[BioC] Agilent CGH data

[jhs1jjm at leeds.ac.uk]

Quoting jhs1jjm at leeds.ac.uk on Tue 25 Sep 2007 20:55:53 BST:

> Quoting Sean Davis <sdavis2 at mail.nih.gov> on Tue 25 Sep 2007 17:50:10 BST:
>
> > Sean Davis wrote:
> > > jhs1jjm at leeds.ac.uk wrote:
> > >> R 2.5.0 on openSUSE 10.2 x86_64.
> > >>
> > >> Hi,
> > >>
> > >> I'm using the arrayQuality package to analyse 3 44k Agilent CGH arrays
> > with the
> > >> aim of identifying regions of gain/loss.
> > >>
> > >> With the HTML report generated using the agQuality function i'm not
> > getting the
> > >> coloured loess curve on the MA plot for raw M. Additionally i'm only
> > getting 1
> > >> value for the dot plot of controls normalized M values (-)3xLv1 (n=330)
> > and
> > >> likewise for the control A values. Alternatively when I run the
> > maQualityPlots
> > >> function on my mraw object created in marray  I get these but don't get
> > the
> > >> comparative box plot.
> > >>
> > >> Firstly is this important as I'm unsure of how useful the comparative
> > boxplots
> > >> are as some values are NA? Secondly is this an appropriate tool to use
> and
> > are
> > >> there any others that may be of more use both for quality control and
> for
> > >> analysis further down the line? Thankyou kindly for any input.
> > >
> > > Hi, John.  Are these CGH arrays or expression arrays?  The two probably
> > > need some different treatment.  You imply you are using CGH arrays in
> > > looking for regions of gain/loss.  Is this the case?
> >
> > And, then, of course, there is the subject, "Agilent CGH data"--SORRY!
> >
> > In this case, you do not want to rely on loess or other non-linear
> > normalization methods.  Also, the MA plots for the best arrays DO show a
> > positive slope--this is totally expected and sought after.  In other
> > words, with higher M-values, we expect higher A-values.
> >
> > We have found that a pretty good measure of quality of CGH arrays is the
> >  dlrs:
> >
> > 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)
> >   }
> >
> > Run this on the Log ratios (ordered by chromosome and position).  Good
> > values are less than 0.2 or so, but even some slightly higher can be used.
> >
> > As for analysis, you may want to look into the snapCGH package, as it
> > allows multiple analyses to be run with the same data structures.
> >
> > Sean
> >
> Hi Sean,
>
> I'd been using the loess method with the marray package, I was going by a
> paper
> i'd read regarding Agilent feature extraction software vs other pre
> processing
> methods (Zahurak et al 2007 I think). The MA plot for the raw intensities
> does
> show a positive slope, in this case which normalization method should I use?
>
> I ran dlrs and got the following:
>
> > qual <- dlrs(CNA.object[,3])
> > qual
> [1] 0.5586258
> > qual2 <- dlrs(CNA.object[,4])
> > qual2
> [1] 0.5778217
> > qual3 <- dlrs(CNA.object[,5])
> > qual3
> [1] 0.5625572
>
> As you can see I used the CNA.object for which you kindly provided a function
> to
>  separate the ch from location and order them. Having done that I realized
> that
> the log to ratios I'd used are from the mnorm object (mnorm at maM), I went back
> and created a second CNA.object as follows:
>
> > CNA.object2 <-
> CNA(logratio,agilentInfo$chromosome,agilentInfo$location,data.type="logratio")
>
> and then got the following:
>
> > qual <- dlrs(CNA.object2[,3])
> > qual
> [1] 0.5802947
> > qual2 <- dlrs(CNA.object2[,4])
> > qual2
> [1] 0.6258332
> > qual3 <- dlrs(CNA.object2[,5])
> > qual3
> [1] 0.5925305
>
> Does this mean the quality of the arrays is poor? How would I go about
> referencing this (for my dissertation)?
>
> Thanks again
>
> John
>
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>
Sean,

Don't worry about the normalization question. I've seen that this is dealt with
in the snapCGH vignette so will look at that.

Regards

John