[BioC] limma package
yunie at caltech.edu
Fri Dec 12 00:44:16 MET 2003
I'm able to read the Agilent flat file using the read.table function but
what if I have a large amount of arrays to input. This function seems to
only input one table at a time. Are there any ways to read in multiple files
simultaneously (as in the read.maimages function)? Can you please list the
commands/functions I would need to perform the reading in normalized signals
task? Then I can go and look through help in R for them?
How would I go about defining the weight matrix for each array since the
source for the Agilent analysis program is not included in the read.maimages
function? I have a column of filter values in the Agilent txt file which was
read in through read.maimages (manually through the column argument). How
can I define this filter column?
Thanks for you time,
From: Gordon Smyth [mailto:smyth at wehi.edu.au]
Sent: Wednesday, December 10, 2003 4:45 PM
To: Anna Cao
Cc: bioconductor at stat.math.ethz.ch
Subject: Re: [BioC] limma package
At 06:15 AM 11/12/2003, Anna Cao wrote:
>I'm trying to normalize between arrays using the limmma package so I
>can run SAM. I want to know whether we can read log2 ratio data into
>R? I'm using Agilent's extraction program to extract cDNA arrays
>data and the image analysis program from Agilent automatically
>normalize within array, giving the normalized intensity and log
>ratio. Is there anyway I can by-pass reading Mean/Median foreground
>and background intensity and directly feed the processed (background
>subtracted and normalized) intensity into R?
This is easy in R. Use the Agilent flat file, use read.table(), then
collect the normalized columns into a matrix in R. The matrix can then but
used as input for example to the lmFit() command in limma. You'll have to
do the input yourself though, there aren't any canned limma commands to do
it. If you need help with this, you might want to ask for help on the
R-help mailing list.
>Another question: Can I also input a column that filters spots which
>were unsatisfactory according to the image analysis program? And how
>can I can remove these bad spots from further calculation in R using
>the limma package or any other functions in R?
Using limma, you set the appropriate elements of the 'weights' matrix to
zero for those spots. This applies to both normalization and linear
modelling functions in limma. Normally the weights matrix is set
automatically using the wt.fun argument of read.maimages, but you will need
to create it yourself if you're reading in the data manually as it were.
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