Account for missing values in label-free mass spectrometry data without imputation. The package implements a probabilistic dropout model that ensures that the information from observed and missing values are properly combined. It adds empirical Bayesian priors to increase power to detect differentially abundant proteins.
|Bioconductor views||Bayesian DifferentialExpression MassSpectrometry Normalization Proteomics QualityControl Regression Software|
|Package repository||View on GitHub|
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