Data Analysis Core

数据分析核心

• 批准号: 10385187
• 负责人: Vilas Menon
• 金额: $ 40.17万
• 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/10385187
• 关键词: Data; Analysis; Core

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）：项目摘要 表征衰老的特征及其在多个人体组织中的功能含义 寿命，哥伦比亚大学衰老组织映射（Custmap）中心提出了一本小说 全基因组和靶向分子面板结合使用空间环境绘制细胞组成。 数据分析核心在数据处理和分析的各个方面都起着关键作用， 组织映射，鉴定感应标记，以及数据协调，协调和 通过Sennet联盟数据协调中心（CODCC）传播。为了实现这些目标， DAC将使用综合分析方法结合由转录组组成的多模式数据 在成人人类寿命中检查的组织中的范围和有针对性的蛋白质组学分析。这 包括生物分析核心中描述的三种主要数据方式：大规模高分辨率 迭代间接免疫荧光成像（4i）数据，全基因组空间分辨的空间 转录组学（ST）数据和整个转录组的单核RNA-Seq数据。这三个数据 共同的方式可以在单细胞时进行全面的（全基因组）分子表征 太空的分辨率；任何单个实验尚未证明这种属性的组合 目前在人体组织中的规模技术。通过使用既定的处理来整合这些模式 和分析工作流程，数据分析核心将生成已知和新型衰老的地图 相关的标记物，衰老细胞以及衰老细胞对每个组织周围环境的影响 类型。为了实现这些目标，数据分析核心将实施特定于模式的数据处理 工作流程，然后进行跨模式数据分析，跨个体映射构建以及新颖的识别， 大脑，脊髓和皮肤中的细胞类型特异性衰老特异性特征。这包括交叉引用 基于组织的签名，来自持续努力的数据，以识别与衰老相关的特征 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， 数据分析核心将与行政核心紧密合作，以与Sennet Codcc接口， 为了将数据管理和分析的各个方面与财团的其他成员进行协调。