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Question: lumi for Illumina methylation data - understanding the colour adjustment
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gravatar for Pan Du
7.8 years ago by
Pan Du1.2k
Pan Du1.2k 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.
ADD COMMENTlink modified 7.8 years ago by Lavinia Gordon480 • written 7.8 years ago by Pan Du1.2k
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gravatar for Lavinia Gordon
7.8 years ago by
Lavinia Gordon480 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
ADD COMMENTlink written 7.8 years ago by Lavinia Gordon480
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. >> >> >> >> >> >
ADD REPLYlink written 7.8 years ago by Pan Du1.2k
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
ADD REPLYlink written 7.8 years ago by Lavinia Gordon480
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