Genomics Statistical Analysis Advisor
Design genomic studies statistically sound and interpret results with confidence
What You Can Do
This skill guides you through the complete statistical workflow for genomic research—from study design and sample size determination to analysis method selection and result interpretation. You get evidence-based recommendations for handling genomic data's unique challenges: multiple testing correction, batch effects, and small-sample inference. Each recommendation includes the reasoning, assumptions, and limitations so you can make informed choices for your research.
Features
Get recommendations for sample size, replication design, and control group selection tailored to your genomic research question
Calculate statistical power and minimum sample sizes needed to detect biologically meaningful effects with your data characteristics
Navigate family-wise error rate, false discovery rate, and other corrections appropriate for testing across thousands of genomic features
Choose between parametric, non-parametric, and specialized genomic methods based on your data distribution and experimental design
Identify batch effects in your design and recommend appropriate removal or adjustment methods (ComBat, SVA, surrogate variables)
Distinguish statistically significant results from biologically meaningful findings and contextualize effect sizes in genomics literature
Get recommendations on log-transformation, normalization, and scaling methods appropriate for different genomic data types
Learn which plots best communicate your results: volcano plots, Manhattan plots, heatmaps, Q-Q plots, and domain-specific visualizations
Example Output
Study Design for RNA-seq Comparison:
✓ Recommended sample size: 8–10 per group (quality variance estimate ~0.15) ✓ Suggested method: DESeq2 with LFC shrinkage (for stable effect sizes) ✓ Multiple testing: FDR ≤ 0.05 (Benjamini-Hochberg correction) ✓ Critical assumption: Negative binomial distribution fit; verify with diagnostic plots
Batch Effect Mitigation Plan for Microarray:
✓ Detected sources: Scan date (p < 0.001), sample preparation batch (p = 0.04) ✓ Recommended approach: ComBat parametric adjustment (sufficient samples per batch) ✓ Validation strategy: Replicate 5 positive control probes in next batch ✓ Trade-off awareness: ComBat assumes comparable true signal across batches
Statistical Result Interpretation:
✓ Your p-value < 0.001 passes FDR correction, but effect size (Cohen's d = 0.28) is small ✓ Biological interpretation: Validate with orthogonal method before functional follow-up ✓ Visualization suggestion: Forest plot with confidence intervals shows effect uncertainty better than p-values alone
What's Included
- Study Design Templates: Pre-built templates for case-control, longitudinal, and intervention genomic studies with complete statistical specifications
- Calculation Tools & Formulas: Ready-to-use power calculations, sample size formulas, and correction factor references for genomic analyses
- Method Decision Flowcharts: Interactive logic flows to choose between statistical methods based on your data distribution, study design, and genomic context
- Interpretation Frameworks: Structured approaches for translating p-values, confidence intervals, and effect sizes into biological conclusions
- Batch Effect Toolkit: Detection methods, adjustment strategies, and validation approaches for common batch sources in genomics platforms
- Visualization Best Practices: Guidance on choosing and creating publication-quality plots that effectively communicate genomic findings
Who It's For
- Genomicists and bioinformaticians designing experiments
- Biostatisticians supporting genomic research programs
- Wet-lab researchers planning genomic or gene expression studies
- PhD students and postdocs in genomics or computational biology
- Data analysts reviewing or troubleshooting genomic analysis pipelines
Best For
- Designing genome-wide association studies (GWAS) and QTL mapping
- Planning RNA-seq, microarray, or qPCR experiments with adequate power
- Selecting appropriate statistical methods for genomic data analysis
- Interpreting p-values, effect sizes, and multiple testing corrections
- Detecting and mitigating batch effects in genomic datasets
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