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bioc
proDA:Differential Abundance Analysis of Label-Free Mass Spectrometry Data
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.
Maintained by Constantin Ahlmann-Eltze. Last updated 5 months ago.
proteomicsmassspectrometrydifferentialexpressionbayesianregressionsoftwarenormalizationqualitycontrol
19 stars 7.52 score 48 scripts 1 dependents
jvadams
LW1949:An Automated Approach to Evaluating Dose-Effect Experiments Following Litchfield and Wilcoxon (1949)
The manual approach of Litchfield and Wilcoxon (1949) <http://jpet.aspetjournals.org/content/96/2/99.abstract> for evaluating dose-effect experiments is automated so that the computer can do the work.
Maintained by Jean V. Adams. Last updated 8 years ago.
3 stars 4.78 score 40 scripts