[BioC] Agilent Mouse 8x60K array

[Nathan (Nat) Goodman]

Thanks for the detailed response, Gordon.  limma is a great package that we use all the time.  Evidently, we should be using it even more!  It will take me a few days to work though your suggestions.  I'll get back with any questions along the way and conclusions at the end.

Thanks again,
Nat

On Feb 5, 2013, at 4:48 PM, Gordon K Smyth wrote:

> Dear Nat,
> 
> Are your arrays hybed with one dye or two?  I will assume one.
> 
>> Date: Mon, 4 Feb 2013 12:31:36 -0800
>> From: "Nathan (Nat) Goodman" <ngoodman at systemsbiology.org>
>> To: "James W. MacDonald" <jmacdon at uw.edu>
>> Cc: bioconductor at r-project.org
>> Subject: Re: [BioC] Agilent Mouse 8x60K array
>> 
>> Hi Jim
>> 
>> Everything you mentioned is good, and I agree straightforward to program up by hand.  The other things I'd like to do are equally obvious and probably not too hard.
>> 
>> 1) Use the negative controls to define the limit of detection.
> 
> The propexpr() function in the limma package does this.
> 
> The nec() and necq() functions also use the negative controls in the context of background correction and normalization, but the local background estimates provided by Agilent should be subtracted first.
> 
>> 2) Use the positive controls to define the standard curve -- aka normalization -- or at least confirm that normalization worked as expected.
> 
> limma also uses the controls, and not just the positive controls, in the normalization process.
> 
> limma offers rich possibilities to up-weight or down-weight different types of controls in various ways, even to determine the normalization entirely from controls, but I doubt that there is any need to do this for a Mouse 8x60K array.
> 
> To examine how well the normalization has worked with respect to the controls, use the plotMA() function after setting probe control status appropriately.
> 
>> 3) Propagate the variance estimates from the replicated probes to downstream tests of significance.
> 
> limma does this using duplicateCorrelation().  Otherwise, if the replicated probes don't fit into the duplicateCorrelation framework, then propogating the variances is essentially impossible, for the reasons explained by Jim.
> 
> BTW, you asked for an Agilent equivalent of the affy package, but the affy package doesn't do (2) or (3) for Affymetrix arrays.
> 
> Best wishes
> Gordon
> 
>> Before I forget, I want to thank you for taking the time to engage in this conversation.  I really appreciate the help.
>> 
>> Best,
>> Nat
>> 
> 
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