Skip to main content

Table 1 Overview of several pre-processing methods.

From: The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies

  Normalization method Summarization method
MAS Global scaling – individual array normalization; moderate influence on expression levels, no effect on outliers. Non-parametric methods are potentially more reliable Tukey biweight (robust average) – subtract MM from PM and adjust for negative values
dChip Average median scaling – individual array normalization; moderate influence on expression levels, no effect on outliers. Non-parametric methods are potentially more reliable Model-based index estimate – subtract MM from PM, but take indivual probe variability, assessed over all available arrays, into account
RMA Quantile normalization – multiple array normalization; considerable influence on expression levels, with removal of outliers. Parametric methods are potentially more reliable Median polish – only use MM for background adjustment; fit parameters of linear model robustly using median polish, taking into account all available arrays
GCRMA Quantile normalization – multiple array normalization; considerable influence on expression levels, with removal of outliers. Parametric methods are potentially more reliable Median polish – only use MM for background adjustment; fit parameters of linear model robustly using median polish, taking into account all available arrays; fit extra GC-content parameter