[BioC] Question about normalization of microarray data

michael watson (IAH-C) michael.watson at bbsrc.ac.uk
Thu Nov 18 11:40:57 CET 2004

Just a quick thought (off the top of my head) - presumably the spots where there is only signal in the reference are only of interest in a "they're off" kind of way - you won't (and can't) actually be interested in fold changes for those spots.

Therefore, you could remove these spots, normalise the rest of the data according to Loess, and then either just analyse that data, or replace the spots you removed with a value which you are satisfied means "off" in the experimental sample.


-----Original Message-----
From: Johan Lindberg [mailto:johanl at biotech.kth.se] 
Sent: 18 November 2004 08:15
To: bioconductor at stat.math.ethz.ch
Subject: [BioC] Question about normalization of microarray data

Hi all. I have a question about normalization of microarray data.
In our lab we use in-house spotted cDNA arrays. We have so far used commercial reference when doing reference design. We are now trying a new approach but we have problems with normalization. What we have done is to pool product from every spot on the chip and done in vitro transcription on the PCR product. So we have RNA corresponding to every spot on the chip. Then this is used as a reference. It is much cheaper and we get signal from every spot on the chip instead of having spots with no signal in both channels. But when one looks at an MA-plot the plot will be skewed towards the reference. There are about (in this pilot case) 2000 spots that only give signal in the reference channel (which will skew the MA-plot). This will make many assumptions not correct when normalizing the data, e.g. using lowess normalization assuming that the ratio R/G should be 1 for most spots. 
Since the case for this kind of data is that one channel should be much stronger than the other, and we want to keep the normalization within slide (to be able to correct for spatial biases and intensity dependent) the only way I could think of is by spotting a lot of control spots (not present in the tested RNA or the reference RNA) and use these to normalize the data. 
Any comments of tips of how to normalize this kind of data are greatly appreciated.
Best regards 
//Johan Lindberg
Johan Lindberg
Royal Institute of Technology
AlbaNova University Center
Stockholm Center for Physics, Astronomy and Biotechnology Department of Molecular Biotechnology 106 91 Stockholm, Sweden
Phone (office) +46 8 553 783 44
Fax + 46 8 553 784 81
Visiting adress Roslagstullsbacken 21, Floor 3
Delivery adress Roslagsvägen 30B http://www.biotech.kth.se/molbio/microarray/index.html

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