Microbiome Diversity Reporter

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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

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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

Parameter	Description	Required
--input	OTU/ASV table path (TSV format)	Yes
--metadata	Sample metadata (TSV format)	Required for Beta diversity
--metric	Alpha diversity metric: shannon, simpson, chao1, observed_otus	No (default: shannon)
--alpha	Calculate Alpha diversity only	No
--beta	Calculate Beta diversity only	No
--full	Generate full report (Alpha + Beta)	No
--output	Output report path	No (default: stdout)
--format	Output format: html, json, markdown	No (default: html)

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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

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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

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Dependencies

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

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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

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python/R scripts executed locally	Medium
Network Access	No external API calls	Low
File System Access	Read input files, write output files	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Output files saved to workspace	Low

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support