Data-Analysis-Core

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Project Summary The Data Analysis Core (DAC) of the Minnesota Tissue Mapping Center (MN TMC) of Senescent Cells (SnCs) is co-directed by Constantin Aliferis, an expert in biomedical data science and bioinformatics modeling with a long track record in successfully leading large-scale informatics cores; Jinhua Wang, a senior bioinformaticist specializing in single cell data and integrative genomics; and Steve Johnson, an expert in data management, data quality and informatics services, and collaborative science. The DAC also includes experts in causal and predictive modeling (Dr. Kummerfeld), omics imaging (Dr. Pengo), modeling of cell dynamics and cell movement (Dr. Odde), and statistical planning, quality control measures, and statistical hypothesis testing (Dr. Guan).The overall goal of the DAC is to be the final step in the construction of a MN TMC 4D SnC atlas for healthy human adipose, liver, skeletal muscle, and ovarian tissues to be delivered (along with all supporting data) to the SenNet Consortium Organization and Data Coordinating Center (CODCC) for the construction of a human 4D SnC Atlas. Healthy human tissues over a range of ages will be analyzed with both bulk and single cell characterization and spatio-temporal analysis by the MN TMC Biological Analysis Core (BAC) using samples provided by the Biospecimen Core (BSP). The DAC will be responsible for data ingestion from BSP and BAC, mapping to interoperable and searchable ontologies, annotation, curation, and analysis. It will build data storage, search, retrieval, analysis, and visualization tools and link human specimens to a rich set of de-identified health metadata from corresponding electronic health records. In collaboration with the SenNet consortium, DAC will establish benchmarks, contribute to standard operating procedures and standards development, and ultimately prepare and share datasets with the CODCC to enable a final 4D human SnC atlas with healthy aging. DAC will leverage cutting-edge informatics, high performance computing, expert faculty, and advanced data storage and management capabilities at the University of Minnesota (UMN). It will use existing data/metadata standards, software tools, and analysis methods that ensure reproducibility and usability. DAC will deploy ontology and analytic standards widely accepted in the fields of high throughput omics and data capture, harmonization, transfer, security, and analysis and that are germane to the task of creating an atlas of SnCs. The DAC will also work closely with the other TMCs and the CODCC to develop and implement customized SenNet-wide standards fine-tuned to the needs of the consortium; data quality metrics, ontologies, and data elements; integration of imaging and omics data; analytical tools for visualization, segmentation, and annotation; SOPs; Common Data Elements (CDEs); and the network's public data sharing policy. The DAC will finally conduct a preliminary study in collaboration with Mayo Clinic (Drs. LeBrasseur and Mielke) that will comprehensively illustrate how the resulting data can be utilized to build a functional SnC atlas and establish a set of SnC biomarkers.