[Bioc-devel] Use of Matrix inside SummarizedExperiment

[Peter Hickey]

Thanks, Hervé!

On 26/01/2016, Hervé Pagès <hpages at fredhutch.org> wrote:
> Hi Pete,
>
> On 01/25/2016 12:32 PM, Peter Hickey wrote:
>> The Matrix virtual class in the Matrix package seems to mostly work as
>> an assays element in a SummarizedExperiment. This is especially useful
>> for data that can be efficiently represented as a sparse matrix, e.g.,
>> using the dgCMatrix class.
>>
>> My understanding is that this works because the (concrete subclasses
>> of) Matrix implement the necessary basic S4 methods to form a basic,
>> matrix-like API. However, there are a couple of edge cases that I'm
>> hoping it might be possible to smoothen out. Ideally, I'd love if this
>> could work for any class that implements a minimal matrix-like API
>> (I'm currently experimenting with such a class) and not just for the
>> Matrix virtual class and its concrete subclasses. From reading the
>> SummarizedExperiment code, it looks like the minimal methods required
>> for an element of a (concrete subclass of) Assays object would be dim,
>> dimnames, [, [<-, rbind, cbind, length. I suppose that if any
>> additional methods are added for the Assays virtual class (e.g., I
>> have an almost-complete combine,SummarizedExperiment-method that calls
>> a combine,Assays-method) then these matrix-like objects must also have
>> such methods defined to ensure relatively straightforward inheritance.
>>
>> Here are a couple of instances where a matrix and a Matrix behave
>> (understandably) differently but where it would be nice if it "just
>> worked". There may well be others, but I'd be interested to know
>> whether this is worth further pursuing.
>>
>> library(SummarizedExperiment)
>> library(Matrix)
>> m <- matrix(1:10, ncol = 2)
>> m2 <- Matrix(m)
>>
>> # SummarizedExperiment constructor has specialised matrix method.
>> se <- SummarizedExperiment(m)
>> # This won't work because there is no Matrix specialisation
>> se2 <- SummarizedExperiment(m2)
>> # But can get around this by wrapping the Matrix in a SimpleList to defer
>> to
>> # the SummarizedExperiment,SimpleList-method
>> se2 <- SummarizedExperiment(SimpleList(m2))
>
> Note that wrapping the Matrix in an ordinary list also works.
>
>> # I guess the only way around this is to write a SummarizedExperiment
>> method
>> # for every matrix-like class, which might be too much overhead for the
>> # SummarizedExperiment package to maintain. Perhaps there is another
>> solution,
>> # e.g., try wrapping the input in a call to SimpleList if no method found
>> and
>> # then deferring to the SimpleList method? Could be too messy to be worth
>> it ...
>
> The method for matrix already does this wrapping into a SimpleList
> object and then defers to the method for SimpleList method. I just
> replaced the current method for matrix by a method for ANY that does
> exactly the same thing. With this change, SummarizedExperiment() takes
> any matrix-like object.
>
>>
>> # assay<- dispatches on value (which must be a matrix)
>> assay(se) <- assay(se)
>> # Won't work because there is no Matrix specialisation
>> assay(se2) <- assay(se2)
>> # But using assays() does work
>> assays(se2)[[1]] <- assays(se2)[[1]]
>> # Could value be dropped from the assay<- signatuare and the object
>> validated
>> # during/following the consequent call to assays<-?
>
> That makes a lot of sense. Having the assay() setter dispatch on 'x',
> 'i', and 'value' has no real benefit. Dispatching on 'x' and 'i' is
> enough and allows the assay() setter to take any matrix-like object as
> long as the resulting SummarizedExperiment object is valid.
>
> These 2 changes are in SummarizedExperiment 1.1.17.
>
> Thanks for the suggestions,
> H.
>
>>
>> Cheers,
>> Pete
>>
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>>
>
> --
> Hervé Pagès
>
> Program in Computational Biology
> Division of Public Health Sciences
> Fred Hutchinson Cancer Research Center
> 1100 Fairview Ave. N, M1-B514
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>
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>