Berend Hoekman

TL;DR: It is concluded that the univariate t test and the mww test with multiple testing corrections are not applicable to data sets with small sample sizes, but their performance improves markedly with increasing sample size up to a point at which they outperform the other methods.

TL;DR: An overview of the most important aspects of LC(n)-MS with respect to optimizing peak capacity and evaluate orthogonality is given and examples from research serve to highlight possibilities and shortcomings of present-day approaches.

TL;DR: A platform for the comparative profiling of urine using reversed-phase liquid chromatography-mass spectrometry (LC-MS) and multivariate statistical data analysis and the added peptides were ranked as highly discriminatory peaks despite significant biological variation is described.

TL;DR: MSCompare as mentioned in this paper is a modular framework that allows the arbitrary combination of different feature detection/quantification and alignment/matching algorithms in conjunction with a novel scoring method to evaluate their overall performance.

TL;DR: The aim was to distinguish low-abundance signal peaks from noise by noting their coherent behavior across multiple data sets, and central to this is the need to delay the culling of noise peaks until the final peak-matching stage of the pipeline, hence the name TAPP: threshold-avoiding proteomics pipeline.