Experimental Design (cc-study-design)
SkillMediaUse when designing or auditing the experimental plan for a Cancer Cell (Cell Press) study, orthogonal validation across in vitro, in vivo, and human tumor systems, with controls, replicates, power, randomization, and blinding. It plans design; it does not run statistics or write figures.
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Experimental Design (cc-study-design) skill
What this skill tells your AI
The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in Cancer-Cell-Skills/skills/cc-study-design/SKILL.md and read by ahel’s review.
When to trigger
- The mechanism rests on a single system (e.g., cell lines only)
- Reviewers will ask "is this true in vivo?" or "does it hold in patients?"
- Controls, replicates, or sample-size logic are unclear
- Planning mouse / PDX / organoid experiments and unsure about rigor elements
Orthogonal validation ladder
Cancer Cell expects a mechanism corroborated across independent, complementary systems. Build the strongest ladder the biology allows:
| Layer | Systems | Role |
|---|---|---|
| In vitro | Cell lines (multiple, authenticated), primary cells, co-cultures, biochemistry | Establish mechanism, gain/loss-of-function, epistasis |
| Functional genetics | CRISPR KO/KI, shRNA/siRNA with rescue, point mutants, degrons | Causality and specificity |
| In vivo | GEMM, syngeneic, xenograft, PDX, orthotopic, metastasis models | Mechanism operates in a tumor in an organism |
| 3D / ex vivo | Tumor organoids, patient-derived organoids, slice cultures, spheroids | Bridge to human, drug response |
| Human | Patient tumor samples, TMAs, scRNA-seq, public cohorts (TCGA), outcome data | Translational anchor / clinical relevance |
A Cancer Cell paper typically spans in vitro + in vivo + a human anchor. Decide early which layers carry the causal claim and which provide corroboration.
Controls (the parts reviewers attack)
- Genetic perturbation: include rescue / add-back; use ≥2 independent sgRNAs or shRNAs (or rule out off-target); non-targeting / scramble control matched to delivery.
- Pharmacology: vehicle control, dose-response, on-target genetic phenocopy, and where possible a resistant mutant or analog.
- In vivo: isotype/vehicle controls, littermate or co-housed controls for GEMMs, tumor-volume and endpoint pre-defined.
- Antibody / staining: isotype, KO/KD-validated, single-stain compensation for flow.
Replicates: biological vs technical
- Biological replicates = independent biological units (separate mice, independent cell passages/cultures, distinct patients). These define
n. - Technical replicates (duplicate wells, repeat measurements) reduce measurement noise but do not increase
n. - Report both clearly; never inflate
nwith technical replicates (seecc-statistics).
Sample size, randomization, blinding (especially animals)
- Power / sample size: justify mouse
n(effect size + variance from pilot or literature); state the basis even if informal. - Randomization: allocate animals to arms randomly (e.g., when tumors reach a set volume), not by cage convenience.
- Blinding: blind outcome assessment (tumor measurement, histology scoring, imaging quantification) wherever feasible.
- Inclusion/exclusion: pre-define humane endpoints and exclusion criteria; report all animals/samples and any exclusions.
Human-sample design
- Define cohort, inclusion/exclusion, and how samples link to outcomes.
- Power survival / association analyses; pre-specify primary comparison.
- Note IRB/consent (route to
cc-ethics-registration).
Checklist
- Mechanism validated across ≥2 orthogonal systems; causal layer identified
- In vivo evidence present (GEMM / xenograft / PDX / orthotopic) where claims require it
- Genetic perturbations include rescue and ≥2 independent reagents
- Matched controls defined for every perturbation and treatment
- Biological vs technical replicates distinguished;
n= biological units - Animal sample size justified; randomization and blinding specified
- Inclusion/exclusion criteria and humane endpoints pre-defined
- Human-sample cohort and primary comparison pre-specified
Anti-patterns
- Conclusions from a single cell line or a single system
- shRNA/CRISPR phenotype with no rescue (off-target not excluded)
- "n=3" that is three wells of one experiment (pseudo-replication)
- Mouse experiments with no randomization, no blinding, no power basis
- Therapeutic efficacy claimed without an in vivo tumor model
- Selecting samples post hoc to fit the hypothesis
Output format
【Causal claim layer】in vitro / in vivo / human
【Orthogonal systems planned】...
【Controls per perturbation】rescue + 2 reagents? vehicle/isotype?
【Replicates】biological n = ... ; technical handled separately
【Animal rigor】power basis / randomization / blinding / endpoints
【Human anchor】cohort + primary comparison
【Gaps to close before submission】...
【Next step】cc-reporting-standards (rigor reporting) or cc-statistics
Signals
- GitHub stars
- 1k
- Forks
- 155
- Last commit
- Sep 2026
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cc-study-design- Source
- github.com/brycewang-stanford/awesome-journal-skills
github.com/brycewang-stanford/awesome-journal-skills