Background: Analysis of exhaled breath, especially of volatile organic compounds (VOCs

Supplementary data are available at Bioinformatics online.

This study has developed an efficient preprocessing strategy for ion mobility spectrometry (IMS) data allowing for improved peak clarity and comparability of different measurements. Using the discrete wavelet transform for data compression and denoising, and fitting a lognormal function to the strong tailing of the reactant ion peak (RIP), enables a data reduction to 25% or less, a significant increase of the signal-to-noise ratio, and the successful elimination of the RIP tailing. The preprocessing of breath measurements obtained by coupling an IMS to a gaschromatographic column, has resulted in the desired outcome of smooth peaks lying on a common base level. These results are transferable to other applications of one-and two-dimensional separations with IMS or instrumentations generating a similar data structure.

Most proteomics experiments make use of 'high throughput' technologies such as 2-DE, MS or protein arrays to measure simultaneously the expression levels of thousands of proteins. Such experiments yield large, high-dimensional data sets which usually reflect not only the biological but also technical and experimental factors. Statistical tools are essential for evaluating these data and preventing false conclusions. Here, an overview is given of some typical statistical tools for proteomics experiments. In particular, we present methods for data preprocessing (e.g. calibration, missing values estimation and outlier detection), comparison of protein expression in different groups (e.g. detection of differentially expressed proteins or classification of new observations) as well as the detection of dependencies between proteins (e.g. protein clusters or networks). We also discuss questions of sample size planning for some of these methods.