Universitätspublikationen
Refine
Document Type
- Article (1)
- Working Paper (1)
Language
- English (2)
Has Fulltext
- yes (2)
Is part of the Bibliography
- no (2)
Keywords
- statistics (2) (remove)
Here, we present a peptide-based linear mixed models tool—PBLMM, a standalone desktop application for differential expression analysis of proteomics data. We also provide a Python package that allows streamlined data analysis workflows implementing the PBLMM algorithm. PBLMM is easy to use without scripting experience and calculates differential expression by peptide-based linear mixed regression models. We show that peptide-based models outperform classical methods of statistical inference of differentially expressed proteins. In addition, PBLMM exhibits superior statistical power in situations of low effect size and/or low sample size. Taken together our tool provides an easy-to-use, high-statistical-power method to infer differentially expressed proteins from proteomics data.
In many cases, the dire situation of public finances calls into question the very soundness of sovereigns and prompts corrective actions with far-reaching consequences. In this context, European authorities responded with several measures on different fronts, for instance by passing the "Fiscal Compact", which entered into force on January 1, 2013. Of critical importance in this framework is the assessment of a country’s situation by way of statistical measures, in order to take corrective actions when called for according to the letter of the law. If these statistics are not correct, there is a risk of imposing draconian measures on countries that do not really need it.