Empirical Bayesian Random Censoring Threshold Model Improves Detection of Differentially Abundant Proteins

A challenge
in proteomics is that many observations are missing
with the probability of missingness increasing as abundance decreases.
Adjusting for this informative missingness is required to assess accurately
which proteins are differentially abundant. We propose an empirical
Bayesian random censoring threshold (EBRCT) model that takes the pattern
of missingness in account in the identification of differential abundance.
We compare our model with four alternatives, one that considers the
missing values as missing completely at random (MCAR model), one with
a fixed censoring threshold for each protein species (fixed censoring
model) and two imputation models, k-nearest neighbors
(IKNN) and singular value thresholding (SVTI). We demonstrate that
the EBRCT model bests all alternative models when applied to the CPTAC
study 6 benchmark data set. The model is applicable to any label-free
peptide or protein quantification pipeline and is provided as an R
script.