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Extended data analysis using DeCyder EDA

our base set (Fig 2).

From this base set two subsets were created: one containing all known samples, the other containing all unknown samples. For the final classification the set called “unknowns” was used; for all other calculations the set called “base set–unknowns” and its subset “p<0.001” were used.

Data reduction
We further reduced our data set by restricting it to proteins that showed differential expression. From the differential expression calculation routines, we selected One-Way-ANOVA for multiple-comparison tests. Because all samples were run in duplicate, we found some artificially low p values. For filtering we selected those proteins with p values < 0.001. The remaining 150 proteins now formed the new data set “p<0.001” on which all subsequent analysis was performed except classification.


Answering questions
Question 1: How many groups or classes exist in the data set?

To answer this question we performed two different analyses on the “p<0.001” data set: PCA and hierarchical clustering.

The results from the PCA show that on the expression group level the three valid groups normal, benign, and malignant were well separated. Benign and malignant were close to each other while distant from normal (Fig 3).

At the spot map level the maps from the malignant group were well separated from the benign and normal. Spot maps from the benign and normal groups partly overlapped in the 2-D view, but were still well separated in the 3-D view. This first overview analysis showed clearly that the three valid groups are separable and we should find some valid markers helping us to classify the unknown samples later on.

In the hierarchical clustering we found two main clusters, one
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