Missing Data Handling In Metabolite Datasets

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:"