[BioC] lumi for Illumina methylation data - understanding the colour adjustment

[Lavinia Gordon]

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.
>>>
>>>
>>>
>>>
>
>
>
>


-- 
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Murdoch Childrens Research Institute
Royal Children's Hospital
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