Wet-lab context, quantitative training, and hands-on pipeline experience for biomedical labs that need code, analysis, and documentation to match their research workflow.
Prefer async? Email hello@laminarbio.com with your dataset, goal, timeline, and current tools.
MOVAT-stained aortic section image processing with a documented QuPath workflow.
QuPath pipelines for stained tissue sections, microscopy images, segmentation, morphometry, and batch export. Workflows are documented and reproducible, with a deployed example for MOVAT-stained aortic sections and atherosclerosis lesion quantification.
R and Python workflows for experimental data cleaning, joins, QC checks, statistical modeling, and reproducible tables. Common analyses include logistic regression, count models, survival analysis, and mixed-effects models for labs with repeated analysis needs.
Structured systems for records that are currently split across spreadsheets: animal colony records, genotyping logs, cage assignments, weaning schedules, sample inventories, and shared lab trackers. Built in a stack your institution can support and your lab can maintain.
A 2–3 hour working session to map where AI, automation, or conventional scripting can realistically improve a research workflow. You leave with a prioritized roadmap, expected inputs and outputs, and a clear first implementation project.
Why Laminar
Cardiovascular biology at UVA's Sonkusare Lab — TRP channels, calcium signaling, atherosclerosis. Real bench experience.
Neuroscience + Statistics at UVA. Logistic, Poisson, NB regression. R and Python daily.
Certified EMT. We understand clinical data, patient-facing context, and translational stakes — not just the bench.
We ship tools that non-developers actually use. Strategy is step one, not the deliverable.
Institution-friendly
Laminar is designed for institutional reality: workflows can be delivered in local files, documented scripts, or institution-approved stacks your lab can actually maintain. We scope around IT restrictions, shared-drive habits, handoff to trainees, and compliance-sensitive work so the result is usable inside your lab, not just impressive in a demo.
How it works
You describe the problem, we ask the right questions. We figure out if there's a fit and what a scoped engagement looks like, including data inputs, expected outputs, timeline, and handoff needs.
Within 48 hours, you get a plain-language proposal: what we'll build, what you provide, how long it takes, what it costs. Fixed-fee for projects, hourly for retainers.
Code is documented. Pipelines are annotated. You or a grad student should be able to re-run everything six months from now without calling us. That's the standard we hold.
Engagement formats
Project-based work for image analysis, data cleaning, statistical workflows, and documented scripts. Typical range: $100–$175 / hr or fixed-fee after scoping.
Retainer-friendly support for labs with recurring analysis, reporting, or workflow maintenance needs. Typical range: $95–$150 / hr.
Focused 2–3 hour review of a workflow, dataset, or lab process with a written implementation plan. Typical range: $1,200–$2,500 / session.