Data Analysis Core

数据分析核心

• 批准号: 10530969
• 负责人: Kai Tan
• 金额: $ 64.28万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 依托单位国家: 美国
• 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/10530969
• 关键词: 3-Dimensional; ATAC-seq; Address; Atlases; Benchmarking; Biological Assay; Cell Nucleus; Cells; Computational algorithm; Computer software; Computing Methodologies; Coupled; Data; Data Analyses; Data Analytics; Data Set; Databases; Detection; Development; Encapsulated; Ensure; Fluorescent in Situ Hybridization; Gene Expression Profile; Genetic Transcription; Genomics; Heart; Heterogeneity; Human; Human BioMolecular Atlas Program; Image; Infrastructure; Leadership; Link; Measurement; Mediating; Metadata; Methods; Modeling; Molecular; Multiomic Data; Ontology; Organ; Output; Pathway interactions; Phase; Planet Earth; Quality Control; RNA; Regulatory Pathway; Reproducibility; Research; Research Personnel; Signal Pathway; Software Tools; Spatial Distribution; Statistical Data Interpretation; Statistical Models; Taxonomy; Technology; Tissue atlas; Tissues; Visualization; algorithm development; cell type; collaboratory; data exchange; data format; data integration; data management; design; diverse data; experience; indexing; interoperability; large scale data; member; microCT; molecular imaging; multiple omics; open source; serial imaging; software development; success; tissue mapping; virtual

PROJECT SUMMARY – DATA ANALYSIS CORE Reproducible and robust computational methods, coupled with rigorous statistical analyses, are critical for the success of the Center. We will ensure a unified approach to data analysis, integration, and management that leverages the existing infrastructure and the computational strengths of our investigators. Our data analysis effort will be divided into four tiers, with increasing level of data integration as we move up the tiers. Members of the Data Analysis Core (DAC) will be involved in all phases of project planning, from design to execution, to ensure that the data flow between the DAC and the OSPs and between our Center and other HuBMAP tissue mapping centers is well coordinated. Members of the DAC have extensive experience in algorithm development, genomics and imaging data analyses, large-scale data management, and coordination of data analytic efforts within multi-project centers. We propose the following five specific aims.1) To design and implement a pipeline for tier-one analyses using both public and in-house software tools. The pipeline will handle raw data generated using all assay types by the Center. 2) To develop and deploy computational methods for tier-two analyses. These methods will be used for the discovery and taxonomy of different cell types in the heart and inference of spatial distribution of cells and gene expression patterns in the heart. 3) To develop and deploy computational methods for tier-three analyses. These methods will be used for the discovery of transcriptional regulatory pathways contributing to spatial and temporal heterogeneity of the heart, and signaling pathways mediating interactions between different cell types in heart tissue microenvironment. 4) To construct integrated multidimensional heart atlases. Using cell-centric signatures and pathway models generated in Aims 2 and 3 as anchors, we will aggregate genomic and imaging data and metadata collected throughout the project. 5) To collaborate with the HuBMAP Integration, Visualization, and Integration collaboratory (HIVE) and other research centers of the Human Biomolecular Atlas Program (HuBMAP). We will contribute to benchmarking of software generated by HuBMAP investigators, development of common data formats, and improvement of interoperability of software tools.

项目摘要 - 数据分析核心 可重现和健壮的计算方法，再加上严格的统计分析，至关重要 中心的成功。我们将确保采用统一的数据分析，集成和管理方法 利用现有的基础设施和调查人员的计算优势。我们的数据分析 努力将分为四个层，随着我们向上升级，数据集成水平的升高。成员 数据分析核心（DAC）将参与项目计划的所有阶段，从设计到执行，再到 确保数据在DAC和OSP之间以及我们的中心与其他Hubmap组织之间 映射中心协调得很好。 DAC的成员在算法方面具有丰富的经验 开发，基因组学和成像数据分析，大规模数据管理以及数据的协调 多项目中心内的分析工作。我们提出以下五个特定目标。1）设计和 使用公共和内部软件工具实施用于层次分析的管道。管道将 处理中心使用所有测定类型生成的原始数据。 2）开发和部署计算 两层分析的方法。这些方法将用于不同单元的发现和分类法 心脏中的类型和细胞空间分布的推断和心脏中基因表达模式。 3）到 开发和部署用于三层分析的计算方法。这些方法将用于 发现有助于心脏空间和暂时异质性的转录调节途径， 和信号通路，介导心脏组织微环境中不同细胞类型之间的相互作用。 4） 构建综合的多维心脏地图集。使用以细胞为中心的特征和途径模型 在目标2和3作为锚点中生成，我们将汇总基因组和成像数据，以及收集的元数据 在整个项目中。 5）与Hubmap集成，可视化和集成合作 人类生物分子图书馆计划（Hubmap）的合作（Hive）和其他研究中心。我们将 有助于Hubmap研究人员生成的软件的基准测试，开发常见数据 格式，改善软件工具的互操作性。