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Genome Wide Profiling of Paired Cancerous and Normal Breast Tissues and Rapid Interpretation of Gene Expression Data

s. This study also showed the potential of the Panther gene classification system for rapidly and reliably identifying biomarkers and exploring novel genes. TaqMan assays confirmed the gene expression changes detected by the microarray, which indicates a good concordance between the Applied Biosystems Expression Array System and the TaqMan process.

In about 50% of the cases the actual fold changes determined by both methods were approximately the same. However, in virtually all cases where a fold change (either up- or downregulated) was detected by the Applied Biosystems Expression Array System it was confirmed by TaqMan analysis.

Data Analyis

Data Output

Microarray data analysis includes both primary and secondary data analysis. The primary data analysis is performed using the software provided with the Applied Biosystems Expression Array System. For secondary analysis, the system software is compatible with both GeneSpring and Spotfire software application packages. The Expression Array System can also export microarray data in a flat-file format and industry standard MAGE-ML.

Differential Gene Expression

Generally, the following three steps are used to determine differential gene expression:

Step 1: Regression analysis on biological replicates (and/or technical replicates), which rejects array outliers based on regression threshold. For example, the data point that has a correlation coefficient of R2 < 0.75 or R2 < 0.95 is used as threshold.

Step 2: Perform t-test/ANOVA analysis to determine differentially expressed genes based on p-value threshold (e.g. p < 0.001, p < 0.01 or p < 0.05). This is dependent on the confidence level researchers would like to select between test samples and normal samples.

Step 3: Determine fold change of genes by measuring the a
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