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Fine Tuning Your Data Analysis:,,, Tunable Parameters of the Affymetrix Expression Analysis Statistical Algorithms

. The two other transcripts (bioC and rat gene2) are more resilient to the increased stringency, and maintain low p-values, and Present calls, as tau is increased. Modifying tau directly influences the balance of sensitivity vs. specificity of the Detection analysis.

II. Tunable Parameters Affecting Change Analysis

II.A. Calculation of Change p-values

The calculation of the p-value is influenced by one tunable parameter, termed Perturbation, which operates on the normalization factor applied to the arrays during the comparison. Cutoffs, termed gamma1 and gamma2, are applied to the two ends of the p-value range to delineate the qualitative Change calls.

Instead, gammas serve as thresholds to divide the data into zones associated with Change calls such as Increase. Changing gamma values can be visualized as sliding the cutoffs (red lines on plot) to the left or right, resulting in a change in the number of transcripts scoring a given call. An ideal placement of the gamma cutoffs would allow the maximum number of true changes to be detected, while minimizing false changes. This is shown with the line illustration above the graph; the p-value distribution of truly changing genes overlaps to an extent with the p-value distribution of non-changing genes. The region of overlap is primarily composed of transcripts that score a Marginal Change call. Moving gamma1 (default = 0.0025 for the Rat Genome array) to a lower p-value level (dashed red line, = 0.001), results in higher stringency, and fewer genes are reported as Increased (2.4% compared to 2.8% at default gamma1 value). This higher stringency setting decreases false changes at the cost of losing some real changes.

The tradeoff of detecting real changes while minimizing false changes is further illustrated in Figure 8. Genes increasing in the same RNA comparison (brain) represent
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