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

increase in perturbation, whereas those for transcripts 3 and 4 move towards higher p-values (less significant), eventually crossing over the gamma cutoffs and generating No Change calls. This occurs because probe sets vary in how their individual probe pairs perform in the rank-based test used to compare the baseline to the experimental probe arrays. Transcripts with more stable p-values (such as 1 and 2 in Figure 11) tend to have higher LogRatio values than those whose p-values are impacted by changing perturbation, indicating that, in general, greater changes tend to be more robust in terms of scoring a significant Change p-value.

Conclusion

The tunable parameters of the Statistical Algorithm directly influence the outcome of raw data analysis of GeneChip expression arrays. Changing the parameters can be done, but is recommended only if fine-tuning of the analysis is required. If a parameter is to be modified, it is advisable to do so in small increments, and to keep track of the changes. Analysis parameters are reported in the .CHP files, and can be viewed in the experiment information tab. Be certain that analysis parameters are consistent across compared samples within an experiment.

Should You Change these Parameters?

In general, Affymetrix does not recommend changing the Statistical Algorithms parameters. This is especially true of tau and perturbation factor, since they are integral to p-value calculations. The default values set for these parameters in MAS 5.0 were established at Affymetrix to achieve the highest accuracy and sensitivity for the current GeneChip expression arrays. Future array designs and assay conditions may require changes in certain parameter values. Such changes will be provided by Affymetrix, and in general, will be preset in the Library files released with an array design.

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