[Bioc-devel] eSet questions

Hi,

First, according to the manual pages for the "annotatedDataSet" class

small "s" for Dataset ... threw me for a minute

(in BioConductor 1.9) is a "virtual superset for 'exprSet' , 'eSet',
etc".  While this seems to be the case for the soon-to-be-deprecated
exprSet, it seems not to be the case for an eSet.  Is that
interpretation correct?

I am not sure annotatedDataset is going anywhere.  It probably should
be removed.

Now to the real question.  

Given this description, one might attempt a design something like

setClass("ArrayCube", representation=list(
	rawData = "AssayData",
	experimentData = "MIAME",
	featureData = "AnnotatedDataFrame",
	hybridizationData = "AnnotatedDataFrame",
	measurementData = "AnnotatedDataFrame"
))

This obviously looks a lot like an eSet.  The differences are

It seems to me that you don't want to adopt the "AssayData"-"phenoData"
relationship documented in the eSet man page.  So the above is
not like an eSet, and the conflict is mostly with the AssayData
structure.

[1] I am thinking about the rawData entry as a list of data frames (or
data matrices), with each one corresponding to a unique file on the hard
disk.  These would be easy to read into R in the use cases above, but
violate one of the validity constraints on the assayData object in the
current eSet. (Specifically, the contraint that the columns in any
matrix in the assayData object must correspond to rows of the phenoData
object.)

This constraint is quite important for all the applications of eSet
in use, so abandoning it suggests designing another class.

[2] The featureData slot would describe the rows in each of those data
matrices.  In order to accommodate the RPPA data, however, featureData
might refer to patient samples instead of the genes that it would refer
to in the eSet design.

I have not had time to think at length about the RPPA data structure.
It seems possible to use the eSet design to represent it, but there
is substantial reorganization of the data relative to its physical
origins.  There are costs and benefits to shoehorning the data into
an ExpressionSet-like structure and I don't know how to weigh
them at the moment.  The real question seems to me to be whether it
is valuable to request X[G, S] for any of these data structures, where
X is the basic container, G is a predicate identifying a gene selection
and S is a predicate identifying a sample selection.  If you want that
AND you want to inherit the infrastructure available for ExpressionSets
to get that, then it makes sense for you to try to extend what we have in Biobase
to cover what you are dealing with.  It seems to me that you might
want to combine AssayData and AnnotatedDataFrame components in a
structure that does not extend eSet to get what you want.

[3] Similarly, hybridizationData would replace the phenoData slot, and
it also could refer to samples or to genes/proteins depending on the
data type.  Also, the phenoData object has to describe the "list"
dimension of the rawData instead of the "column" dimension".
[4] The measurementData slot would describe the measurement columns from
the software.  For already known packages, it would then be easy to
convert an ArrayCube into, for example, an RGList by slicing along the
desired measurement columns.  For novel quantification packages, one
could make an interface that lets the user specify which measurements
have which interpretation, and then make an RGList after they have had a
chance to load the data easily and start exploring it.

In any event, before I head further down this road, I'd like to get some
feedback on whether it would be [a] feasible or [b] desirable either to
create such a thing or to change the design of an eSet into such a thing.

My reaction, based on very brief contemplation, is that you'll be designing
a structure that does not extend eSet but shares some components and some
functionalities.  If the ability to represent, e.g.,
RPPA and Expression arrays in a single container type becomes important
we'll consider how the eSet constraints need to evolve.  Thus far they
seem to be effective for the most common types of high-throughput data
encountered.

The folks who actually designed the key Biobase containers may well have
different views of this situation.  This is just my personal reaction.