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Figure 1 | BMC Bioinformatics

Figure 1

From: A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database

Figure 1

ROC curves for the Classic RMA, Full refRMA, and MAS5 algorithms via affycomp. Each of the summarization algorithms are shown with respect to False Positive probe sets vs. True Positive percentage for the affycomp spiked-in HG U133A data set. The spiked-in probe sets used are limited to a) lowly expressed probe sets (≤ 2 pM) and b) moderately expressed probe sets (≥ 4 and ≤ 32 pM) as defined in [9]. The Full refRMA model performs better than either of the MAS5 algorithms, but does not do as well as the Classic RMA model for the spiked-in probe sets. The likely reason for this result is discussed in the text.

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