# Data Analysis Core

> **NIH NIH U54** · UNIVERSITY OF MINNESOTA · 2024 · $590,636

## Abstract

PROJECT SUMMARY
The Data Analysis Core (DAC) of the Midwest Murine Tissue Mapping Center (MM-TMC) of Senescent Cells
(SnCs) will build upon extensive computational resources to meet all the Center’s informatics and data analytics
needs. The DAC MPIs are Jinhua Wang, an expert in genome informatics and bioinformatics modeling with a
long track record in successfully building and leading informatics cores for center grants at the University of
Minnesota (UMN), and Alexander Misharin, a senior researcher specializing in single cell data and integrative
genomics at Northwestern University. The DAC also includes experts in cross-species comparative genomics,
transcriptomic analysis, gene pathway modeling, genetic biomarker selection, proteomics/metabolomics data
analysis and tool development, deep neural network modeling of cellular imaging, and statistical planning, quality
control measures, and statistical hypothesis testing. The overall goal of the DAC is to perform multi-scale and
multi-modality analysis of the collected data (for SnC identification, novel SnC biomarker discovery, SnC spatial
pattern discovery, and SnC cellular states dynamics modeling) and prepare it for the SenNet Consortium
Organization and Data Coordinating Center (CODCC) for the construction of a murine SnC 4D Atlas. The DAC
MPIs will work closely with Yale and UMN human TMC DAC centers to inform and help advance the ongoing
analysis carried out in human tissues. Notably, DAC MPI Wang has had productive collaborations with Yale and
UMN human DAC directors for more than a decade. Select murine tissues (liver, lung, skeletal muscle, and
adipose) over a range of ages, strains, and perturbations will be analyzed with both bulk and single cell profiling
and spatial analysis by the MM-TMC Biological Analysis Core (BAC). The DAC will be responsible for data
ingestion from the BAC, mapping to interoperable and searchable ontologies, annotation, curation, and analysis.
We will 1) build or use the best practice tools for data storage, search, retrieval, analysis, and multi-omics data
joint embedding; 2) create a comprehensive murine SnC biomarker set, including both known and novel
biomarkers; and 3) establish a cross-comparison procedure to bridge murine and human SnC analyses. In
collaboration with the SenNet Consortium, the DAC will establish benchmarks, contribute to standard operating
procedures and standards development, and prepare and share datasets with the CODCC to enable a murine
SnC 4D atlas. The DAC will leverage cutting-edge informatics, high performance computing, expert faculty, and
advanced data analytics, data storage and management capabilities at MM-TMC institutions. 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 including: 1) data quality metrics, ontologies, and data elements; 2)
integration of imaging and omics data analytical tools for visualizat...

## Key facts

- **NIH application ID:** 10908554
- **Project number:** 5U54AG079754-03
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** Jinhua Wang
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $590,636
- **Award type:** 5
- **Project period:** 2022-08-02 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10908554

## Citation

> US National Institutes of Health, RePORTER application 10908554, Data Analysis Core (5U54AG079754-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10908554. Licensed CC0.

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