Title Quantitative Analysis of Mass Spectrometry Data Abstract Quantification of liquid chromatography mass spectrometry (LC-MS) data is complicated by missing values. Some are easier to deal with than others. For example, values missing completely at random can be ignored or imputed based on the observed peptide abundances. Left-censored values that fall bellow the detection limit are harder to deal with as observed values are not representative of the missing ones. Here I will present a statistical model that takes into account the ’missingness’ mechanism and imputes the values accordingly. I will also discuss a normalisation method that removes biases of arbitrary complexity.