proDA_package: proDA: Identify differentially abundant proteins in...
In const-ae/proDA: Differential Abundance Analysis of Label-Free Mass Spectrometry Data
proDA_package R Documentation
proDA: Identify differentially abundant proteins in label-free mass spectrometry
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.
const-ae/proDA documentation built on Oct. 31, 2023, 9:39 p.m.
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| proDA_package | R Documentation |
proDA: Identify differentially abundant proteins in label-free mass spectrometry
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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