Dear conductors,



I solved the normalization problem on my own. I was mainly using limma
package.



The data are obtained from 4 home-made oligonucleotide microarrays. They
consist of about 800 50-nt-long DNA probes, complementary to the genes known
to be implicated in disease development.



First I’ve checked the quality of the data by creating MAplot and intensity
profiles of both dyes using  plotDensities() function. In order to compare
changes of the data during normalizations  I thought it would be nice to
have boxplot, so I made one on my raw data. Creating spottypes was also a
good idea. My MAplot looks much better and it’s more clear.



I was’t sure which type of normalization I should choose, so I prepared all
possible combinations of normalization methods. I was observing the changes
on the MAplots and boxplots. I decided also to perform background
correction. My results suggest that the normexp. and half are the most
suitable methods for my dataset. They work nicely with loess and
robustspline normalizations. It was also necessary to use normalization
between arrays and the promissing results were given by scale and aquantale
methods.



The most suitable normalization model for my dataset is: Normexp.+ Standard+
Aquantile**

and it gave much better results than half + „robustspline”+ scale (after all
those operations the banana shape still was present).



I couldn’t use the printtiploess, because there is a problem with my
microarray dimensions. Each chip consists of two subarrays, with different
size, so I decided to replace printtiploess with a loess method.



I will also try to find a solution to my sctatistical problem or...I just
ask my local satistician:)



Best,

B.

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