Hi all,

I want to normalize some data comming from a single chanel non affy
platform.- I’d like to use the rma( ) function, but I would need to create
first an Affybatch object from the data that I have, a task that I find a
bit difficult to do given the data that I have. I think that would be much
more easier to use the bg.adjust ( ) internal function, but I’m not sure if
I can apply this function directly to my data (a matrix with just raw
foreground intensities).

I’ve read the help file of this function but I still have some doubts. I
have understood that  bg.adjust ( ) is the internal function that uses rma
( )  to correct for the background bias. I figure that the input that it
needs is just a matrix with the raw intensities in natural scale and from
here, the function estimates the signal intensities corrected by the
background without using the background measures at all. Am I right?. My
data does not have PM and MM intensities, all  the values are “PM”  and I
have a probe per gene, Would still it be correct under theses cirmcumstances
using the bg.adjust ( ) function?


Other authors suggest to estimate the true signal intensities  just by
substracting the median of the background probe intensities from the mean of
the foreground ones, and setting the negative values, let’s say 0.5. I think
that this method is not completely correct, what’s your opinion about
this?.-

Moreover, I want to use the quantiles method to normalize the same data,
would it be right to apply the normalize.quantiles ( ) function directly to
the previous background corrected matrix?-

Thanks a lot.-

Pedro





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