Abstract：
: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.
journal_name
J Proteome Resjournal_title
Journal of proteome researchauthors
Koopmans F,Cornelisse LN,Heskes T,Dijkstra TMdoi
10.1021/pr500171usubject
Has Abstractpub_date
2014-09-05 00:00:00pages
3871-80issue
9eissn
1535-3893issn
1535-3907journal_volume
13pub_type
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