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Investigates and optimizes the performance of three statistical approaches by using simulated and experimental data sets with varying numbers of missing values. LimmaRP uses three tools, including standard t-test, moderated t-test, and rank products for the detection of significantly changing features in simulated and experimental proteomics data sets with missing values.

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LimmaRP classification

LimmaRP specifications

Web user interface
Input data:
Intensity/abundance values of protein
Programming languages:
Restrictions to use:
Input format:
Computer skills:

LimmaRP support


  • Veit Schwämmle <>


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Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej, Odense, Denmark

Funding source(s)

This work was supported by a postdoctoral fellowship and a project grant from the Danish Council for Independent Research, Natural Sciences, from the NordForsk Center of Excellence MitoHealth, from the Faculty of Science, from the University of Southern Denmark and from the Novo-Nordisk Foundation.

Link to literature

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