watch out for

1) dye bias (R/G)
2) probe bias (final base before the interrogated site, at least)

for Infinium arrays there has been some discussion as to whether quantile
normalization squashes subtle biological differences, thus exploration of
#2.  That's considering that the dye bias is mostly removed from play.  With
GG methylation arrays, the smaller number of probes (especially a custom
array!) and presence of dye bias suggests treading carefully.  If you have
replicates among your data, try looking at the effects of affine, quantile,
and PLM/BLM normalization between each.  I'd compare notes, but we're on
Infinium.

I personally have been working with twin data on the Infinium arrays, along
with a high number of replicates from a single whole blood sample (my P.I.
got burned by a subtle Illumina omission when his first data set was run).
While the noise from identical samples is much smaller than that within twin
pairs and between unrelated individuals or different cell types, it's there,
and may be significant enough (even with just 384 targeted probes, as yours)
to consider normalization.  Again, my data is unusual in that we are more
interested in differences within pairs than anything else, but for
permutation testing I do want a common baseline.  So we are pursuing
appropriate normalization strategies.

If memory serves, Roche/Nimblegen proudly notes that they do not normalize
their methylation array results, so there's another perspective.

Hope this helps,

--t


On Fri, Aug 21, 2009 at 4:20 AM, r.kandimalla <r.kandimalla@erasmusmc.nl>wrote:

> Dear all,
>
> I would like to know some information regarding normalisation and
> statistics to apply on my custom GGMA assays.
>
> Is normalisation necessary for these assays ? if yes what do you suggest ?
>
> I havent applied any statistics on the data to find differential
> methylation among subgroups, instead i just took the beta values and did
> comparisons. To elaborate, i have two different subgroups of tumors in my
> data set. To compare them i just took the ratio of avg beta of one sub
> groups to avg beta of another subgroup and came up with significant list of
> genes (i have chosen the ratio > 2 to be significant in that particular
> comparison).
>
> This was a validation assay of our genome wide screen using agilent arrays.
> With custom GGMA consisting of 384 probes, we were able to validate quiet
> some data and is encouraging, but i have questions regarding the analysis of
> ggma data, whether im doing something wrong ??
>
> Your input is highly appreciated.
>
> Best regards,
>
> Raju
>
>
> --
> Raju kandimalla, PhD student
> Department of Pathology
> Josephine Nefkens Institute
> Erasmus MC, Be-302
> P.O. Box 2040
> 3000 CA Rotterdam
> The Netherlands
> Tel: +31-10-7043093
>
> _______________________________________________
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>



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but I've seen what it can do to skyscrapers.

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