Scientific advisory, study design and training
When you want the study to be analysis-ready from day one - or you need a clear plan to triage and salvage an existing dataset.
- Omics-aware study design: endpoints, confounders, batch strategy, and “what will we conclude if X happens?”
- Practical power/feasibility input (including what not to do with the sample size you have)
- Methods + analysis sections for grants/proposals and internal documents
- Reviewer-response support: targeted re-analysis, sensitivity checks, and figure refits
- Hands-on training/workshops using your own data and your preferred stack
Typical format: short consults, project “audit” days, or recurring advisory (e.g., 1-2 days/month). Deliverables: a decision-oriented analysis plan, written notes, and (when relevant) training materials.
Multi-omics and spatial data analysis
From raw data to figures and tables you can publish or make decisions from - end-to-end or as a focused module.
- Bulk RNA-seq; sc/snRNA-seq (QC, annotation, differential states, compositional effects)
- Spatial transcriptomics (10x Visium / Visium HD: QC, deconvolution/colocalization, region-level biology)
- LC-MS proteomics / metabolomics (QC, differential abundance, pathway context)
- Multi-omics integration and network/pathway-level interpretation
- Reproducible pipelines (Snakemake/containers where they add real portability)
Typical format: a scoped analysis module or full workflow with checkpoints. Deliverables: reproducible code, clean tables, publication-ready figures, and a concise written results narrative aligned to your question.
Translational biomarker and target discovery
For programs that need mechanism, stratification, or actionable signals - not just lists of differentially expressed features.
- Phenotype-linked signatures and candidate panels designed for follow-up validation
- Network/module analysis tied to readouts (disease severity, hemodynamics, outcomes)
- Predictive modeling with clear validation logic and interpretable outputs
- Target/biomarker prioritization with a transparent rationale and next-step experiments
Typical format: focused discovery sprint or embedded support within a translational program. Deliverables: ranked candidates/panels, model outputs with validation summaries, and concrete next-step recommendations.
How I work
Intake and alignment
- Scope, success criteria, constraints (timeline, compute, data access), and what decisions the analysis must support
Data audit and analysis plan
- Quick QC/feasibility readout, then a written plan: contrasts, covariates, validation strategy, and deliverables
Iterative checkpoints
- Short cycles with visible outputs (figures/tables), decision notes, and course-corrections early rather than late
Delivery and handoff
- Reproducible repo + walkthrough. You should be able to rerun, extend, and defend the work
Support through submission / follow-up
- Targeted additions for rebuttals, extra sensitivity checks, and “one more figure” moments