proDA_package: proDA: Identify differentially abundant proteins in...
In proDA: Differential Abundance Analysis of Label-Free Mass Spectrometry Data
Description
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.
proDA documentation built on Nov. 8, 2020, 5:01 p.m.
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Description
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.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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