Differential expression analysis is performed by applying Student’s t-test or one-way ANOVA for a multiple-comparison test. The results from the differential expression analysis are used to reduce the data set, for example limiting it to proteins that show changes in expression level. In this study we used one-way ANOVA.
Principal components analysis (PCA) helps identify some underlying sources of variation, and will give a first impression if, and how well groups and classes might be separated. This type of analysis is extremely sensitive to outliers and might help to identify possible mismatches. In this study each patient sample was run as a duplicate on separate gels that included dye swapping. The two spot maps from the same patient should show up close to each other in the diagrams.
Pattern analysis finds patterns in the expression profiles in the EDA data without any prior information about the variables. Items with similar expression profiles—such as proteins, spot maps, and experimental groups—are clustered. In this study we applied two types of unsupervised clustering:
• Hierarchical clustering, which is displayed as a heat map with dendrogram, showing if and how many different classes exist in the data set
• K-means clustering, which shows clusters of proteins with similar expression patterns
Discriminant analysis identifies markers, and creates a classifier. The classifier is used to classify unknowns. This analysis also helps find proteins that might be useful for the development of a noninvasive diagnostic test.
Based on the results from the different calculations, new sets can be created and new calculations and biological interpretation can be performed.
DeCyder Extended Data Analysis Software, one network u