Data Analysis Core

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Data Analyses Core: Abstract The Data Analysis Core (DAC) will provide the expertise to manage, model, and analyze data generated by the Duke Tissue Mapping Center (TMC), so as to deliver senescent cell signatures and tissue maps of senescent cells to the CODCC. This will be achieved by pragmatic and innovative execution of the mandated aims – Data Processing, Data Analysis, Map Construction and Consortium Coordination. The Data Processing team will be responsible for the implementation of a cloud native platform on Microsoft Azure that will process data according to FAIR (Findable, Accessible, Interoperable and Reusable) guidelines. The team will coordinate with the Biospecimen Core to document potential confounding variables such as race, sex, live or cadaveric tissue origin; with the Biological Analysis Core for their expertise in optimal pipelines for processing specific assay data, and with the Data Analysis team to ensure the data is collected in a format that is interoperable with downstream analysis. The Data Analysis team will be responsible for the characterization of senescent cell signatures that takes into account the heterogeneity of senescent cells and the dynamics of transitioning to the senescent state. The team will use an iterative strategy to identify senescent cells, identify and expand associated markers, and characterize the functional signature conditional on the biological context of the senescent cell. The team will make use of organoids for initial characterization of the dynamic signature, using these putative signatures to identify rare senescent cells in normal tissue (including biofluids), and refine the putative signature by re-weighting signature elements based on the extent to which they occur in senescent cells in normal tissue. The Map Construction team will be responsible for the development of spatial maps of senescent cells in normal tissue using advanced computational biology methods, innovative tensor analysis approaches and modern deep learning architectures. The team will integrate data from spatial assays (multiplexed immunohistochemistry images, Visium spatial transcriptomics, and Cartana in-situ sequencing) and single cell assays (combined scRNA-seq and scATAC-seq) to build spatial maps predictive of the transcriptome, epigenome and secretome of senescent cells in normal tissue from lung, heart, muscle and skin. The team will also develop a dashboard tool that interfaces with Azure for map visualization, and evaluate the accuracy of these maps using cross-validation, data sets from public repositories, and maps constructed by other TMCs. The Consortium Coordination team will be responsible for annotation of all data sets using terms from NIH Common Data Elements Repository and OBO Foundry ontologies, creation of policies for data and metadata capture, definition of practices for reproducible analysis including use of containers and workflow orchestration scripts, and conversion of data, models, pipelines and tissue maps to interoperable formats for uploading to the CODCC. The team will also lead the collaborative development, with other interested parties from the SenNet consortium, of a Senescent Cell Ontology.