Differential protein expression analysis is one of the main challenges in proteomics. It denotes the search for proteins, whose encoding genes are differentially expressed under a given experimental setup. An important task in this context is to identify the differentially expressed proteins or, more generally, all proteins present in the sample. One of the most promising and recently widely used approaches for protein identification is to cleave proteins into peptides, separate the peptides using liquid chromatography, and determine the masses of the separated peptides using mass spectrometry. The resulting data needs to be analyzed and matched against protein sequence databases. The analysis step is typically done by searching for intensity peaks in a large number of 2D graphs. We present an interactive visualization tool for the exploration of liquid-chromatography/mass-spectrometry data in a 3D space, which allows for the understanding of the data in its entirety and a detailed analysis of regions of interest. We compute differential expression over the liquid-chromatography/mass-spectrometry domain and embed it visually in our system. Our exploration tool can treat single liquid-chromatography/mass-spectrometry data sets against existing protein sequence databases. When using multidimensional protein identification technology (MudPIT) [21], the liquid chromatography method is applied under multiple different conditions in order to obtain data even from proteins, whose properties are hard to capture. The multiple results need to be merged during the analysis step.For differential protein expression analysis, data from different cell populations (e. g., diseased vs. healthy) or from one cell population acquired under different conditions need to be compared. Protein identification is used to register the data sets and to identify differentially expressed proteins. More information on the relevant biological background in the field of proteomics is given in Section 2.