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Statistical models for proteomics data often estimate protein fold changesbetween two samples, A and B, as the average peptide intensity from sample Adivided by the average peptide intensity from sample B. Such average intensityratios fail to take full advantage of the experimental design which eliminatesunseen confounding variables by processing peptides from both samples underidentical conditions. Typically this structure is exploited through theestimation of a protein ratio as the median ratio of matched peptideintensities. This simple solution fails to account for a substantial missingdata bias which has led to the development of more sophisticated averageintensity models. Here we develop the first statistical model that accounts fornonignorable missingness while utilizing peptide level matched pairs acrosssamples. Our simulation analysis shows that models which fail to utilizepeptide level ratios, su.er astonishing losses to accuracy with basic ANOVAestimates having an average MSE 371% higher than median ratio estimates. Inturn, median ratio estimates have an average MSE 35% higher than our modelestimates. An analysis of breast cancer data reinforces these relationships andshows that our model is capable of increasing the number of proteins estimatedby 22%.

The Midpoint Mixed Model with a Missingness Mechanism (M5): A  Likelihood-Based Framework for Quantification of Mass Spectrometry Proteomics  Data (Preprint)