First preprocessing step is we normalize data using RMA method, next we applied baseline shift for the datasets by shifting all measurements upwards by a number of means (or averages).
This process then followed by performing global mean adjustment. First, the global mean of all intensities of all datasets is calculated. Then, the difference between each individual mean and the global mean is calculated. This difference value is then added to (or subtracted from) each individual expression intensity value on each dataset. The result is that all datasets now have the same overall mean.
Some of our current questions are: 1.This normalization method is correct for ML classification (like SVM etc) 2.any one can explain with R code what is correct method of preprocessing of microarray data for ML classification and please share the tutorial for this....
