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

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

Introduction

The Statistical Algorithms in Microarray Suite 5.0 process raw GeneChip probe set data to generate expression values (Signals and LogRatios), Detection and Change calls, and associated p-values for every transcript represented on the array.

Non-parametric rank tests are used to generate transcript Detection and Change p-values. Qualitative calls, such as Present and Increase, are derived from these p-values (see references 2 and 4). The Detection and Change algorithms incorporate user-tunable parameters that influence the analysis stringency of p-value calculation and call generation. This technical note introduces the tunable parameters of the Statistical Algorithm, and provides data examples that help explain how analysis results are affected when parameters are modified. For an in-depth discussion on how the Statistical Algorithms and their parameters work, please see reference 1.

There are two tiers of tunable parameters in the Statistical Algorithms. The first tier is represented by p-value cutoffs for both the Detection and Change calls. These parameters (alphas and gammas) do not alter the calculation of the p-values, only the generation of calls. The second tier of tunable parameters is represented by tau and perturbation for the Detection and Change p-value calculations, respectively. These parameters affect the calculation of p-values directly, and should be used only when fine-tuning of the underlying equations is required. User-tunable parameters do not influence Signal or LogRatio calculations.

Affymetrix recommends the default values, set in the library files for a given array type, in most cases for accurate gene expression analysis. These values were established using a large number of spiked sample
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