Project Summary – Core D (Data Analysis Core) The Data Analysis Core (DAC) will build on available computational infrastructure to fulfill all the needs for advanced data analysis and informatics of the Yale SenNet TMC. It will also provide support for data storage and sharing, metadata management and referencing, web-based data portable and query, and rigorous statistical analyses to accomplish the center’s missions. The DAC will be led by Dr. Yuval Kluger, who provides prominent expertise bioinformatics and computational analysis of multi-dimensional data including sequencing and imaging data. He has an extensive track record of methodology development and collaborative work in studies analyzing data generated by genomics and proteomics technologies in major NIH consortia. Co-I Gershkovich is directing Yale Clinical Pathology data infrastructure and management. He will provide prominent expertise in building pipelines, frameworks and portals for integrating biospecimen metadata with biological analysis data from multiple modalities including omics and images. He will develop a workflow tracking system for specimen collection, annotation, analytic data, and sharing. The DAC will develop pipelines to analyze data from 4 assay modalities in the Biological Analysis Core (BAC). Specifically, the DAC will carry out the key functions for (1) data processing – to set up a set of multiscale and multimodal data processing pipelines and quality assurance protocols, (2) data analysis – to establish an integrated single-cell & spatial data analysis workflow and biomarker signature inference, (3) map construction – to construct tissue molecular and cellular maps via image co-registration and multi-omics co-referencing, and (4) consortium coordination – to work with the CODCC and other TMCs to develop common database, visualization, and query. The DAC will generate the Molecular Maps and Cellular Maps of cellular senescence in human lymphoid organs, define cell type-specific senescence biomarkers, dissect senescent cell heterogeneity, and delineate the interactions with senescence- associated tissue environments. It will also assemble data standards, facilitate dissemination, and integrate our data with existing or newly generated atlases and maps from SenNet and other NIH Consortia. These are highly valuable next-generation biomedical data resources for a broad community of basic, translational, and clinical research.