domains / families / science / normalization-qc

normalization-qc

separate

Designs QC, corrects signal drift, removes batch effects, filters features, normalizes samples, and imputes missing values for untargeted LC-MS/GC-MS metabolomics, framing each step as a measurement model that can create or erase biological signal. Use when processing a peak/feature table before statistical analysis, choosing a drift-correction or sample-normalization method, deciding QC RSD vs D-ratio filtering, or handling left-censored missing values. The feature table is produced by metabolomics/xcms-preprocessing or metabolomics/msdial-preprocessing; transformation/scaling for modeling defers to metabolomics/statistical-analysis; cross-study design issues link to experimental-design/batch-design.

When to use

• processing a peak/feature table before statistical analysis
• choosing a drift-correction or sample-normalization method
• deciding QC RSD vs D-ratio filtering
• handling left-censored missing values
• correcting within-batch drift against injection order
• normalizing per-sample dilution
• imputing by missingness mechanism
• removing batch effects

When not to use

• transformation/scaling for modeling
• cross-study design issues

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