Data Analysis Core

数据分析核心

• 批准号: 10683322
• 负责人: Vilas Menon
• 金额: $ 36.1万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10683322
• 关键词: Adult; Biological; Biological Assay; Blood; Brain; Cells; Central Nervous System; Cerebrospinal Fluid; Classification; Data; Data Analyses; Data Coordinating Center; Data Set; Ensure; Gene Expression; Gene Proteins; Goals; Image; Indirect Immunofluorescence; Individual; Investigation; Link; Longevity; Maps; Marker Discovery; Modality; Molecular; Molecular Profiling; Molecular Target; Play; Process; Proteomics; Resolution; Role; Sampling; Sequence Alignment; Skin; Spinal Cord; Spottings; Techniques; Tissues; Universities; Work; age group; age related; biomarker identification; cell type; computerized data processing; cytokine; data harmonization; data management; data standards; data visualization; genome-wide; high resolution imaging; human tissue; image registration; imaging Segmentation; machine learning algorithm; member; molecular phenotype; multi-scale atlas; multimodal data; multimodality; novel; novel marker; senescence; single nucleus RNA-sequencing; tissue mapping; transcriptome; transcriptomics

DATA ANALYSIS CORE (DAC): PROJECT SUMMARY To characterize signatures of senescence and their functional implications in multiple human tissues across the life span, the Columbia University Senescence Tissue Mapping (CUSTMAP) Center proposes a novel combination of genome-wide and targeted molecular panels to map cellular composition with spatial context. The Data Analysis Core correspondingly plays a key role in all aspects of data processing and analysis, tissue mapping, and identification of markers of senscence, as well as data harmonization, coordination, and dissemination through the SenNet Consortium Data Coordination Center (CODCC). To achieve these goals, the DAC will use an integrative analysis approach to combine multi-modal data consisting of transcriptome- wide and targeted proteomics profiling in the tissues being examined across the adult human lifespan. This includes the three major data modalities described in the Biological Analysis Core: large-scale high-resolution Iterative Indirect Immunofluorescence Imaging (4i) data, genome-wide spatially resolved Spatial Transcriptomics (ST) data, and transcriptome-wide single-nucleus RNA-seq data. These three data modalities in concert allow for comprehensive (genome-wide) molecular characterization at single-cell resolution in space; this combination of attributes has not been demonstrated by any single experimental technique at scale currently in human tissue. By integrating these modalities using established processing and analysis workflows, the Data Analysis Core will generate maps of known and novel senescence- associated markers, senescent cells, and the effects of senescent cells on their surroundings in each tissue type. To achieve these goals, the Data Analysis Core will implement modality-specific data processing workflows, followed by cross-modal data analysis, cross-individual map-building, and identification of novel, cell type-specific senescence signatures in brain, spinal cord, and skin. This includes cross-referencing tissue-based signatures to data from ongoing efforts to identify senescence-related signatures in cerebrospinal fluid and blood, the primary biofluids associated with central nervous system and skin. Finally, the Data Analysis Core will work closely with the Administrative Core to interface with the SenNet CODCC, in order to harmonize all aspects of data management and analysis with other members of the consortium.

数据分析核心（DAC）：项目总结 为了表征衰老的特征及其在多种人体组织中的功能意义， 寿命，哥伦比亚大学衰老组织绘图（CUSTMAP）中心提出了一种新的 全基因组和靶向分子面板的组合，以映射具有空间背景的细胞组成。 相应地，数据分析核心在数据处理和分析的各个方面都发挥着关键作用， 组织绘图和识别敏感标记，以及数据协调，协调， SenNet Consortium Data Coordination Center（CODCC）数据协调中心。为了实现这些目标， DAC将使用综合分析方法来联合收割机组合多模式数据， 广泛和有针对性的蛋白质组学分析在整个成年人的生命周期中被检查的组织。这 包括生物分析核心中描述的三种主要数据模式：大规模高分辨率 迭代间接免疫荧光成像（4 i）数据，全基因组空间分辨 转录组学（ST）数据和转录组范围的单核RNA-seq数据。这三个数据 一致的模式允许在单细胞上进行全面的（全基因组）分子表征 空间分辨率;这种属性的组合还没有被任何单一的实验证明 目前在人体组织中大规模使用的技术。通过使用已建立的处理方法整合这些模式， 和分析工作流程，数据分析核心将生成已知和新的衰老地图- 相关标记物、衰老细胞以及衰老细胞对每个组织中其周围环境的影响 类型.为了实现这些目标，数据分析核心将实现特定于模态的数据处理 工作流程，其次是跨模态数据分析，跨个人地图建设，并确定新的， 脑、脊髓和皮肤中的细胞类型特异性衰老特征。这包括交叉引用 基于组织的签名的数据，从正在进行的努力，以确定衰老相关的签名， 脑脊液和血液，与中枢神经系统和皮肤相关的主要生物流体。最后， 数据分析核心将与管理核心密切合作，以与SenNet CODCC接口， 以便与合作体其他成员协调数据管理和分析的所有方面。