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

the user changes the L and H values individually. In the majority of cases, it is not necessary to set the L and H gamma limits differently. This fine-tuning is only necessary if Change call accuracy is not optimal at one or both extremes of the Signal range of the array.

II.D. Change, significance (Normalization Perturbation Factor)

Effective normalization between arrays being compared is essential to detect changes in gene transcription accurately. When comparing two arrays, the Statistical Algorithm uses multiple normalization factors derived from a fundamental normalization factor using the Normalization Perturbation parameter. The lowest value for perturbation is 1.00, indicating no perturbation of normalization is carried out, while the highest allowed value must be below 1.5. An established default (1.1 for 16-20 probe pair designs) is based on the most accurate Change calls made from a calibrated data set. Increasing the perturbation factor increases analysis stringency by decreasing sensitivity to change, and fewer genes are called Increased or Decreased. Figure 10 shows the relationship between perturbation and change detection across an entire array. As perturbation is increased, the stringency of change detection is raised, and fewer genes score as Increased or Decreased. As seen with previous parameters, there is a trade off of sensitivity and specificity. Increasing perturbation above the default value will reduce false changes, but will also decrease sensitivity to real change. Conversely, setting the perturbation factor lower than the default value increases sensitivity, causing more genes to be called Increased or Decreased, but some of these additional changes will be false.

The effect of changing the perturbation factor on individual probe sets Change p-values can vary considerably, as shown in Figure 11. p-values for probe sets 1 and 2 show little or no response to an
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