We are a team of faculty and staff biostatisticians, bioinformaticians, and data scientists with diverse experience in statistical modeling, biomarker discovery, and predictive analytics.
We work collaboratively with researchers, physician scientists, and quantitative scientists across Duke’s campus to develop and apply statistical models to help meet analytical challenges of translational research. This research is diverse, ranging from observational and interventional clinical implementation trials to multi-timepoint multi-omics investigations of drug or infection exposure research. Our diversity in backgrounds coupled with our cross training of bioinformatics and biostatistics makes us a ‘one-stop-shop’ research team that manages everything from providence-conscious data intake through manuscript support.
We are available to Duke Investigators to consult on your research project or discuss collaborative opportunities with you. If you are interested, please complete an intake survey and Rachel Myers will contact you.
Core Areas of Support
Providence-conscious data management is the foundation of our research program, and we strive to ensure all data that we intake are stored and managed in accordance with responsible conduct of research principles:
- Clinical and biological data storage
- Data standardization and harmonization
- Database design and implementation
- Clinical data capture design, implementation, testing, and quality assurance
- Infrastructure support for blinded clinical adjudication
- Integration with the Biobank LIMS
We have several existing data processing pipelines, normalization procedures, and workflows in place. Additionally, we are always developing and adding new workflows as the technologies to generate such data continues to evolve. If you have something specific and it is not listed here, please feel to reach out.
- Genotyping
- DNA sequencing
- Microarray
- Gene Expression
- Single-cell RNA sequencing
- Bulk RNA-Sequencing
- Microarray
- PCR and other targeted gene expression assays
- Epigenetics
- ATAC-seq
- CHiP-seq
- Proteomics
- Targeted
- Unbiased/untargeted
- Cytokines
- Metabolomics
- Targeted (i.e. Biocrates p180 or p400)
- Unbiased
We understand that research data analysis is not a one-size-fits all. Our team utilizes a wide range to statistical and computational methodologies to tailor our analyses to the research question and data at hand:
- Observational and interventional clinical trial analysis
- Clinical outcome association analyses (i.e. genome-wide association studies (GWAS), transcriptome-wide association studies (TWAS))
- Functional enrichment analyses
- Biological pathway associations
- Biomarker and digital-biomarker discovery
- Classifier development and validation
- Diagnostic assay design and validation
- Longitudinal data analysis
- Complex Bayesian statistical modeling
- Supervised and unsupervised machine learning
- Multi-omic analysis
- Non-parametric and parametric exploratory data-analysis
Meet Our Team
Data Management Team
- Christina Nix - Analyst Programmer
Statistical Analysis Team
- Rachel Myers, PhD - Research Scientist
- Ilya Zhbannikov, PhD – Biostatistician
- Cameron Miller, PhD – Biostatistician
- Nicholas O’Grady, MS – Biostatistician
- Nathan Bihlmeyer, PhD - Biostatistician
Photo Courtesy: National Human Genome Research Institute