistered with de novo spatial glycan imaging and tissue transcriptomics performed on adjacent serial sections using MALDI and Nanostring DSP, respectively. Whole cell segmentation together with single channel protein expression data will be leveraged for cell clustering and annotation in high resolution MIBI-TOF images. Meanwhile, pixel-based approaches will be employed to identify complex phenotypic features in a cell- agnostic manner that can be readily deployed on both MIBI-TOF and MALDI data. These data will be interrogated with respect to cell or pixel-based features to identify composite tissue neighborhoods consisting of recurring combinations of either feature type. Lastly, image co-registration across each modality will permit the spatial distributions of cell subsets characterized by MIBI to be correlated with local glycan composition and transcriptional programs.

项目总结-数据分析核心