[BioC] Agilent 2 colour spike-in controls

[Naomi Altman]

I am not familiar with this chip, but we did find that the controls 
on our custom Agilent chips had striking dye bias.
However, the bias was similar on every chip.

I usually do a scatterplot matrix of every channel against all other 
channels with the same treatment.  (logarithmic scale) Even
without normalization, these should be diagonal, although there is 
usually some curvature.  If they are highly scattered, you might have
a problem.  We found that the labeling dyes can degrade rapidly under 
some conditions.

--Naomi

At 06:25 AM 5/7/2009, Nathan S. Watson-Haigh wrote:
>A quick question and hopefully a quick answer in reply....
>
>I have data from Agilent bovine chips. Are the spike-in probe replicates
>(of which there are 32 on each chip) supposed to be highly replicable on
>an MA plot? Some of my chips show good reproducibility of the spike-in's
>on MA plots, but others show streaks running bottom left to top right on
>the MA plot. I'm wondering if this may indicate problems with some of
>the chips!?
>
>Find my code below.
>
>Cheers,
>Nath
>
>
>My SpotTypes.txt file is like:
>
><SpotTypes.txt>
>SpotType        ControlType     ProbeName       col
>Other   *       *       white
>Probe   0       *       black
>Negative        -1      *3xSLv1*        blue
>E1A 1   1       *E1A_r60_1$     red
>E1A n11 1       *E1A_r60_n11$   pink
>E1A a20 1       *E1A_r60_a20$   brown
>E1A 3   1       *E1A_r60_3$     orange
>E1A a104        1       *E1A_r60_a104$  yellow
>E1A a107        1       *E1A_r60_a107$  green
>E1A a135        1       *E1A_r60_a135$  blueviolet
>E1A a22 1       *E1A_r60_a22$   cyan
>E1A a97 1       *E1A_r60_a97$   bisque4
>E1A n9  1       *E1A_r60_n9$    aquamarine
></SpotTypes.txt>
>
><code>
>RG <- read.maimages(files=targets$FileName, source="agilent",
>names=targets$Name, )
>spottypes <- readSpotTypes()
>RG$genes$Status <- controlStatus(spottypes, RG)
>
>nArrays <- ncol(RG)
>png(file = "Raw_MA.png", type = "cairo1",
>width=5*ceiling(sqrt(nArrays)), height=5*ceiling(sqrt(nArrays)),
>units="in", res=300)
>par(mfrow=c(ceiling(sqrt(nArrays)), ceiling(sqrt(nArrays))),
>mar=c(5.1,4.1,3,1))
>for(i in 1:nArrays) {
>         plotMA(RG, array=i, pch=16)
>}
>dev.off()
></code>
>
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Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111