On Tue, Nov 4, 2008 at 1:13 AM, Zhi-Qiang Ye <yezhiqiang@gmail.com> wrote:

> 2008/10/22 Sean Davis <sdavis2@mail.nih.gov>:
> > You generally will not want to do any normalization besides a possible
> > shift of the center.  Any linear normalization that affects the slope
> > of the M vs. A plot or nonlinear normalization will likely decrease
> > signal.  As for quality control, a good, general measure to track is
> > the dlrs, a robust measure of the standard deviation.
> >
> >
> > dlrs <-
> >  function(x) {
> >    nx <- length(x)
> >    if (nx<3) {
> >      stop("Vector length>2 needed for computation")
> >    }
> >    tmp <- embed(x,2)
> >    diffs <- tmp[,2]-tmp[,1]
> >    dlrs <- IQR(diffs)/(sqrt(2)*1.34)
> >    return(dlrs)
> >  }
> >
> > For agilent arrays, most of the dlrs should be around or under 0.2,
> > generally.  However, this might vary a bit based on lab-to-lab
> > variation.  In any case, if there is a significant outlier, that is
> > suspect.  The input to the above function is the log ratios for a
> > single array arranged in chromosome and position order.
>
> Hi, Sean
>
>    What is the base of the log ratios for input to dlrs, 2, 10 or e?
> Thanks.
>

Sorry to take so long to get back to this.

To answer the question, it doesn't matter.  However, the cutoff of 0.2 is
based on log10.  The DLRS approximates a standard deviation, so you can it
to determine what you can see on the array to some degree.

Sean

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