proDA

proDA accounts for missing values in label-free mass spectrometry data without imputation by implementing a probabilistic dropout model that combines information from observed and missing values to detect differentially abundant proteins.

Key Features:

• Probabilistic Dropout Model: The model ensures that information from observed and missing values are properly combined.
• Empirical Bayesian Priors: Empirical Bayesian priors are added to increase power to detect differentially abundant proteins.
• Imputation-Free Approach: Missing values are handled without imputation.

Scientific Applications:

• Differential Abundance Analysis: Detection of differentially abundant proteins in label-free mass spectrometry data.

Methodology:

The package implements a probabilistic dropout model with empirical Bayesian priors.