New statistical algorithms for Monitoring Gene Expression on GeneChip® Probe Arrays ( between replicates. Results are shown ...)

New statistical algorithms for Monitoring Gene Expression on GeneChip® Probe Arrays

between replicates. Results are shown in Figure 6.

The results show that for both MAS 4.0 and MAS 5.0, over 95% (at some concentrations 100%) correct No Change calls are made.

The concordance between Increase calls made by MAS 4.0 and MAS 5.0 are shown in Figure 7. The same transcript group was compared in pairs of experiments to evaluate the calls for a 2-fold and 4-fold change in concentration. For example, in the Latin Square represented in Figure 1, transcript group B was compared between Experiments 1 and 2, then 2 and 3, then 3 and 4, and so on, to determine the fraction correctly assigned an Increase call at the expected 2-fold change. For the 4-fold change, transcript group B was compared between Experiments 1 and 3, 2 and 4, 3 and 5 etc., such that all transcript groups were evaluated.

In this data set, MAS 5.0 shows greater accuracy than does MAS 4.0 at both 2-fold and 4-fold Increase levels. For example, at the 2 pM spike in Figure 7B, MAS 5.0 gives a nearly 10% greater fraction of correct Increase calls than does MAS 4.0. The drop in accuracy seen at very high concentration is due to the saturation of probe sets by the vast excess of spiked transcripts.

To understand the difference in the performance of calls generated by MAS 4.0 and MAS 5.0 on a biological sample, we assessed the results using an independent method. We used the power of replicates and the wellknown student’s t-test as a reference.

We analyzed a number of replicates in two tissues and then asked, “How well do the results from any single comparison match results from the group as a whole?”

The group was built from two sets of experiments—mouse brain and mouse heart—containing six replicates for each tissue. Each replicate was compared to every other replicate with both MAS 4.0 and MAS 5.0 (36 comparisons of 12488 probe sets). The t-test was then used to
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