Data Analysis Unit

数据分析单元

基本信息

批准号：
10251228
负责人：
Kai Tan
金额：
$ 35.02万
依托单位：
CHILDREN'S HOSP OF PHILADELPHIA
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-30 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10251228
关键词：
Address Atlases Benchmarking Biological Assay Cells Clinical Clonal Evolution Computational algorithm Computer software Computing Methodologies Coupled Cytometry Data Data Analyses Data Analytics Data Coordinating Center Data Set Databases Development Encapsulated Ensure Gene Expression Profile Genomics Goals Human Hypersensitivity Image In Situ Infrastructure Leadership Link Measurement Mediating Metadata Methods Modeling Molecular Multiomic Data Network-based Ontology Output Pathway interactions Pediatric Neoplasm Phase Planet Earth Proteins Protocols documentation Quality Control RNA Reproducibility Research Research Personnel Sampling Site Software Tools Spatial Distribution Statistical Algorithm Statistical Data Interpretation Statistical Models Taxonomy Technology Trees Visualization algorithm development cancer cell cell type data exchange data format data integration data management design diverse data experience genome sequencing genomic data interoperability large scale data member neoplastic cell open source single molecule success transcriptome sequencing tumor tumor heterogeneity tumor microenvironment virtual whole genome

项目摘要

Abstract Reproducible and robust computational methods, coupled with rigorous statistical analyses, are critical for the success of CPTCA. 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. Methods used in tier one analyses are mostly open-source and developed by other groups whereas most methods used in tiers two to four analyses will be developed by our team. Specifically, the integration and visualization of longitudinal multiomics data poses many new challenges. Members of the Data Analysis Unit (DAU) have extensive experience in statistics, algorithm development, genomic data analyses, large-scale data management, and coordination of data analytic efforts within multi-project centers. We propose the following five specific aims to achieve the goals of the DAU: 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 CPTCA; 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 a tumor, inference of clonal evolution of malignant cells, and inference of spatial distribution of cells and gene expression patterns in a tumor; 3) To develop network- based methods for tier-three analyses. These methods will be used for the discovery of pathways contributing to spatial and temporal heterogeneity of the tumor; 4) To construct integrated tumor atlases. We will aggregate clinical, genomic and imaging data and metadata collected throughout the project; 5) To collaborate with the Data Coordinating Center (DCC) and other research centers of the Human Tumor Atlas Network (HTAN). Working with investigators at the DCC and other research centers, we will contribute to benchmarking of software generated by HTAN investigators, development of common data formats, and improvement of interoperability of software tools.

抽象的可重复且稳健的计算方法，加上严格的统计分析对于 CPTCA 的成功至关重要。我们将确保采取统一的方法利用现有基础设施进行数据分析、集成和管理以及我们研究人员的计算能力。我们的数据分析工作将是分为四层，随着层的上升，数据集成的水平不断提高。第一层分析中使用的方法大多是开源的，并由其他人开发而第二层到第四层分析中使用的大多数方法将由我们的团队。具体来说，纵向多组学数据的整合和可视化提出了许多新的挑战。数据分析部门 (DAU) 的成员拥有广泛的具有统计学、算法开发、基因组数据分析、大规模数据方面的经验管理和协调多项目中心内的数据分析工作。我们提出以下五个具体目标以实现 DAU 的目标： 1) 使用公共和内部的方法设计和实施用于一级分析的管道房屋软件工具。该管道将处理使用所有分析类型生成的原始数据通过 CPTCA； 2) 开发和部署第二层分析的计算方法。这些方法将用于不同细胞类型的发现和分类。肿瘤、恶性细胞克隆进化的推断、空间推断肿瘤中细胞的分布和基因表达模式； 3）发展网络- 基于方法的第三层分析。这些方法将用于发现导致肿瘤空间和时间异质性的途径； 4）构建综合肿瘤图谱。我们将汇总临床、基因组和影像数据，整个项目收集的元数据； 5）与数据协调部门合作中心（DCC）和人类肿瘤图谱网络（HTAN）的其他研究中心。我们将与 DCC 和其他研究中心的研究人员合作，为对 HTAN 研究人员生成的软件进行基准测试，共同开发数据格式，以及软件工具互操作性的改进。