Compound Internal Standard Ratio Calculation

compound-internal-standard-ratio-calculation

Summary

Compute the ratio of compound peak area to internal standard peak area for each sample, a fundamental normalization step in metabolomics that accounts for instrument variability and sample preparation differences. This ratio serves as the basis for downstream quality control, batch correction, and absolute concentration estimation.

When to use

Apply this skill immediately after loading metabolomics measurements into a SummarizedExperiment object, before any batch correction or quality filtering. Use it when you have paired measurements of compound area and assigned internal standard area in the same sample, and need a normalized metric that is independent of injection volume or extraction efficiency variation.

When NOT to use

• Samples lack internal standard measurements or internal standard area is zero (will produce NaN or Inf values)
• Internal standard assignment is unknown or ambiguous for a compound
• Input is already a ratio matrix; re-computation would create spurious values

Inputs

• SummarizedExperiment object with primaryAssay slot (compound area values)
• SummarizedExperiment object with secondaryAssay slot (internal standard area values)
• rowData metadata containing internal standard assignment per compound

Outputs

• SummarizedExperiment assay slot named 'ratio' containing compound/IS area ratios
• Numeric matrix of dimensions [compounds × samples] with ratio values

How to apply

Within the mzQuality workflow, the buildExperiment function automatically calculates the compound/internal standard ratio for each sample by dividing the primaryAssay (compound area) by the secondaryAssay (internal standard area). The ratio is stored as a computed assay slot in the SummarizedExperiment object. Ensure that each compound has a designated internal standard recorded in the object's metadata (rowData), and that both area measurements are present and non-zero for all samples. The calculated ratio is then used in subsequent steps: quality control filtering (via Rosner outlier tests on QC sample ratios), batch correction (ratio_corrected assay), background signal estimation, and concentration prediction via linear regression against known calibration standards.

Related tools

• mzQuality (Performs ratio calculation via buildExperiment and doAnalysis; stores ratio in assay slot) — https://github.com/hankemeierlab/mzQuality
• SummarizedExperiment (Container object that stores assay matrices (primaryAssay, secondaryAssay, ratio) and rowData/colData metadata)
• R base (Arithmetic division operation (primaryAssay / secondaryAssay))

Examples

path <- system.file("extdata", "example.tsv", package = "mzQuality"); exp <- buildExperiment(readData(path)); assay(exp, "ratio")

Evaluation signals

• Verify that the 'ratio' assay is present in the SummarizedExperiment object and has dimensions [number of compounds × number of samples]
• Check that all ratio values are finite positive numbers (no NaN, Inf, or negative values unless intentional masking is applied)
• Confirm that ratio values are used downstream in batch correction (ratio_corrected assay) and QC filtering (Rosner outlier detection on QC ratios)
• Inspect compoundPlot(exp, assay='ratio') to visually verify that QC sample ratios cluster tightly (low intra-group variance) before batch correction
• For samples with known concentrations, verify that ratio values exhibit linear correlation with concentration in concentrationPlot output

Limitations

• Assumes internal standard area is non-zero for all samples; samples with zero or missing internal standard values will produce invalid ratios (NaN or Inf)
• Ratio calculation alone does not account for matrix effects or batch-related drift; downstream batch correction using ratio_corrected is required
• Internal standard must be chosen appropriately and assigned consistently across all compounds; poor internal standard selection (high RSD in QC samples) will propagate to unreliable ratios
• Assumes that compound and internal standard are measured in the same injection and sample preparation; cross-sample or cross-batch internal standard substitution may violate this assumption

Evidence

• [other] buildExperiment uses primaryAssay and secondaryAssay as compound and Internal Standard areas respectively, and automatically calculates the compound/Internal Standard ratio for each sample: "buildExperiment deduces rowData and colData slots from compoundColumn and aliquotColumn, uses primaryAssay and secondaryAssay as compound and Internal Standard areas respectively, and automatically"
• [other] The ratio is the foundation for calibration-line concentration estimation via linear regression: "The Concentration Plot is a scatter plot with a linear model added, based on the calculated ratio and known concentration."
• [readme] Ratio is stored in the SummarizedExperiment assay slot for downstream analysis: "Calculate the ratio between the compounds and assigned internal standards"
• [readme] QC sample ratios are tested for outliers to detect instrument or sample preparation failures: "The package tests samples for outliers, specifically for (Pooled) Quality Control (QC) samples using their Compound / Internal Standard ratio"
• [intro] mzQuality recommends internal standards based on RSD of batch-corrected ratios in QC samples: "mzQuality is capable of recommending different internal standards by exhaustively calculating the Relative Standard Deviation of QC samples (RSDQC) of batch-corrected ratio's"