From raw single-cell data to a publication-ready story
You invested in the experiment. You built the libraries. Then the Cell Ranger output arrived and the data sat there, waiting for someone who understands the biology.
I am a cell biologist with 70+ publications who runs scRNA-seq from raw output to paper-ready story. Not 500 genes in a table. Figures reviewers can eva.
What you get
Biological interpretation, not gene lists
Every cluster is annotated with validated markers and explained in terms of what it means for your research question. The output is a narrative, not a spreadsheet.
Publication-ready figures
UMAP, dotplots, heatmaps, trajectory plots, composition charts, sized and formatted for your target journal. TIFF at 300-600 dpi or vector PDF/SVG. Palette and typography matched to your manuscript.
Reproducible notebook
Full R or Python notebook with pinned environment. Your team can rerun every step, inspect every parameter, and extend the analysis after delivery.
Methods paragraph
Written for your manuscript. Every analytical choice justified so that reviewers can check it.
Reviewer preparation
The analysis anticipates the questions reviewers will ask. Quantitative validation of annotation. Justified resolution. Documented integration choice. Fewer revision rounds.
What sets this work apart
Reproducible by default
Biology first, then technique
Biology first, then technique
Pinned environments. Fixed random seeds. Documented parameters. Your team can rerun everything I deliver and get the same result.
Biology first, then technique
Biology first, then technique
Biology first, then technique
Most single-cell analysts learned the tools first. I learned the biology first, over twenty years and 70 publications, and then the tools. The difference shows in every annotation decision.
Validated, not assumed
Transparent AI-augmented workflow
Transparent AI-augmented workflow
Cell-type annotation is checked against canonical markers quantitatively, not just visually. When the annotation is wrong, I find it before your reviewers do.
Transparent AI-augmented workflow
Transparent AI-augmented workflow
Transparent AI-augmented workflow
I use AI tools to accelerate technical execution. Every biological decision, every interpretation, every quality check is mine. I will tell you exactly which is which.
How it works
1. You send
Your data (raw counts, Cell Ranger output, a Seurat or AnnData object, or a GEO accession), your biological question, and your target journal if you have one.
2. I scope
Within three working days you get a written scope: what the data can answer, which tier fits, exact price, and timeline. No work starts before you approve.
3. I analyze
Quality control, integration, annotation, interpretation. You receive interim results at defined checkpoints so nothing surprises you at the end.
4. We refine
Figures, methods, and narrative iterated until submission-ready. Revision rounds are included in the scope.