Written for people who have the data and have to make decisions about it. No sign-up, nothing gated. RSS.
Honest price ranges for scRNA-seq analysis in 2026 — in-house, core facility, freelancer and service provider — and where the hidden costs are.
Why fixed cut-offs like "<5% mitochondrial" fail on some tissues, how to set data-driven thresholds, and what to report so reviewers stop asking.
How to annotate single-cell clusters so the labels survive peer review — the failure modes of each approach and a workflow that combines them.
What deconvolution, spatially variable genes and niche analysis actually do, which platform needs which method, and where spatial analyses usually go wrong.
Twelve questions that separate a deliverable you can publish from a folder of plots — data handling, reproducibility, revisions, and who owns what.
A practical triage for the five re-analysis requests reviewers actually make, what each one really costs you in time, and how to answer without redoing the whole project.
Why per-cell differential expression inflates false positives, when pseudobulk is the right answer, and exactly how to run it.
A practical route from a GEO accession to an analysis you can trust — what to check in the metadata, when raw counts matter, and the traps in combining datasets.
The file formats, metadata and written questions that turn a two-week analysis into a three-day one — with a copy-paste checklist.
What each spatial technology actually measures, what resolution really means in practice, and which question each one can and cannot answer.
How to tell a real rare population from a doublet cluster or ambient contamination, which tools to use, and the order to run them in.
How to choose an integration method, how to tell over-correction from correction, and the checks that show your biology survived.
Ligand–receptor scoring is a hypothesis generator, not evidence of interaction. How to run it defensibly and how to report it.
Timelines for each stage of a single-cell project, what actually causes delays, and how to compress a deadline without cutting corners.
A file-by-file walkthrough of the 10x Cell Ranger output directory — what each one is for, which to keep, and which to send when someone asks for your data.
What to put in the budget for analysis, how to justify it to a review panel, and the line items applicants forget until the money is already allocated.
Power thinking for single-cell studies: how many cells per sample, how many reads per cell, and why the number of subjects matters more than either.
An honest comparison of the four ways to get single-cell data analysed — including when each one is the wrong choice.
Ten checks you can run in an afternoon — without being a bioinformatician — to find out whether a delivered single-cell analysis will survive review.
A practical comparison of the two dominant single-cell toolkits — where each is genuinely better, where results differ, and what that means if you switch mid-project.
Monocle3, Slingshot and RNA velocity produce a trajectory from almost any dataset. How to tell whether yours reflects biology.
How to look for a 0.5% population in single-cell data, and the four artefacts that produce a convincing rare cluster that is not real.
Consent, controlled access, transfer mechanics and deletion — the practical checklist for getting human sequencing data to someone outside your institution without creating a problem.