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Extended data analysis using DeCyder EDA

Key words: DeCyder EDA • extended data analysis • workspace • base set • differential expression analysis • principal components analysis • pattern analysis • discriminant analysis • Ettan DIGE


DeCyder™ Extended Data Analysis (EDA) Software was used to perform advanced statistics on samples from a study on human ovarian cancer. The known samples could be clearly separated into normal, benign, and malignant, agreeing with classifications made by pathologists. The classifier created from the known samples was used to classify the unknown samples. All unknown spot maps were classified correctly, except for one from a poorly cast gel. The incorrectly classified sample was a duplicate of a sample that had been correctly classified.


Introduction
The Ettan™ 2-D Difference Gel Electrophoresis (DIGE) system has greatly reduced system variation, to a level that allows the detection of minor expression changes of as little as 10%.

Univariate analysis within DeCyder 2-D Differential Analysis Software (DeCyder 2-D)—Student’s t-test, one-way Analysis Of Variance (ANOVA), and two-way ANOVA—allows the identification of proteins that have altered expression with a given statistical confidence, as well as the interaction of two conditions.

Nevertheless, there might be remaining questions:
• How many groups or classes exist in a given data set?
• Are there proteins or spots that behave similarly to a given protein or spot (i.e. co-regulation)?
• Are there proteins that might be used for the development of noninvasive tests (i.e. diagnostic markers)?
• Are there proteins or protein patterns that might be characteristic of a biological state (e.g. tumor versus normal tissue
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