Bayesian statistical modeling reveals missing value mechanisms in label-free Mass Spectrometry-based proteomics experiments
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This talk reviewed bayesian statistical modeling strategies for upstream data processing of MS-based proteomics experiments. This included missing value imputation and summarization of peptide ions to the protein-level. These processing steps were performed with a bayesian model which allowed error propogation at each step. The final summarized values were used for differential analysis in a error-in-variance model.