Hi Pan, Thanks for this, this answers my question. With many thanks for your time, Lavinia. On 22/12/2010 3:43 AM, Pan Du wrote: > Hi Lavinia > > You are right. For Infinium methylation chip, the methylated and > unmethylalted probes are always in the same color channel. When performing > non-linear color adjustment (like quantile or other curve fitting methods), > the ratio of methylated and unmethylalted probe intensities may change. For > example, the intensities in the high range may adjust differently from those > in the low intensity range, which will also cause beta or M-value change > slightly. > > As any adjustment or normalization may also bring additional bias to the > data, if the color imbalance between two color channels are consistent > across the entire dataset, we do not recommend to perform aggressive color > adjustment, like quantile adjustment. However, if the color imbalance are > inconsistent across samples, then the color adjustment becomes important. > > Hope this clarifies your question. > > > Pan > > > On 12/20/10 10:20 PM, "Lavinia Gordon"<lavinia.gordon at="" mcri.edu.au=""> wrote: > >> Hi Pan >> >> Thank you for your reply, very prompt at this busy time of year! >> I think I am getting confused with how the HumanMethylation27 array >> works, or perhaps I don't understand the colour adjustment properly. >> My query is really, if the methylated and unmethylated are the same >> colour, and that colour is adjusted (so I assume Cy3 is adjusted >> differently to Cy5, as Cy5 is incorporated differently), why are the >> intensities of the methylated adjusted differently to the intensities of >> the unmethylated if they are the same colour? >> >> Many thanks, >> >> Lavinia. >> >> On 21/12/2010 2:56 PM, Pan Du 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. >>> >>> >>> >>> > > > > -- Senior Bioinformatics Officer Murdoch Childrens Research Institute Royal Children's Hospital Flemington Road Parkville Victoria 3052 Australia www.mcri.edu.au