[Bioc-devel] plotPCA for BiocGenerics

While I tend to agree with you that PCA is too big an operation to be
hidden within a plotting function (MDS is an edge-case I would say), I
can?t see how we can ever reach a point where there is only one generic
plot function. In the case of PCA there is a number of different plot-types
that can all lay claim to the plot function of a PCA class, for instance
scoreplot, scatterplot matrix of all scores, biplot, screeplot, accumulated
R^2 barplot, leverage vs. distance-to-model? (you get the idea). So while
having some very well-thought out classes for very common result types such
as PCA, this class would still need a lot of different plot methods such as
plotScores, plotScree etc (or plot(?, type=?score?), but I don?t find that
very appealing). Expanding beyond PCA only muddles the water even more -
there are very few interesting data structures that only have one visual
representation to-rule-them-all?

just my 2c

best
Thomas

Date: Mon, 20 Oct 2014 18:50:48 -0400
From: Kevin Coombes <kevin.r.coombes at gmail.com>

Well. I have two responses to that.

First, I think it would be a lot better/easier for users if (most)
developers could make use of the same plot function for "basic" classes
like PCA.

Second, if you think the basic PCA plotting routine needs enhancements,
you still have two options.  On the one hand, you could (as you said)
try to convince the maintainer of PCA to add what you want.  If it's
generally valuable, then he'd probably do it --- and other classes that
use it would benefit.  On the other hand, if it really is a special
enhancement that only makes sense for your class, then you can derive a
class from the basic PCA class
    setClass("mySpecialPCA", contains=c("PCA"), *other stuff here*)
 and implement your own version of the "plot" generic for this class.
And you could tweak the "as.PCA" function so it returns an object of the
mySpecialPCA class. And the user could still just "plot" the result
without hacving to care what's happening behind the scenes.

On 10/20/2014 5:59 PM, Michael Love wrote:

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 <mailto:kevin.r.coombes at gmail.com>> wrote:

   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 <mailto: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
       <mailto: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
           <mailto: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
               <mailto: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
       <mailto:davide.risso at berkeley.edu>

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   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 <mailto:davide.risso at berkeley.edu

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