[BioC] plot methylation figure for end analysis

[Michael Stadler]

I agree that the profile or metagene plot is useful and widely applied.

Creating such a plot consists of several steps, including:
- quantifying and accumulating signal around sites of interest
- normalization
- visualization

The qProfile() function in QuasR was designed to do the
quantification/accumulation, which is not straightforward to implement
efficiently and could use a lot of memory.

This leaves it to the user to choose an appropriate approach for the
normalization (if at all necessary) and gives full flexibility for plotting.

qProfile returns a list of vectors, so normalization and plotting are
not very complex (as shown in the example mentioned by Tim, in the QuasR
vignette in section 6.1.6 "Create a genomic prole for a set of regions
using qProfile").

I would be interested to know your opinions - are these still too
complex, so that a plot method would be of high value?

Michael



On 13.09.2013 07:21, bach le wrote:
> Many thanks. I've checked BSmooth but it seems not to be what I mentioned.
> As Tim said, this kind of figures are used quite often to illustrate the
> difference between groups of objects. Yet till now it seems that there has
> not been any offer from BioC (afa I may know). It would be great if someone
> can help to implement it.
> Cheers,
> 
> 
> On Fri, Sep 13, 2013 at 3:56 AM, Tim Triche, Jr. <tim.triche at gmail.com>wrote:
> 
>> Bsmooth/bsseq (and QuasR, as well) are great for manipulating and analyzing
>> BSseq data, but there's something in this email that I've never really
>> found a great solution for, and that is so-called "profile" or "metagene"
>> plots (the latter is a confusing bit of terminology, given that it is
>> re-used for linear combinations of gene effects in expression studies).
>>
>> Suppose you wanted to look at the distribution of H3K4me3 or H3K27me3
>> ChIP-seq density relative to the start of (or exon/intron junctions within,
>> or enhancers whose activity was linked to, ...) transcribed vs. silent
>> genes across a genome.  Suppose you also wanted to look at methylation
>> proportions relative to the same positions (again, the idea here is to take
>> a bunch of landmarks and plot a summary statistic describing the behavior
>> of some measurement relative to those landmarks, across the genome).  An
>> example of such a plot is on page 23 of the QuasR user's manual.  For
>> clarity, here's a link:
>>
>> https://dl.dropboxusercontent.com/u/12962689/profile.png
>>
>> This seems like something that is so incredibly common it ought to be a
>> generic function within BioC somewhere, but I've never found a really easy
>> way to do it.  If I want to plot a density matrix mat[x,y] for alhl x and
>> all y, I can call image(mat) or persp(mat) and that's that.  Is there
>> something like this within biocGenerics of which I've remained completely
>> ignorant, or... ?
>>
>> If it isn't a generic somewhere, I think I may have to implement one.
>>
>>
>>
>>
>> On Thu, Sep 12, 2013 at 1:32 AM, Alex Gutteridge <alexg at ruggedtextile.com
>>> wrote:
>>
>>> On 12.09.2013 08:25, Gia [guest] wrote:
>>>
>>>> Hi,
>>>> I would like to ask if anyone experienced with creating figures from
>>>> BS-Seq methylation data for end analysis of genes (or any genomic
>>>> features), like what was presented in the following link:
>>>> http://www.nature.com/ng/**journal/v39/n1/fig_tab/ng1929_**F4.html<
>> http://www.nature.com/ng/journal/v39/n1/fig_tab/ng1929_F4.html>
>>>> It would be appreciate if you can show how to create this kind of
>>>> figures using any Bioconductor packages.
>>>> Cheers,
>>>>
>>>>  -- output of sessionInfo():
>>>>
>>>> None
>>>>
>>>> --
>>>> Sent via the guest posting facility at bioconductor.org.
>>>>
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>>>
>>> I've only skimmed through that paper but that figure isn't showing BS-Seq
>>> data as far as I can see - it's showing the results of an array / IP
>> based
>>> approach to methylation quantitation. BSmooth is one of the best packages
>>> I've found for manipulating BS-Seq data. If you get your data in there
>> you
>>> could derive analogous statistics (i.e. methylation levels) pretty
>> easily.
>>> Then any of the base plotting functions will give you similar graphs.
>>>
>>> --
>>> Alex Gutteridge
>>>
>>>
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>>
>>
>> --
>> *He that would live in peace and at ease, *
>> *Must not speak all he knows, nor judge all he sees.*
>> *
>> *
>> Benjamin Franklin, Poor Richard's
>> Almanack<http://archive.org/details/poorrichardsalma00franrich>
>>
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