[BioC] Agilent Mouse 8x60K array

[Gordon K Smyth]

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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