Question: lumi for Illumina methylation data - understanding the colour adjustment
8.3 years ago by
Pan Du • 1.2k
Pan Du • 1.2k wrote:
Hi Lavinia 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! Pan 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 > adjustment. > > So, for example, probe A is a red probe, and has a (GenomeStudio) > unmethylated intensity of 2205 and a methylated intensity of 2822. > 2822/(2822+2205) > beta = 0.5613686 > After colour adjustment, it has an unmethylated intensity of 1718.882 > and a methylated intensity of 2576.6539: > 2576.6539/(2576.6539+1718.882) > 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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