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Extended data analysis using DeCyder EDA

unknown” patient who was run as a duplicate came out once correctly as malignant, and once incorrectly as benign (Fig 8).

A detailed analysis of the images showed that both spot maps from the duplicate patient were of very low quality. Both gels were from the same casting batch and showed severe polymerization problems, as well as dust particles and air bubbles.


Conclusions
The purpose of this application note was to demonstrate how the different analysis procedures provided by DeCyder EDA might be used to answer the remaining questions from the DeCyder 2-D software BVA analysis.

It was not necessary to apply all possible calculations; neither would that provide clearer answers. We defined the questions to answer, and then selected the analysis tools that were suitable.

The remaining questions from BVA analysis were the following:
• Q1: How many groups or classes exist in the data set?
• Q2: Are there proteins or spots that behave similarly to a given protein or spot (co-regulation)?
• Q3: Are there proteins that might be used for the development of noninvasive tests?
• Q4: Are there proteins or protein patterns that might be characteristic of a biological state (e.g. tumor versus normal tissue)?

If and how many classes were in our data set was partly answered by the results from PCA and from hierarchical clustering. PCA showed that on the expression group level, as well as on the spot map level, the respective spot maps from the known groups were well separated. Hierarchical clustering worked well with the 150 selected proteins, but subclustering for the malignant samples did not follow the classification from the pathologists. If subclassification of the malignant samples was necessary, the experimental design and the anal
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