Why RMA method is better than Median method for normalising microarray data?

Question

[Manoj]

Why RMA method is better than Median method for normalising microarray data from .cel file?

[Devon Ryan]

Have you read any papers on RMA?

[Manoj]

Sir , I have read some papers .. but I am unable to understand ... I would be glad if you help me ..

Thanking you in advance.

Answer 1

[Manoj]

Sir , after lot of search I found this ..

There are many normalization methods! Which of the methods is most stable and gives best results is dependent on the type of data, the image analysis program, etc. To determine the best method, it is a good idea to try several methods initially on a few datasets and inspect the results visually using controls.

a) Scale normalization: the simplest way to normalize data is simply to adjust the scale of the data, e.g. set the median of differences to 0. This does not consider any region or intensity dependent effects, however.

b) Lowess (aka loess): Local regression does take into account intensity dependent effects and might partially correct for background (additive) effects. There are also variants that take into account local effects, e.g. print-tip lowess. This type of normalization is most commonly used for two-colour arrays.

c) Quantile: Similar idea to scale normalization but more drastic, as all of the various quantiles are adjusted and not only the 50% quantile (median). This type of normalization is most commonly used for affymetrix arrays.