Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
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cc-statistics
GitHub专为Cancer Cell论文提供生物学统计指导，核心在于明确独立生物重复n、避免伪重复、选择合适统计检验、校正多重比较及规范误差棒报告。不用于实验设计或绘图。
触发场景
样本量n定义模糊或疑似伪重复
不确定数据适用的统计检验方法
存在多重比较且未进行校正
图表或图注中误差棒含义未标注
安装
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cc-statistics -g -y
SKILL.md
Frontmatter
{
"name": "cc-statistics",
"description": "Use when defining n, choosing statistical tests, correcting for multiple comparisons, and reporting error bars for a Cancer Cell (Cell Press) manuscript. Focuses on biological statistics and avoiding pseudo-replication; it does not design experiments or build figures."
}
Biological Statistics (cc-statistics)
When to trigger
nis ambiguous, or you suspect pseudo-replication- Unsure which test fits the data and design
- Many comparisons without multiplicity correction
- Error bars / variability are unlabeled in figures or legends
Defining n (the core issue)
n= number of independent biological replicates (separate mice, independent cultures/passages, distinct patients).- Technical replicates (duplicate wells, repeat reads) describe measurement precision and do not count toward
n. - State
nfor every panel in the legend, with what one unit is ("n = 5 mice per group", "n = 3 independent experiments"). - Pooling cells from many wells of one experiment and calling it n=many is pseudo-replication — a classic Cancer Cell reviewer catch.
Choosing the test
| Design | Typical test |
|---|---|
| Two groups, continuous, ~normal | Unpaired t-test (Welch if unequal variance) |
| Two paired conditions | Paired t-test |
| Two groups, non-normal / small n | Mann-Whitney U |
| >2 groups, one factor | One-way ANOVA + post-hoc (Tukey/Dunnett) |
| Two factors (e.g., genotype × treatment) | Two-way ANOVA + correction |
| Tumor growth over time | Mixed-effects / repeated-measures ANOVA (not many t-tests per timepoint) |
| Survival / time-to-event | Kaplan-Meier + log-rank; Cox for covariates |
| Categorical / proportions | Fisher's exact / chi-square |
| Correlation | Pearson (normal) / Spearman (ranked) |
| High-dimensional omics | Model-based (DESeq2/edgeR/limma) with FDR |
Check assumptions (normality, equal variance) and report how. Prefer non-parametric or Welch corrections for small/uneven bench-scale data.
Multiple comparisons
- Few planned comparisons → Tukey / Dunnett / Holm / Bonferroni.
- Genome-wide / omics → control the false discovery rate (Benjamini-Hochberg); report adjusted p / q-values.
- Do not run many pairwise t-tests across groups or timepoints without correction.
Error bars and reporting
- Define what every error bar is: SD, SEM, or 95% CI — in the legend.
- Show data points (dot plots / superplots) rather than bar-only charts when
nis small. - Report exact p-values (not just asterisks) where feasible, plus the test and
n. - Distinguish biological-replicate variability from technical noise in plots.
- For survival, give hazard ratios with CIs, not p-value alone.
Checklist
-
ndefined per panel as biological replicates; one unit specified - No pseudo-replication (technical reps not counted as
n) - Test choice matches design; assumptions checked
- Repeated/longitudinal data analyzed with appropriate model, not serial t-tests
- Multiple comparisons corrected; FDR for omics
- Error bars defined (SD/SEM/CI) in every legend
- Exact p-values, test name, and
nreported - Data points shown for small-n comparisons
- Statistical software + versions stated (in STAR Methods)
Anti-patterns
- "n=3" meaning three technical wells of one experiment
- SEM used to make tiny error bars without saying so
- Bar charts hiding 2–3 underlying points
- Multiple t-tests across timepoints/groups, uncorrected
- Asterisks with no test, no
n, no exact p - Treating omics features as independent without FDR control
Statistics pass for Cancer Cell
Use this as a second-pass capability check. First lock the cancer context, mechanism, model system, validation chain, and translational boundary; then test whether the manuscript addresses cancer-biology reviewers who expect mechanistic oncology, translational relevance, and strong multi-modal validation.
- Primary move: Check estimand, denominator, uncertainty, multiplicity, missing data, sensitivity, and reporting standard before interpreting any result.
- Decision ledger: return
claim / evidence / blocker / next editrows so the next pass can patch the manuscript directly. - Neighbor test: compare against Cell for broader biology, Nature Cancer for oncology breadth, Clinical Cancer Research for clinical translation; if the neighboring outlet has the stronger audience claim, recommend re-routing before polishing.
- Verification floor: before submission-ready advice, re-open
resources/official-source-map.mdfor volatile rules and name the one unresolved fact that could change the recommendation.
Output format
【n definition】biological unit = ...; per-panel n stated? Y/N
【Pseudo-replication risk】none / fix: [...]
【Tests】per analysis: ...
【Multiplicity】correction used: ...
【Error bars】SD/SEM/CI defined in legends? Y/N
【Reporting gaps】exact p / data points / software version
【Next step】cc-figures-tables
版本历史
- 1839142 当前 2026-07-05 12:26
