Basic Sciences & Bioinformatics
Unit Lead
Tristan Grogan, MS, is a biostatistician specializing in biomarker discovery/validation, multi-omic integration, and the statistical design and analysis of clinical and translational studies. His work spans a wide array of medical domains, including oncology, cardiology, anesthesiology, pediatrics, urology, and metabolic disease. Tristan collaborates closely with investigators to support study design, data integration, and statistical modeling, particularly in projects involving high-throughput sequencing, imaging biomarkers, and patient-reported outcomes.
He has co-authored over 200 peer-reviewed publications and led statistical efforts in numerous prospective clinical trials, retrospective cohort studies, and NIH-funded biomarker validation studies. Tristan's methodological expertise includes survival analysis, CART models, interrupted time series/segmented regression analysis, and generalized linear mixed models. He is also experienced in bioinformatics procedures using R/Bioconductor, particularly in applications involving gene expression, methylation, and pathway analysis.
About
The Basic Sciences & Bioinformatics Unit specializes in statistical and computational support for laboratory-based and molecular research. We collaborate with investigators working across genomics, proteomics, transcriptomics, metabolomics, microbiome, single-cell, and epigenetic datasets. Our team provides expertise in high-dimensional data analysis, integrative modeling, and rigorous bioinformatics workflows designed to uncover mechanistic insights and biological pathways.
Whether you are generating data through sequencing, mass spectrometry, or high-throughput screens, we partner from experimental design through to publication. Our services include quality control, normalization, differential expression testing, pathway analysis, integration with public or EHR datasets for outcome prediction, and downstream visualization and interpretation. We also support cutting-edge analytical approaches including gene co-expression networks, dimensionality reduction (e.g., PCA, WGCNA), clustering, machine learning, and statistical modeling of multi-omic data.
Services
- Experimental Design Consultation
- Power/sample size calculations for omics studies
- Replication strategies and batch effect considerations
- Selection of appropriate modeling framework (e.g. linear mixed models, Cox, penalized)
- Methodological Strategy and Bioinformatics Aims
- Sequence and assay-specific pre-processing
- RNA-seq, miRNA, methylation, WGS/WES, proteomics, microbiome
- Normalization and batch correction
- Sample outlier detection and filtering
- Differential Expression
- DESeq2, edgeR, limma, etc.
- Dimensionality Reduction
- PCA, WGCNA, Elastic net, LASSO
- Clustering and Classification
- Hierarchical clustering, k-means, SVM, random forests
- Gene ontology (GO) and pathway enrichment
- Network Analysis
- WGCNA
- Integration with public datasets or EHR data
- Heatmaps, KM Curves, and publication-ready figures
- Outcome modeling and predictive analysis
- Association between molecular features and outcomes
- Combining clinical and omic features in risk models
- Statistical and methodological write-up
- Data visualization for publication and presentation
- Reviewer response and revision support
Team
