[Bioc-devel] plotPCA for BiocGenerics

davide risso risso.davide at gmail.com
Mon Oct 20 23:15:19 CEST 2014

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

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.


On Mon, Oct 20, 2014 at 1:26 PM, Kevin Coombes <kevin.r.coombes at gmail.com>

>  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
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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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