Microbiome Diversity Reporter

Source: https://github.com/aipoch/medical-research-skills

Microbiome Diversity Reporter

When to Use

• Use this skill when the task needs Interpret Alpha and Beta diversity metrics from 16S rRNA sequencing results.
• Use this skill for academic writing tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

• Scope-focused workflow aligned to: Interpret Alpha and Beta diversity metrics from 16S rRNA sequencing results.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

• Python 3.8+
• numpy
• pandas
• scipy
• scikit-bio
• matplotlib
• seaborn
• plotly (for interactive charts)

Example Usage

See ## Usage above for related details.

cd "20260318/scientific-skills/Academic Writing/microbiome-diversity-reporter"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help
python scripts/main.py -h

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Overview

This tool is used to analyze and interpret diversity metrics in microbiome 16S rRNA sequencing data, including:

• Alpha Diversity: Species diversity within a single sample
• Beta Diversity: Species composition differences between samples

Usage

Command Line

# Analyze Alpha diversity for a single sample
python scripts/main.py --input otu_table.tsv --metric shannon --output alpha_report.html

# Analyze Beta diversity (PCoA)
python scripts/main.py --input otu_table.tsv --beta --metadata metadata.tsv --output beta_report.html

# Generate full report (Alpha + Beta)
python scripts/main.py --input otu_table.tsv --full --metadata metadata.tsv --output diversity_report.html

Parameter Description

Input Format

OTU Table (TSV)

#OTU ID	Sample1	Sample2	Sample3
OTU_1	100	50	200
OTU_2	50	100	0
OTU_3	25	25	50

Metadata (TSV)

SampleID	Group	Age	Gender
Sample1	Control	25	M
Sample2	Treatment	30	F
Sample3	Treatment	28	M

Output

Generates HTML/JSON/Markdown reports containing:

1. Alpha Diversity Results

   • Diversity index values
   • Rarefaction curves
   • Box plots (by group)

2. Beta Diversity Results

   • PCoA scatter plots
   • NMDS plots
   • Distance matrix heatmaps
   • PERMANOVA statistical tests

3. Statistical Summary

   • Sample information statistics
   • Species richness
   • Diversity index distribution

Example Output

{
  "alpha_diversity": {
    "shannon": {
      "Sample1": 2.45,
      "Sample2": 1.89,
      "Sample3": 2.12
    },
    "statistics": {
      "mean": 2.15,
      "std": 0.28
    }
  },
  "beta_diversity": {
    "method": "braycurtis",
    "pcoa": {
      "variance_explained": [0.45, 0.25, 0.15]
    }
  }
}

References

1. Shannon, C.E. (1948) A mathematical theory of communication
2. Simpson, E.H. (1949) Measurement of diversity
3. Chao, A. (1984) Non-parametric estimation of classes
4. Lozupone et al. (2005) UniFrac: a phylogenetic metric