# Relative Abundance Calculation

> Use when after generating a frequency count table (e.g., from count_fold_changes output showing numbers of increased vs. decreased metabolites by class) and before creating proportional visualizations (pie charts, stacked bar plots).

- Skill: `holobiomicslab/relative-abundance-calculation` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/relative-abundance-calculation`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/relative-abundance-calculation/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/relative-abundance-calculation

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# relative-abundance-calculation

## Summary

Transform frequency count tables of metabolite subsets into relative abundance (percentage) data to enable proportional comparison across compound classes or fold-change categories. This is essential for pie-chart and proportional visualizations in metabolomics workflows where raw counts obscure class-level distributions.

## When to use

After generating a frequency count table (e.g., from count_fold_changes output showing numbers of increased vs. decreased metabolites by class) and before creating proportional visualizations (pie charts, stacked bar plots). Specifically when you have categorical metabolite counts stratified by a variable (e.g., Class, fold-change direction) and need to express each stratum as a percentage of the total.

## When NOT to use

- Input data is already in relative abundance or percentage format (re-normalizing will distort the data).
- Raw metabolite abundance values or normalized intensity counts are the goal; ra_table is for categorical frequency tables only, not individual sample-level metabolite intensities.

## Inputs

- frequency count dataframe (output from count_fold_changes with columns for metabolite class/category and counts of increased/decreased metabolites)

## Outputs

- relative abundance dataframe (percentage values, rows = metabolite classes/categories, columns = fold-change direction or other stratification, values = percentages summing to 100%)

## How to apply

Apply the ra_table function to a frequency dataframe generated from count_fold_changes, specifying the variable parameter to match the column used for stratification (e.g., variable='Class'). The function computes column-wise percentages, converting raw counts into relative abundances that sum to 100% within each grouping. This normalized representation allows fair visual comparison of metabolite proportions across classes regardless of absolute count magnitude, and prepares the data for downstream pie_chart or stacked visualization functions that expect ratio or percentage inputs.

## Related tools

- **count_fold_changes** (generates the frequency count table (raw counts of increased/decreased metabolites by class) that serves as input to ra_table) — github.com/connor-reid-tiffany/Omu
- **pie_chart** (consumes the relative abundance table output by ra_table to render proportional pie-chart visualizations) — github.com/connor-reid-tiffany/Omu
- **ggplot2** (underlying graphics library for rendering outputs compatible with ra_table and pie_chart workflows)
- **R (base)** (execution environment for omu package functions including ra_table)

## Examples

```
ra_table(count_fold_changes(omu_summary_output, column='Class', sig_threshold=0.05), variable='Class')
```

## Evaluation signals

- Output dataframe has the same row and column dimensions as the input frequency table (no rows/columns dropped or added).
- All values in the output are numeric percentages in the range [0, 100].
- For each column in the output, values sum to 100 (or very close, within floating-point precision ~1e-10), confirming proper normalization.
- Subsequent pie_chart rendering executes without errors and produces a plot where slice sizes correspond to the relative abundance percentages.
- Row/column ordering and metadata labels are preserved identically from input to output.

## Limitations

- ra_table operates only on frequency count dataframes; it is not designed for raw metabolite abundance matrices or intensity-normalized data.
- Percentages assume the input frequency table is complete and contains no missing values; incomplete frequency data will produce incorrect normalization.
- When frequency counts are very small (e.g., <5 metabolites per class), percentage estimates may have high uncertainty and are more suitable for descriptive visualization than statistical inference.

## Evidence

- [other] ra_table frequency data generation: "First, a frequency data frame (percentage values) must be made from the count data frame using the ```ra_table``` function"
- [other] ra_table input and usage in pie_chart workflow: "Apply count_fold_changes with column='Class' and sig_threshold=0.05 to generate a frequency table counting increased vs. decreased metabolites. Transform the frequency table to relative abundance"
- [other] ra_table output feeds downstream visualization: "This frequency data frame can be used in the ```pie_chart``` function"