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

[Zhi-Qiang Ye]

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.

ZQ Ye