Single channel v two channel normalization
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SSK ▴ 10
@ssk-7679
Last seen 8.9 years ago
United Kingdom

Hi, 

I am using a two color microarray. After normalization I get values for M and A but I am looking for a single normalized expression value for each gene that you find with single color microarrays. Does anyone know how I can do so?

Thank you!

normalization limma microarray • 983 views
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Aaron Lun ★ 28k
@alun
Last seen 12 hours ago
The city by the bay

Check out the RG.MA function. This will return the unlogged red/green intensities from a (normalized) MAList object that contains M- and A-values. Okay, so this isn't a single value, but it's not obvious how you would otherwise summarize a two-colour result into a single statistic. Note, if you want to do a more rigorous separate channel analysis of two-colour data, check out Chapter 12 in the limma user's guide.

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@gordon-smyth
Last seen 1 hour ago
WEHI, Melbourne, Australia

Hi SSK,

I'm a bit worried what you are planning to do with the separate channel results. Two colour microarrays are innately competitive. It isn't usually correct to try to reverse back from M and A values to single channel expression values.

If your experiment has a common reference sample (same sample hybridized to Cy3 for all arrays for example), then you can treat the M-values more or less as you would log-expression values from a single colour technology. So, in that case, the M-values may be what you're after.

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SSK ▴ 10
@ssk-7679
Last seen 8.9 years ago
United Kingdom

Thank you for your answers. I am a bit confused. I am trying to do something similar to this paper:

http://www.ncbi.nlm.nih.gov/pubmed/8944026

It seems as though they just use a red/green ratio and compare this ratio across a subset of genes. Does this make sense?

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That approach doesn't seem to be useful for anything other than an exploratory analysis. It won't account for variability between arrays, not will it give any measure of statistical significance. More rigorous strategies are outlined in various chapters of the limma user's guide, e.g., Chapter 11.

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The paper you cite by DeRisi et al (1996) used a common reference. So in effect they just analyzed the M-values.

The microarray field has move on enormously in the past 20 years. It would be a good idea to read something more recent.

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