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Table 3 Number of GO terms with p-values less than 10-10 for four pre-processing algorithms, according to CLASSIFI on the GSE2350 data. Larger numbers indicate better performance.

From: A distribution-free convolution model for background correction of oligonucleotide microarray data

Normalization Background Correction Methods
Loess 86 87 88 57
Quantile 48 47 50 60
Scale 83 80 76 24
  1. To examine the effect of normalization on the results, quantile normalization, scale normalization (as defined for the MAS 5.0 algorithm) or loess was used in combination with each of the background methods discussed in this paper. All methods (except for MAS 5.0) used median polish summarization. Differentially expressed genes were selected using two-sample t-tests. The methods GCRMA, dChip and PLIER could not be used because their background correction, normalization, and summarization algorithms cannot be separated easily.