( atistical Algorithms work. The examples...)Fine Tuning Your Data Analysis:,,, Tunable Parameters of the Affymetrix Expression Analysis Statistical Algorithms

atistical Algorithms work. The examples do not provide sufficient information to serve as guidelines on how to modify parameters for a particular analysis. For most users, Affymetrix recommends using the default values of the tunable parameters as provided in MAS 5.0.

Results

I. Tunable Parameters Affecting Detection

I.A. Calculation and interpretation of Detection p-values

To determine if transcripts are detected specifically, probe pair discrimination scores (R) for every probe set are compared to a threshold value (tau) using a Wilcoxon signedrank test (see references 1 and 2). This non-parametric test establishes whether the probe sets overall discrimination is higher than the threshold, and generates a p-value as a measure of confidence in the specific detection of the transcript (references 1 and 2). The tau threshold influences the actual calculation of p-values, and is discussed further in section I.C. Once p-values are calculated, they can be used to rank probe sets by detection confidence, and to generate qualitative Detection calls. Cutoffs, operating as Figure 2. Modifying alpha1 changes the percentage of probe sets called Present in rat heart RNA (hybridized to the Rat Genome U34A array). The percentage of Present calls is plotted as a function of the user-tunable parameter alpha1. This array's default value for alpha1 (0.04) is indicated with the red line. Changing alpha1 from the default (arrows) alters the analysis stringency and the balance of sensitivity to specificity. tunable parameters, allow the user to modify how the p-values are used to make Detection calls. These are described next.

I.B. Detection Call cutoff values (alphas)

The p-values output by the Detection analysis give rise to three possible Detection Calls: Present (P), Marginal (M), and Absent (A). The calls are g
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