clinprotools_2_0

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1. Visualization of data
ClinProTools 2.0 shows intuitive visualization features on one screen (see Fig. 1): 1. Display of single and average spectra to allow inspection of data 2. Gel view, displaying of all spectra at a time 3. Intuitive display functions 4. Visualization of peak info from each sample for detection of best separating peaks
The output has a design which gives the user a maximum level of convenience together with high flexibility. Lists can be sorted and the output exported to Microsoft Excel. Sorted according to the p-value the Standard Deviation as well as Box and Whiskers plots can be displayed. For the statistics of single peaks, CPT 2.0 uses the Quick Classifier algorithm (QC) (see Fig. 7 B) which is a univariate sorting algorithm. This algorithm shows good results especially when a
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Fig. 2: Gel view (A) and stack view (B) of spectra to illustrate higher expression of a marker candidate
Figure 2 shows additional ways of data visualization. The complete spectral information for a particular m/z value (mass to charge ratio) can be visualized in grayscale - and other colors (Fig. 2A). A stack view (Fig. 2B) can be used to get a better impression of the distribution of a certain peak in all samples.
Fishing for biomarkers: Analyzing mass spectrometry data with ClinProTools 2.0
Introduction
The discovery of new protein biomarkers is one of the most promising objectives in the area of proteomics. Here, biomarkers are defined as peptides/proteins that act as an indicator of a certain disease, treatment, status, or progression of disease. The identification of new and validated biomarkers is of particular interest in different clinical areas like oncology, neurology, toxicology and pharmacology. Especially in oncology patients should benefit in the near future from biomarkers for early diagnosis resulting in better prognosis and recovery. Mass spectrometric techniques play an emerging role in biomarker research due to their high mass accuracy, sensitivity, and speed of detection. The ClinProt system builds on mass spectrometric power and enables the researcher, through a simple and parallel prefractionation with magnetic beads, to directly assess peptide/ protein profiles from biofluids as well as other specimens. The starting point for biomarker discovery projects are usually samples sets of 20-30 patients per group (e.g. healthy vs. diseased). For validation of biomarker candidates, the number of patients should exceed hundreds or thousands of samples which implies the need for powerful bioinformatic tools. The ClinProTools 2.0 software package fulfils all requirements to find biomarkers in a large number of very complex/hidden patterns with highly sophisticated bioinformatics tools. The concept of ClinProTools 2.0 is based on intuitive visualization of data together with bioinformatics data analysis in a single software which is easily operated, even by novice users:
Fig. 1: The ClinProTools 2.0 graphical user interface (GUI): 1. Spectra view display single and average spectra for all classes 2. Virtual gel view allowing overview of all spectra 3. Selection and information on model settings 4. Representation of the two best separating peaks in two dimensional space
2. Workflow
The workflow of ClinProTools 2.0 starts by simple loading of spectra for selected classes. Several classes can be loaded in the same analysis; an advantage when answering complex biological questions like different stages of disease (Fig. 6). In summary, data are processed according to the following workflow: 1. Loading selected spectra 2. Recalibration 3. Calculation of average spectra 4. Detection of peak areas on the total average spectrum
3. Univariate and multivariate statistics tools
The starting point for statistical analysis are usually univariate tests. ClinProTools 2.0 offers both statistical tests for expected normal distributed data, as well as for data sets that are not normal distributed. As an output, the software creates a table of peaks that can be sorted according to lowest p-value. The p-value describes the significance and probability of single peaks in order to separate the classes.