( false changes whereas most of the incr...)Fine Tuning Your Data Analysis:,,, Tunable Parameters of the Affymetrix Expression Analysis Statistical Algorithms

false changes, whereas most of the increasing genes in the brain vs. heart comparison represent real biological differences between the two tissues. More changes (~63 additional genes changing greater than 2 fold) can be detected in the different tissues comparison if gamma1 is moved to a less stringent setting (dashed red line), but some of these changes will be false. The number of additional false changes is estimated by the same RNA comparison (~5 genes over 2 fold). Altering gamma has a similar effect on decreasing genes (data not shown).

II.C. Further fine-tuning of Change calls with gamma H and L settings

Accurate detection of change can, in some cases, depend on the Signal range of the changing transcript. Changes in highly expressed transcripts may register different p-values compared to changes in low expressed transcripts, even if the changes are of comparable Signal LogRatios. MAS 5.0 contains a finer level of tuning for change calling, as shown in Figure 9. Each of the gamma cutoffs can be assigned a range of values (bounded by Low and High limits), from which probe set-specific final gamma cutoffs are calculated. Thus, gamma1 is derived from the gamma1L - gamma1H range, and gamma2 is derived from the gamma2L - gamma2H range. The final gamma cutoffs for a given probe set are calculated using a linear interpolation between the L and H limits, based on the probe sets Signal position over the entire arrays Signal range. The Signals used are those from the Experimental array in the comparison. Thus, as shown in Figure 9, if a probe sets Signal is at 30% of the arrays Signal range, the final gamma cutoffs used to decide the Change call for that probe set are set at 30% of the range of values given by the L-H limits for each. The default setting for the L and H limits is to make them equal (i.e., gamma1L = gamma1H and gamma2L = gamma2H). Therefore, the analysis does not use this fine-tuning unless
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