[Bioc-devel] SummarizedExperiment subset of 4 dimensions

Hi Wolfgang and Michael,

As Michael says, there is no redundant information in the 4D array I have,
and all the values are integers.

Of course I can simulate 4D by e.g. creating extra 3D arrays as assays
equal to the length of the fourth dimension, but that makes the assay list
a mess. It would also require me to write accessor functions that
transforms the data into 4D before subsequent calculations (or to use a for
loop..).

Another option would be to include the 4D as a multiple in the 3D, which
would not require a later transformation into 4D. If I understood correct,
the array is just a long vector, which is indexed into different
dimensions, and so everything in an SE object could as well be written as
2D. But (my belief is that) it is actually convenient to use the properties
of dimensions for arrays.

So if there is not a problem extending to 4D, I would be extremely
grateful if you could take a look at it. :)

Regards,
Jesper

On Tue, Mar 31, 2015 at 2:16 PM, Michael Lawrence <
lawrence.michael at gene.com> wrote:

One would need a long-form colData that aligns with the array.

But now I realize that's not what Jesper wants to do here, and is not how
SE is currently designed. Jesper is using the third (and now fourth)
dimension to store an additional dimension of information about the same
sample. We already support 3D arrays for this, presumably motivated VCF,
where, for example, each sample can have a probability for WT, het, or hom
at each position. In that case, all of the values are genotype likelihoods,
i.e., they all measure the same thing, so they seem to belong in the same
assay. But they're also the same biological "sample". Essentially, we have
complex measurements that might be a vector, or for Jesper even a matrix.

The important question for interoperability is whether we want there to
be a contract that assays are always two dimensions. I guess we've already
violated that with VCF. Extending to a fourth is not really hurting
anything.


On Tue, Mar 31, 2015 at 4:52 AM, Wolfgang Huber <whuber at embl.de> wrote:

Hi Michael

where would you put the ?colData?-style metadata for the 3rd, 4th, ?
dimensions?

As an (ex-)physicists of course I like arrays, and the more dimensions
the better, but in practical work I?ve consistently been bitten by the
rigidity of such a design choice too early in a process.

Wolfgang

On 31 Mar 2015, at 13:32, Michael Lawrence <lawrence.michael at gene.com>
wrote:

Taken in the abstract, the tidy data argument is one for consistent data
structures that enable interoperability, which is what we have with
SummarizedExperiment. The "long form" or "tidy" data frame is an effective
general representation, but if there is additional structure in your data,
why not represent it formally? Given the way R lays out the data in arrays,
it should be possible to add that fourth dimension, in an assay array,
while still using the colData to annotate that structure. It does not make
the data any less "tidy", but it does make it more structured.

On Tue, Mar 31, 2015 at 4:14 AM, Wolfgang Huber <whuber at embl.de> wrote:

Dear Jesper

this is maybe not the answer you want to hear, but stuffing in 4, 5, ?
dimensions may not be all that useful, as you can always roll out these
higher dimensions into the existing third (or even into the second, the
SummarizedExperiment columns). There is Hadley?s concept of ?tidy data?
(see e.g. http://www.jstatsoft.org/v59/i10 ) ? a paper that is really
worthwhile to read ? which implies that the tidy way forward is to stay
with 2 (or maybe 3) dimensions in SummarizedExperiment, and to record the
information that you?d otherwise stuff into the higher dimensions in the
colData covariates.

Wolfgang

Wolfgang Huber
Principal Investigator, EMBL Senior Scientist
Genome Biology Unit
European Molecular Biology Laboratory (EMBL)
Heidelberg, Germany

T +49-6221-3878823
wolfgang.huber at embl.de
http://www.huber.embl.de

On 30 Mar 2015, at 12:38, Jesper G?din <jesper.gadin at gmail.com>

wrote:

Hi!

The SummarizedExperiment class is an extremely powerful container for
biological data(thank you!), and all my thinking nowadays is just

circling

around how to stuff it as effectively as possible.

Have been using 3 dimension for a long time, which has been very
successful. Now I also have a case for using 4 dimensions. Everything
seemed to work as expected until I tried to subset my object, see

example.

library(GenomicRanges)

rowRanges <- GRanges(
               seqnames="chrx",
               ranges=IRanges(start=1:3,end=4:6),
               strand="*"
               )

coldata <- DataFrame(row.names=paste("s",1:3, sep=""))

assays <- SimpleList()

#two dim
assays[["dim2"]] <- array(0,dim=c(3,3))
se <- SummarizedExperiment(assays, rowRanges = rowRanges,

colData=coldata)

se[1]
#works

#three dim
assays[["dim3"]] <- array(0,dim=c(3,3,3))
se <- SummarizedExperiment(assays, rowRanges = rowRanges,

colData=coldata)

se[1]
#works

#four dim
assays[["dim4"]] <- array(0,dim=c(3,3,3,3))
se <- SummarizedExperiment(assays, rowRanges = rowRanges,

colData=coldata)

se[1]
#does not work
#Error in x[i, , , drop = FALSE] : incorrect number of dimensions

This is also the case for rbind and cbind. Would it be appropriate to

ask

you to update the SE functions to handle subset, rbind, cbind also

for 4

dimensions? I know the time for next release is very soon, so maybe

it is

better to wait until after April 16. Just let me know your thoughts

about

it.

Jesper

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