[BioC] calculating replicate average in exprs(eset)

[Georg Otto]

Dear Sean,

thanks a lot for your advice! In principle I completely agree, and I
often use limma for blocking, downweighting etc.  However, somtimes I
would like to do some quick filtering of genes, eg. to find out how
many are above a certain intensity level, how many have present calls
in more than x experiments etc.

Best,

Georg

Sean Davis <sdavis2 at mail.nih.gov> writes:

> On 11/29/05 12:26 PM, "Georg Otto" <georg.otto at tuebingen.mpg.de> wrote:
>
>> Hi,
>> 
>> I have a problem with an exprSet, that consists of 16 samples with 2
>> replicates each, i.e. 32 arrays.
>> 
>> Using
>>> exprs(eset)
>> 
>> I get the expression values for each gene in each array, with the two
>> replicates as adjacent columns, like this:
>> 
>> A1 A2 B1 B2 C1 C2 D1 D2 ...
>> 
>> I would like to calculate the mean of the two replicates for each gene
>> and generare a matrix of the mean values. How can I do this?
>
> Hi, Georg.
>
> Instead of averaging, I would suggest using a method of analysis that allows
> you to appropriately replicates as such.  Look at limma and using the block
> argument.
>
>
>> 
>>> Calls<-mas5calls(AffyBatch)
>>> exprs(Calls)
>> 
>> I get a data frame with mas5 calls (P, A, or M). I would like to test,
>> if the calls for the two replicates are the same and return the call
>> to a data frame, otherwise return NA. Any idea how to do this?
>
> Again, I would try to use all the data as best you can.  You could set
> values in your expression matrix to NA or downweight probesets that have an
> absent call if you are using limma.
>
> There are many ways to do these things, but I think averaging and other
> "lumping" techniques may not be the right way to go.
>
> Sean