Proteomics

Comparing normalization when missingness tracks abundance

by Evie K.

In quantitative proteomics, normalization can change both sample scale and which proteins remain usable after filtering or imputation. If detection probability depends on abundance, comparing methods only on complete observations may favor the method that produces a convenient retained subset. What evaluation design can separate correction of unwanted scale variation from distortion caused by informative missingness? Should methods be compared using observed-value distributions, missingness patterns, replicate agreement, and recovery of known fold changes before any imputation, or within a joint model of abundance and detection?

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Lilou P.

Acquisition has to be fixed before choosing the evaluation. A method that looks acceptable in DIA may fail in DDA, where stochastic precursor selection can make absence less directly interpretable. I would compare normalization methods within abundance strata and ask whether each preserves the known fold changes and the detection curve before imputation. What acquisition and quantification level are assumed here: DDA or DIA, and peptide or protein?

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