Dear List,
I have a set of mouse affy data. They platform is Affy mouse 430a2 chip.
There are 8 samples , 4 for each condition.
I normalized the data using rma. The array has 22090 probes originally.
Then, in order to filter out the genes which have no entrez id, are duplicates for the same gene, I used the following command .

filter <- nsFilter(eset1,require.entrez=T,remove.dupEntrez=T,var.func=IQR,var.filter=T)

This leaves me with 6579 genes after filtering. I think I loose many of the genes here. Is there a better way to do the same?.

Also, the other problem that I am facing is that after this step, I create a expression matrix of these remaining 6579 probes.

Now, in order to annotate them, I use the library mouse4302.db
I select the ids from my list and then use the following command
Symbol <- mouse4302SYMBOL[ids]

This gives me a lesser number of probes and genes. I loose more data here.
For example, I am interested in the gene DCK, I check the original annotation file of affymetrix and there are 3 probes that are present for this gene. That means that it should have annotation. But in the final dataset I do not find it.

Can anyone suggest a better method or any corrections to the approach that I am using. I eventually need to merge this data with other data from affy and check for the expression values but, i figured out that I am  not getting the right amount of genes.

Any help is much appreciated. I am a newbie to R and Biconductor, so I am sorry if it is a basic question.
Thank you all in advance for your help.
Thanks,
Himanshu

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