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.

Normalization

Background Correction Methods


DFCM

RMA

None

MAS 5

Loess

86

87

88

57

Quantile

48

47

50

60

Scale

83

80

76

24


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.

Chen et al. BMC Genomics 2009 10(Suppl 1):S19   doi:10.1186/1471-2164-10-S1-S19

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