Analysis Plan

This section outlines your planned analyses and statistical reasoning. It serves as a roadmap for turning raw data into interpretable results.

Core Questions

• What are the main hypotheses or questions you are testing?
  • Example: “Do expression outliers cluster in enhancer-linked genes in the disease group?”
• Are there secondary analyses, subgroup evaluations, or exploratory components?

Design Justification

• Why is your study design appropriate for the analysis?
  • Example: “We use a quasi-experimental design with matched controls to reduce confounding.”
• Are you using repeated measures, batch correction, or stratification?

Methods Overview

• What statistical or computational methods will you use?
  • Examples: DESeq2, mixed-effects models, PCA, Bayesian models, permutation tests
• How will you control for multiple testing or reduce dimensionality?

Dependencies and Assumptions

• What assumptions does your analysis rely on (e.g., normality, independence, linearity)?
• Are there known data limitations (e.g., batch effects, missing values, unbalanced groups)?
• How will you test or account for violations?

Power and Sample Considerations (optional)

• Is there a power analysis? If not, why?
• Minimum sample size required to detect a meaningful effect?