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.