[BioC] quality assessment and preprocessing for tiling array-based CGH data

[Zhi-Qiang Ye]

2008/11/9 Sean Davis <sdavis2 at mail.nih.gov>:
>
>
> On Tue, Nov 4, 2008 at 1:13 AM, Zhi-Qiang Ye <yezhiqiang at gmail.com> wrote:
>>
>> 2008/10/22 Sean Davis <sdavis2 at 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
>

Hi, Sean

    Thank you very much for your kind help :)

Best Regards,
ZQ Ye