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

s, hybridized to human, yeast, and E. coli probe arrays (see references 1 and 2). Altering parameter values is only necessary if the stringency of the analysis requires a change from the established defaults.

Why do the Statistical Algorithms have tunable parameters?
The equations underlying the GeneChip Statistical Algorithms incorporate statistically based stringency parameters that control the balance between true detection and false signal in a predictable manner. These parameters can be used to construct Receiver Operating Characteristics (ROC) plots (Figure 1) when used with calibration data sets (e.g., the Latin Square spiked samples described in Tech Note I). These plots are useful for setting optimal values for each of the parameters to achieve the best balance between true detection and false detection. The flexibility provided by the tunable parameters made it possible for Affymetrix to fine-tune the algorithms for current array designs and assay conditions, and will allow for further refinements in the future as expression arrays evolve.

Experimental design
.CEL files from a rat cardiovascular study (reference 3) were analyzed individually and in comparisons using the Expression Analysis Statistical Algorithms (Affymetrix Microarray Suite 5.0). Each of four tunable parameters, alpha1, tau, gamma1 (L and H together), and the perturbation factor, was altered individually, above and below the default values. Other tunable parameters, such as alpha2, were kept at defaults. Data from an entire array, and from individual probe sets, were extracted and displayed to observe the effects of modifying each of the tunable parameters on relevant analysis metrics, such as p-values.

Note: The analyses shown in this technical note are intended to serve as examples to help communicate how the parameters of the St
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