aluminum methylation arrays or other types of methylation arrays.
And so the idea here is you, similarly, do this sort of bisulfite conversion step,
but then you do hybridization to a microarray.
And then after hybridization to a microarray, you have both
the probe that corresponds to the case where you have an unmethylated probe and
a methylated probe, and you measure the intensity of both of those.
And then you use that same information to try to estimate how much methylation is
happening at a particular locus.
So the first step in either of these processes, whether you use bisulfite
sequencing or DNA methylation arrays, is to normalize the samples.
And so you have to process a couple of different things.
First, you want to be able to detect whether there was methylation
that was at that locus.
You have to compare the methylated DNA to the unmethylated DNA, whether
that's through the bisulfite conversion comparison with the sequence samples, or
with the hybridization signal.
And so there's a couple of packages that you can use to do this,
the charm package and the minfi package.
The minfi package specifically deals with both bisulfite sequencing and microarrays,
and charm focuses more exclusively on the microarray version.
Now, I'm talking about microarrays here although we primarily talk about
sequencing throughout the class because still a large number of studies that
are performed in DNA methylation are focused on using microarray technology,
especially for large samples.
So the next step is smoothing.
And so often you see DNA
methylation data that look like this where you measured it across the genomes.
So this is genomic location on the x-axis, and
this is sort of a methylation measurement after normalization on the y-axis.
And you can see that it sort of jumps around, and so
the idea is you want to find sort of clumps of points like this that are above,
Jeff Leek, PhDAssociate Professor, Biostatistics
