[BioC] lumi for Illumina methylation data - understanding the colour adjustment
dupan at northwestern.edu
Tue Dec 21 04:56:42 CET 2010
It is hard to answer your question without knowing the quality of your data.
My suggestion is performing visually color bias check first before applying
color bias adjustment. If the color bias is not severe, then only perform
conservative adjustment (scaling and shift adjust) or not perform any color
adjustment at all. Need to know the smooth quantile color adjustment has
strong assumption of the data (same distribution of two color channels).
Quantile normalization may bring bias after adjustment, this is the same as
the expression microarray normalization. If you would like to, please send
me the plot produced by plotColorBias2D of this sample. Also, I will add
more detailed description of this in the vignette.
Thanks for reporting this!
On 12/20/10 7:06 PM, "Lavinia Gordon" <lavinia.gordon at mcri.edu.au> wrote:
> Dear Dr Du
> I am a big fan of /lumi/ and was delighted to see that you have made it
> compatible with methylation arrays. I have used these new functions on
> several of my datasets and am very happy with the alternative method of
> working with M values. I just have one query regarding the colour
> So, for example, probe A is a red probe, and has a (GenomeStudio)
> unmethylated intensity of 2205 and a methylated intensity of 2822.
> beta = 0.5613686
> After colour adjustment, it has an unmethylated intensity of 1718.882
> and a methylated intensity of 2576.6539:
> beta = 0.5998446
> Why, if the methylated is the same colour as the unmethylated, has the
> unmethylated intensity decreased by 23% but the methylated by only 9%?
> with thanks for your time,
> Lavinia Gordon.
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