[Bioc-devel] plotPCA for BiocGenerics

Michael Love michaelisaiahlove at gmail.com
Mon Oct 20 23:59:30 CEST 2014

Ah, I see now. Personally, I don't think Bioconductor developers should
have to agree on single plotting functions for basic classes like 'PCA'
(because this logic applies equally to the situation of all Bioconductor
developers agreeing on single MA-plot, a single variance-mean plot, etc). I
think letting developers define their plotPCA makes contributions easier (I
don't have to ask the owner of plot.PCA to incorporate something), even
though it means we have a growing list of generics.

Still you have a good point about splitting computation and plotting. In
practice, we subset the rows so PCA is not laborious.

On Mon, Oct 20, 2014 at 5:38 PM, Kevin Coombes <kevin.r.coombes at gmail.com>

>  Hi,
> I don't see how it needs more functions (as long as you can get developers
> to agree).  Suppose that someone can define a reusable PCA class.  This
> will contain a single "plot" generic function, defined once and reused by
> other classes. The existing "plotPCA" interface can also be implemented
> just once, in this class, as
>     plotPCA <- function(object, ...) plot(as.PCA(object), ...)
> This can be exposed to users of your class through namespaces.  Then the
> only thing a developer needs to implement in his own class is the single
> "as.PCA" function.  And he/she would have already been rquired to implement
> this as part of the old "plotPCA" function.  So it can be extracted from
> that, and the developer doesn't have to reimplement the visualization code
> from the PCA class.
> Best,
>   Kevin
> On 10/20/2014 5:15 PM, davide risso wrote:
> Hi Kevin,
>  I see your points and I agree (especially for the specific case of
> plotPCA that involves some non trivial computations).
>  On the other hand, having a wrapper function that starting from the
> "raw" data gives you a pretty picture (with virtually zero effort by the
> user) using a sensible choice of parameters that are more or less OK for
> RNA-seq data is useful for practitioners that just want to look for
> patterns in the data.
>  I guess it would be the same to have a PCA method for each of the
> objects and then using the plot method on those new objects, but that would
> just create a lot more objects and functions than the current approach
> (like Mike was saying).
>  Your "as.pca" or "performPCA" approach would be definitely better if all
> the different methods would create objects of the *same* PCA class, but
> since we are talking about different packages, I don't know how easy it
> would be to coordinate. But perhaps this is the way we should go.
>  Best,
> davide
> On Mon, Oct 20, 2014 at 1:26 PM, Kevin Coombes <kevin.r.coombes at gmail.com>
> wrote:
>>  Hi,
>> It depends.
>> The "traditional" R approach to these matters is that you (a) first
>> perform some sort of an analysis and save the results as an object and then
>> (b) show or plot what you got.  It is part (b) that tends to be really
>> generic, and (in my opinion) should have really generic names -- like
>> "show" or "plot" or "hist" or "image".
>> With PCA in particular, you usually have to perform a bunch of
>> computations in order to get the principal components from some part of the
>> data.  As I understand it now, these computations are performed along the
>> way as part of the various "plotPCA" functions.  The "R way" to do this
>> would be something like
>>     pca <- performPCA(mySpecialObject)  # or as.PCA(mySpecialObject)
>>     plot(pca) # to get the scatter plot
>> This apporach has the user-friendly advantage that you can tweak the plot
>> (in terms of colors, symbols, ranges, titles, etc) without having to
>> recompute the principal components every time. (I often find myself
>> re-plotting the same PCA several times, with different colors or symbols
>> for different factrors associated with the samples.) In addition, you could
>> then also do something like
>>     screeplot(pca)
>> to get a plot of the percentages of variance explained.
>> My own feeling is that if the object doesn't know what to do when you
>> tell it to "plot" itself, then you haven't got the right abstraction.
>> You may still end up needing generics for each kind of computation you
>> want to perform (PCA, RLE, MA, etc), which is why I suggested an "as.PCA"
>> function.  After all, "as" is already pretty generic.  In the long run, l
>> this would herlp BioConductor developers, since they wouldn't all have to
>> reimplement the visualization code; they would just have to figure out how
>> to convert their own object into a PCA or RLE or MA object.
>> And I know that this "plotWhatever" approach is used elsewhere in
>> BioConductor, and it has always bothered me. It just seemed that a post
>> suggesting a new generic function provided a reasonable opportunity to
>> point out that there might be a better way.
>> Best,
>>   Kevin
>> PS: My own "ClassDicsovery" package, which is available from RForge via
>>     *install.packages("ClassDiscovery",
>> repos="http://R-Forge.R-project.org" <http://R-Forge.R-project.org>)*
>> includes a "SamplePCA" class that does something roughly similar to this
>> for microarrays.
>> PPS (off-topic): The worst offender in base R -- because it doesn't use
>> this "typical" approch -- is the "heatmap" function.  Having tried to teach
>> this function in several different classes, I have come to the conclusion
>> that it is basically unusable by mortals.  And I think the problem is that
>> it tries to combine too many steps -- clustering rows, clustering columns,
>> scaling, visualization -- all in a single fiunction
>> On 10/20/2014 3:47 PM, davide risso wrote:
>> Hi Kevin,
>>  I don't agree. In the case of EDASeq (as I suppose it is the case for
>> DESeq/DESeq2) plotting the principal components of the count matrix is only
>> one of possible exploratory plots (RLE plots, MA plots, etc.).
>> So, in my opinion, it makes more sense from an object oriented point of
>> view to have multiple plotting methods for a single "RNA-seq experiment"
>> object.
>>  In addition, this is the same strategy adopted elsewhere in
>> Bioconductor, e.g., for the plotMA method.
>>  Just my two cents.
>>  Best,
>> davide
>> On Mon, Oct 20, 2014 at 11:30 AM, Kevin Coombes <
>> kevin.r.coombes at gmail.com> wrote:
>>>  I understand that breaking code is a problem, and that is admittedly
>>> the main reason not to immediately adopt my suggestion.
>>> But as a purely logical exercise, creating a "PCA" object X or something
>>> similar and using either
>>>     plot(X)
>>> or
>>>     plot(as.PCA(mySpecialObject))
>>> is a much more sensible use of object-oriented programming/design. This
>>> requires no new generics (to write or to learn).
>>> And you could use it to transition away from the current system by
>>> convincing the various package maintainers to re-implement plotPCA as
>>> follows:
>>> plotPCA <- function(object, ...) {
>>>   plot(as.PCA(object), ...)
>>> }
>>> This would be relatively easy to eventually deprecate and teach users to
>>> switch to the alternative.
>>> On 10/20/2014 1:07 PM, Michael Love wrote:
>>>  hi Kevin,
>>>  that would imply there is only one way to plot an object of a given
>>> class. Additionally, it would break a lot of code.​
>>>  best,
>>>  Mike
>>> On Mon, Oct 20, 2014 at 12:50 PM, Kevin Coombes <
>>> kevin.r.coombes at gmail.com> wrote:
>>>> But shouldn't they all really just be named "plot" for the appropriate
>>>> objects?  In which case, there would already be a perfectly good generic....
>>>>  On Oct 20, 2014 10:27 AM, "Michael Love" <michaelisaiahlove at gmail.com>
>>>> wrote:
>>>>>  I noticed that 'plotPCA' functions are defined in EDASeq, DESeq2,
>>>>> DESeq,
>>>>> affycoretools, Rcade, facopy, CopyNumber450k, netresponse, MAIT (maybe
>>>>> more).
>>>>> Sounds like a case for BiocGenerics.
>>>>> best,
>>>>> Mike
>>>>>          [[alternative HTML version deleted]]
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>>  --
>> Davide Risso, PhD
>> Post Doctoral Scholar
>> Division of Biostatistics
>> School of Public Health
>> University of California, Berkeley
>> 344 Li Ka Shing Center, #3370
>> Berkeley, CA 94720-3370
>> E-mail: davide.risso at berkeley.edu
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>  --
> Davide Risso, PhD
> Post Doctoral Scholar
> Division of Biostatistics
> School of Public Health
> University of California, Berkeley
> 344 Li Ka Shing Center, #3370
> Berkeley, CA 94720-3370
> E-mail: davide.risso at berkeley.edu
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