Data Analysis Unit

点击翻译按钮获取中文摘要

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.