[BioC] Agilent one colour

Sean Davis seandavi at gmail.com
Tue Mar 23 07:49:42 CET 2010

On Tue, Mar 23, 2010 at 12:47 AM, Yogi Sundaravadanam
<yogi.sundaravadanam at agrf.org.au> wrote:
> Hi Sean,
> Thanks for this.
> A bit off topic, but with two colour agilent array, I have been using just the LogRatio for downstream analysis. It makes me wonder if I should be normalising this as well.

Hi, Yogi.  You might need to look into some background on microarray
data analysis.  Generally, if you want to compare between microarray
experiments, you'll want to do a significant amount of quality control
and assessment to be sure and I think most people would agree that
normalization usually improves downstream inference.  In other words,
QA/QC and normalization are probably necessary steps in microarray
data analysis.

> Sorry, but I have never worked with Agilent, and FE doesn't say much.

The Agilent FE manual does not lay out an analysis recipe, I agree.
However, it is useful for determining what data are represented in the
FE .txt file.  I would recommend having it handy when analyzing
Agilent data, in any case.


> -----Original Message-----
> From: Sean Davis [mailto:seandavi at gmail.com]
> Sent: Tuesday, 23 March 2010 10:38 AM
> To: Yogi Sundaravadanam
> Cc: bioconductor at stat.math.ethz.ch
> Subject: Re: [BioC] Agilent one colour
> On Mon, Mar 22, 2010 at 6:23 PM, Yogi Sundaravadanam
> <yogi.sundaravadanam at agrf.org.au> wrote:
>> Hi all,
>> I am working with Agilent one-colour array, and I find working with gProcessedSignal to be just horrible. I was just wondering if people have had experiences normalising one-colour array, and what works best.
>> I was wondering if Background subtraction, followed by quantile normalisation is preferable?
> Hi, Yogi.
> gProcessedSignal is background-subtracted already, I believe, but it
> is not normalized.  Very rarely does the data from a one-color array
> platform come normalized between arrays, a necessary step, I think.
> Quantile normalization between arrays is a reasonable possibility,
> yes.  As with any array study, you'll want to do some quality
> assessment both before and after normalization.
> Sean

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