# Missing Data Handling In Metabolite Datasets

> Use when when importing metabolomics datasets with multiple studies into amanida_read for quantitative or qualitative meta-analysis, and some studies report missing values for p-values, fold-changes, sample sizes, or other effect size metrics.

- Skill: `holobiomicslab/missing-data-handling-in-metabolite-datasets` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/missing-data-handling-in-metabolite-datasets`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/missing-data-handling-in-metabolite-datasets/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Docs & Writing
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/missing-data-handling-in-metabolite-datasets

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# missing-data-handling-in-metabolite-datasets

## Summary

A strategy for handling missing values in metabolomics datasets during import and meta-analysis preparation. Missing data is ignored during the amanida_read import step, allowing incomplete compound records to be retained without imputation, which is appropriate when metabolomics studies report sparse or incomplete effect size information across multiple studies.

## When to use

When importing metabolomics datasets with multiple studies into amanida_read for quantitative or qualitative meta-analysis, and some studies report missing values for p-values, fold-changes, sample sizes, or other effect size metrics. This is common in metabolomics meta-analyses because studies vary in what statistics they report and whether they disclose all required parameters.

## When NOT to use

- If your dataset requires imputation (e.g., expectation-maximization, k-nearest neighbor) rather than listwise deletion — amanida_read does not support imputation methods.
- If missing data occurs in the compound identifier column, as compounds cannot be matched or aggregated without a unique ID.
- If the majority of studies have missing effect size metrics (e.g., >50% of rows missing p-values or fold-changes), indicating that the dataset may be too sparse for reliable meta-analysis.

## Inputs

- CSV, TSV, XLS, or XLSX file with columns for compound identifier, p-value, fold-change, study sample size (N), and bibliographic reference
- Column name mapping vector (coln) specifying the order and names of required columns
- Separator character (e.g., ';' or '\t') if not auto-detected

## Outputs

- R tibble with compound identifiers, p-values, fold-changes, study sizes, and references; rows with complete data retained, rows with any missing values in required columns silently excluded
- Data types validated for downstream meta-analysis (numeric for p-values and fold-changes, integer for N, character for identifier and reference)

## How to apply

During the amanida_read import step with mode='quan' or mode='qual', the function automatically ignores missing data without requiring explicit imputation parameters. Specify the column mapping via the coln parameter for the columns present in your dataset (identifier, p-value, fold-change, N, reference for quantitative analysis; identifier, trend, reference for qualitative analysis). Missing values in any of these columns are silently skipped, and rows with any missing required field are handled gracefully by the downstream compute_amanida or amanida_vote functions, which operate on complete pairs. This approach is justified because metabolomics meta-analysis weights studies by their sample size and statistical significance, so incomplete reporting in one study does not invalidate analysis of studies with complete data.

## Related tools

- **amanida** (Performs missing data handling during import via amanida_read function; also computes weighted meta-analysis on complete-case data) — https://github.com/mariallr/amanida
- **R** (Language and runtime environment for executing amanida and data manipulation functions)

## Examples

```
coln = c("Compound Name", "P-value", "Fold-change", "N total", "References")
input_file <- system.file("extdata", "dataset2.csv", package = "amanida")
datafile <- amanida_read(input_file, mode = "quan", coln, separator=";")
```

## Evaluation signals

- Check that the output tibble has fewer rows than the input file if any rows contained missing values in required columns; use nrow() to compare.
- Verify that all remaining rows have non-NA values in the required columns (identifier, p-value, fold-change, N, reference for quantitative analysis) using complete.cases() or is.na() checks.
- Confirm that numeric columns (p-value, fold-change, N) are stored as numeric or integer types, not character, using str() or class().
- Run compute_amanida on the resulting tibble without errors; if errors occur, investigate whether required columns are present and correctly named.
- Compare the number of compounds in the meta-analysis output with the number of unique identifiers in the cleaned tibble; they should match if no further filtering was applied.

## Limitations

- amanida_read silently removes rows with missing data without logging which rows or columns were affected; users cannot easily audit the exclusion.
- No support for multiple imputation or sensitivity analysis to assess impact of missing data mechanism (MCAR vs. MAR vs. MNAR).
- If missing data is not missing completely at random (MCAR), the resulting meta-analysis estimates may be biased, but amanida does not warn or test for this.
- Missing data handling is hard-coded in amanida_read; users cannot select alternative strategies (e.g., pairwise deletion or indicator variables).

## Evidence

- [intro] missing data is ignored: "During import, missing data is ignored and negative fold-change values are transformed to positive using the reciprocal formula (1/value)"
- [intro] Missing data ignored during workflow: "Apply missing data handling by ignoring missing values during import."
- [readme] amanida_read function processes required columns: "The amanida_read function accepts metabolomics datasets in xls/xlsx, csv, or txt formats with required columns for compound identifier, p-value, fold-change, study size (N), and reference."
- [readme] Column specification for quantitative meta-analysis: "coln: vector containing the column names, which need to be in this order: Id: compound name or unique identification, P-value, Fold-change, N: number of individuals in the study, Reference:"