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A Spatially Resolved Molecular Atlas of Human Endothelium

A Spatially Resolved Molecular Atlas of Human Endothelium
人类内皮细胞的空间分辨分子图谱
批准号：
10411809
负责人：
Long Cai
金额：
$10.0万
依托单位：
CALIFORNIA INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
项目状态：
已结题
起止时间：
2018-09-20 至 2022-06-30
来源：
https://reporter.nih.gov/project-details/10411809
关键词：
AddressAlgorithmsAreaAtlasesBiologicalBiological AssayCategoriesCellsCharacteristicsCollaborationsCollectionColorCommunitiesComputer AnalysisComputer softwareCytometryDataData SetDetectionDevelopmentDimensionsDiseaseEndotheliumGenomicsHealthHumanHuman BioMolecular Atlas ProgramImageImage AnalysisImmunofluorescence ImmunologicIndividualInvestigationLabelLibrariesMachine LearningMechanicsMethodsModelingMolecularNuclearPositioning AttributeProteomeProteomicsResearch PersonnelResolutionSemanticsSoftware ToolsSpottingsTechnologyTissue imagingTissuesTrainingUnited States National Institutes of HealthWorkbasecell typecrowdsourcingdata modelingdeep learningdesignfallsgenomic datahigh dimensionalityhuman imaginghuman tissuehuman-in-the-looplearning strategymembermultiplexed imagingnext generationopen sourceoperationpreservationsingle cell analysissingle moleculeterabytetranscriptometranscriptomicstwo-dimensional

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英文摘要
Project Abstract Recent advances in imaging and genomics are revolutionizing our understanding of human tissues. As these two technologies become more intertwined and high-throughput, there is an increasing need for scalable methods to interpret these data with single-cell resolution. In this Supplement, we seek to build on ongoing work to develop scalable and accurate algorithms for single-cell analysis of spatial genomics data. We propose to develop annotation software for annotating individual cells, their cell types, and functional tissue units in multiplexed imaging data. This software will be essential to produce training data to power the next generation of deep learning models for analyzing multiplexed imaging data.
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