Data Analysis Core

The TriState SenNet Data Analysis Core (DAC) will construct biomarker and map datasets generated from the lung and heart tissue, and related biosamples and biofluids. The DAC will deliver robust and standardized data to the SenNet Consortium Organization and Data Coordinating Center (CODCC). The overall goals of the Data Analysis Core are to store, analyze, and model the SenNet biomarker and map datasets generated by the Biospecimen Core and Biological Analysis Core using robust computational packages and analysis methods; to work together with other TMCs and the CODCC to develop network-wide open data and metadata standards; and to conduct cross-validation of assays within and across TMCs.

TriState SenNet TMC data warehouse set up through data extraction, quality control, and harmonization.

These stated goals will be achieved by 1) providing biostatistical and informatical support to ensure the integrity of data collection, storage, transfer, and harmonization within the TriState SenNet TMC; 2) constructing a lung and heart senescence atlas using advanced and robust statistical, computational, and artificial intelligence algorithms; and 3) collaborating closely with CODCC and other TMCs on developing open data and metadata standards and cross-validating assays within and across TMCs.


Contact PI: Ziv Bar-Joseph, PhD

Professor of Computational Biology
Carnegie Mellon University

Dr. Bar-Joseph is an outstanding expert in computational and systems biology. He has ample expertise in the field of spatial and longitudinal multi-omics data, modeling scRNA-Seq temporal data, and on the use of deep learning methods for the analysis of spatial high throughput biological data. He leads the HuBMAP computational tools center, which is focused on the development of methods for the processing, analysis, and integration of high throughput single cell data from multiple tissues and organs, which he will apply and expand upon as PI of the DAC of this TriState SenNet TMC.

Co-Lead PI: Dongmei Li, PhD

Associate Professor of Clinical and Translational Research
University of Rochester

Dr. Li received her PhD in Biostatistics from The Ohio State University, where her doctoral research focused on resampling-based multiple testing procedures with applications to microarray data analysis. Dr. Li current research focuses on differential analysis methods and multiple testing procedures in genomic data analysis especially in methylation and transcriptome data analysis. Dr. Li has more than ten years of experience conducting statistical methodology research, teaching, mentoring public health students, and providing consulting services for biomedical research. Dr. Li has served as co-investigators and biostatisticians on multiple national grants including NIH R21, P01, P20, P30, and U54 grants on biomedical research. Dr. Li’s research interests include epigenetics and genetic differential analyses, exposure assessment, modeling multiple correlated exposures, multilevel models, structure equation models, social media data mining, and using genetic biomarkers to determine disease susceptibility. Currently, Dr. Li serves as the program director of the Biomedical Data Science Certificate Program and organizes quarterly CTSI analytics colloquiums to promote analytic skills in the biomedical research performed at University of Rochester. Dr. Li is currently the director of the Biostatistics and Informatics Core for the WNY Center for Research on Flavored Tobacco (CRoFT) products, a partnership between Roswell Park Comprehensive Cancer Center and the University of Rochester, examining effects of flavorings in e-cigarettes through in vivo, in vitro, health, behavioral, and marketing studies. CRoFT is one of 9 NIH/FDA-funded Tobacco Centers for Regulatory Science nationally. Dr. Li is also a MPI of an NIH R21 grant focus on examining the epigenetic changes associated with flavored electronic cigarette use. Dr. Li brings extensive experience conducting statistical methodology research, genetic and genomic data analysis experience, and developing new methods for gene differential data analysis.

Co-Lead PI: Qin  Ma, PhD

Associate Professor of Biomedical Informatics
Ohio State University

Dr. Ma leads the Bioinformatics and Mathematical Biosciences Lab at OSU. His technical expertise includes analyzing high-throughput sequencing data, with an emphasis on single-cell sequencing data analysis using artificial intelligence. He has unique expertise in the study of gene regulatory mechanisms, development of computational models and tools for scRNA-seq data analyses and integration, graph neural networks algorithms, and web server development. Dr. Ma leads a multi-institutional R01 study on the construction of cell-type specific gene co-regulation signatures based on single-cell transcriptomics. In addition, Dr. Ma is the MPI of the Coordinating Center for a U24 consortium grant focused on Participant Engagement and Cancer Genome Sequencing. Dr. Ma will contribute his outstanding expertise critical for the development of tissue mapping to this TriState SenNet TMC.

Dongjun Chung, PhD

Associate Professor of Biomedical Informatics
Ohio State University

Dr. Chung provides expertise in statistical and computational methods for integrative analysis of genetic and genomic data with biomedical big data sets. He is a member of the Pelotonia Institute for Immuno-Oncology and the OSUCCC-James. His research program focuses on the development of statistical and computational methods for integrative analysis of genetic and genomic data and biomedical big data.