Data Science
Cancer research increasingly generates large, complex datasets that shape how we understand the disease and better treat patients. The Data Science Shared Resource provides the expertise to design rigorous studies and turn that data into meaningful results, offering integrated study design, statistical analysis, and computational analysis of large data sets. Before your study begins contact the Data Science Shared Resource for help planning your study.
New Project Consultation
Starting a new project involving one or more Shared Resources, or interested in learning more about available services? Contact Dr. Kate Hyde to schedule a consultation or complete the consultation form in Stratocore PPMS.
Request Service
Use Stratocore PPMS to request core facility services and manage scheduling and billing.
Pricing Benefits
Subsidized pricing is available to Fred & Pamela Buffett Cancer Center members. If you haven't received an email about subsidized pricing, please get in touch with cancer center administration to see if you qualify.Contact: buffettcancercenter@unmc.edu
If you conduct cancer-related research at UNMC and are not yet a member of a Cancer Center research program, visit the Fred & Pamela Buffett Cancer Center Membership page to learn more about becoming a member.
Ways to Work with the Data Science Shared Resource
| Option | What It Means |
|---|---|
| Study and Trial Design | Sample size and power calculations, and innovative early-phase clinical trial designs (including BOIN and adaptive/SMART trials), propensity-score matching, and animal study design |
| Grant and Protocol Support | Statistical and analysis plans for applications, plus feasibility review before you commit |
| Statistical Analysis and Interpretation | Across laboratory, clinical, and population-based research, including longitudinal, survival, and meta-analysis |
| High-Throughput Data Analysis | Bulk, single-cell, and spatial transcriptomics; whole-genome, exome, and ATAC sequencing; metagenomics and microbiome; and metabolomics |
| Functional Analysis and Interpretation | Pathway and enrichment analysis (IPA, GSEA, Gene Ontology) and characterization of genes and gene products |
| Protein Docking and Virtual Drug Screening | AI-based screening of large compound libraries against drug targets to identify new candidate treatments |
| Machine learning and AI | Applied to genomic and large clinical datasets |
| Data Management and Sharing | Data collection forms, quality control, data integration, and deposition to NIH repositories (GEO, SRA) |
| Education and Training | Courses, workshops, and one-on-one guidance in study design and data science methods |
| Computing and Software | The core runs analyses on dedicated high-performance computing—hundreds of CPUs, GPU servers for machine-learning work, and over a petabyte of storage—alongside the Holland Computing Center, Nebraska’s largest computing resource. Supported software includes SAS, R, SPSS, Stata, GraphPad Prism, Schrödinger, and Ingenuity Pathway Analysis. |
Citing Shared Resources
Researchers using the Fred & Pamela Buffett Cancer Center Shared Resources for publications, presentations or other scholarly outputs are required to acknowledge the use of them. Proper acknowledgment helps support the continued availability and funding of these cores.
Please acknowledge the Cancer Center Support Grant (P30 CA036727) in all publications and abstracts that have used these services.
Suggested language: Research reported in this publication was supported by the Data Science Shared Resource and the National Cancer Institute under award number P30 CA036727. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute.
Co-Directors
Chittibabu (Babu) Guda, PhD
Co-Director, Data Science Shared Resource
Director, Bioinformatics and Systems Biology
Send Email | 402-559-5954
Lynette Smith, PhD
Co-Director, Data Science Shared Resource
Director, Biostatistics
Send Email | 402-559-8114