Motivation: LC-MS allows for the identification and quantification of proteins from biological
samples. As with any high-throughput technology, systematic biases are often observed in
LC-MS data, making normalization an important preprocessing step. Normalization models
need to be flexible enough to capture biases of arbitrary complexity, while avoiding
overfitting that would invalidate downstream statistical inference. Careful normalization of
MS peak intensities would enable greater accuracy and precision in quantitative�…

Motivation: Quantitative mass spectrometry-based proteomics requires protein-level
estimates and associated confidence measures. Challenges include the presence of low
quality or incorrectly identified peptides and informative missingness. Furthermore, models
are required for rolling peptide-level information up to the protein level. Results: We present
a statistical model that carefully accounts for informative missingness in peak intensities and
allows unbiased, model-based, protein-level estimation and inference. The model is�…