Write Methods Section

Generate or update the methods section based on actual scripts in the pipeline. Links code directly to reproducible documentation.

When to Use

• •After completing a script in scripts/
• •When methods.md is out of sync with code
• •Before writing results section
• •As part of documentation review

Prerequisites

• •At least one script exists in scripts/
• •Scripts have docstrings/documentation
• •dvc.yaml reflects the pipeline structure (if using DVC)

Common Mistakes to Avoid

1. •

   Vague descriptions

   • •❌ "Samples were analyzed using standard methods"
   • •✅ "Samples were analyzed using high-performance liquid chromatography (HPLC; Agilent 1260 Infinity II) with a C18 column (4.6 × 250 mm, 5 μm particle size)"
2. •

   Missing statistical details

   • •❌ "Data were analyzed statistically"
   • •✅ "Group differences were analyzed using two-tailed independent samples t-tests with Welch's correction. Effect sizes were calculated using Cohen's d."
3. •

   Incomplete software information

   • •❌ "Analysis was performed in R"
   • •✅ "Analysis was performed in R version 4.2.1 using the lme4 package (version 1.1-30) for mixed-effects models"
4. •

   Mixing Methods with Results

   • •Methods: What you planned to do
   • •Results: What happened
   • •Keep strictly separate!
5. •

   Insufficient sample size justification

   • •Always include power analysis or rationale
   • •State actual vs. planned sample sizes if different

Execution Steps

1. Gather Context

Read these files:

• •.research/project_telos.md - Aims and methodology notes
• •dvc.yaml - Pipeline stages (if exists)
• •params.yaml - Parameters used
• •scripts/*.py or *.R - All scripts
• •manuscript/methods.md - Existing draft (if any)

2. Analyze Pipeline Structure

Extract from each script:

• •Purpose (from docstring)
• •Inputs and outputs
• •Key parameters
• •Dependencies/libraries used
• •Algorithms/methods applied

3. Methods Section Structure

# Methods

## Overview
<!-- Brief summary of analytical approach -->

## Data
### Data Sources
<!-- Where data comes from, access info -->

### Data Preprocessing
<!-- Cleaning, filtering, transformation -->

## Analysis Pipeline

### [Stage 1 Name]
<!-- What, why, how - link to script -->

### [Stage 2 Name]
<!-- Continue for each stage -->

## Statistical Analysis
<!-- Tests, thresholds, multiple testing correction -->

## Software and Reproducibility
<!-- Tools, versions, how to reproduce -->

4. Writing Standards for Methods

Level of Detail Test: Could another researcher reproduce your work?

Reporting Checklist:

For computational methods:

• • Software name and version
• • Key parameters and settings
• • Random seeds (if applicable)
• • Hardware specs (if relevant)

For statistical analyses:

• • Test name and implementation
• • Significance threshold (e.g., α = 0.05)
• • Multiple testing correction method
• • Effect size measures used
• • Assumptions checked

For machine learning:

• • Model type and hyperparameters
• • Training/validation/test split
• • Cross-validation strategy
• • Evaluation metrics
• • Feature selection method

5. Script-to-Methods Mapping

Create a mapping table:

## Code-to-Methods Reference

| Script | Methods Section | Purpose |
|--------|-----------------|---------|
| `01_preprocess.py` | Data Preprocessing | Quality filtering, normalization |
| `02_feature_select.py` | Feature Selection | Variance-based filtering |
| `03_model_train.py` | Model Training | Random forest classification |
| `04_evaluate.py` | Evaluation | Performance metrics |

6. Generate Methods Draft

# Methods

<!-- 
Draft generated by Research Assistant on [DATE]
Based on scripts in: scripts/
Pipeline defined in: dvc.yaml

⚠️ VERIFY:
- All software versions are current
- Parameters match params.yaml
- Nothing critical is missing
-->

## Overview

This study employed [brief approach summary] to [achieve aim]. 
The analysis pipeline was implemented in Python [version] and 
managed using DVC for reproducibility.

## Data

### Data Sources

[Describe data sources from project_telos.md or scripts]

### Data Preprocessing

*Script: `scripts/01_preprocess.py`*

[Auto-generated from script docstring and code analysis]

## Analysis Pipeline

### [Stage Name]

*Script: `scripts/02_analysis.py`*

[Description from docstring]

Parameters:
- Parameter 1: value (rationale)
- Parameter 2: value (rationale)

## Statistical Analysis

[If applicable - describe tests used]

## Software and Reproducibility

All analyses were performed using:
- Python [version] with [key packages]
- R [version] with [key packages] (if applicable)
- DVC [version] for pipeline management

The complete analysis pipeline is available at [repository] and 
can be reproduced using:

```bash
dvc repro

Code-to-Methods Reference

Missing Documentation Flags

[List any scripts without docstrings or unclear purposes]

### 7. Validation Checks

After generating, verify:

Methods draft created. Validation checklist:

✓ All scripts/*.py referenced ✓ Parameters extracted from params.yaml ? Check: Are all parameter values correct? ? Check: Are software versions current? ? Check: Can a reader reproduce this?

Flagged issues: ⚠️ Script preprocessing.py lacks docstring ⚠️ params.yaml has parameters not mentioned in methods

Run /review_script preprocessing.py to add documentation.

## Reporting Guidelines by Study Type

Follow the appropriate reporting guideline:

| Study Type | Guideline | URL |
|------------|-----------|-----|
| Randomized Controlled Trials | CONSORT | consort-statement.org |
| Observational Studies | STROBE | strobe-statement.org |
| Systematic Reviews | PRISMA | prisma-statement.org |
| Diagnostic Accuracy | STARD | stard-statement.org |
| Animal Studies | ARRIVE | arriveguidelines.org |
| Machine Learning | TRIPOD+AI | tripod-statement.org |

## Related Skills

- `review-script` - Add documentation to undocumented scripts
- `write-results` - Next manuscript section (after figures exist)
- `next` - Get suggestions

## Notes

- Methods should be written in past tense (you did the work)
- Be specific: "threshold of 0.5" not "appropriate threshold"
- Link to code repository in software section
- Update methods when pipeline changes