[Bioc-devel] SummarizedExperiments
On 09/06/2012 02:26 PM, Kasper Daniel Hansen wrote:
Alltogether, this looks great. I am still implementing/testing, but seems pretty nice. Here is a number of convenience methods I believe should be implemented. Some of them comes from eSet and some of them comes from my own work in bsseq, but I have used all of them extensively in various packages. sampleNames, featureNames, pData granges, start, end, width, strand, seqnames, seqlengths, seqlevels findOverlaps, subsetByOverlaps
GenomicRanges 1.9.65 in Bioc devel makes the following operations on
SummarizedExperiment work on the underlying rowData; I particularly like
subsetByOverslaps() for selecting, e.g., SummarizedExperiment rows that
overlap features of interest, and seqlevels(x, force=TRUE) =
paste("chr", 1:5), for instance, for selecting sequences of interest.
See the section 'GRanges compatibility (rowData access)' on
?SummarizedExperiment.
Martin
compare, countOverlaps, coverage, disjointBins, distance,
distanceToNearest, duplicated, end, end<-, findOverlaps, flank, follow,
granges, isDisjoint, match, mcols, mcols<-, narrow, nearest, order,
precede, ranges, ranges<-, rank, resize, restrict, seqinfo, seqinfo<-,
seqnames, shift, sort, start, start<-, strand, strand<-, width, width<-.
'granges' is a method already defined in GenomicRanges, I have found it very convenient. It is not clear to me why it is all lowercase What I have done in bsseq for the later two lines of methods, is to have a class 'hasGRanges' that designate any object with a GRanges. Then implement the granges() method and the rest goes from there. Not sure whether that makes sense in this context, but in general I have found it very convenient for my own work. Code for all of this is in bsseq/R/hasGRanges.R. I have two classes in the package inheriting from hasGRanges. Note that the classes in bsseq have not been updated to build on the new SummarizedExperiment (yet). Kasper On Wed, Sep 5, 2012 at 1:31 PM, Tim Triche, Jr. <tim.triche at gmail.com> wrote:
It seems to work with a Matrix in the assays:
R> packageVersion('GenomicRanges')
[1] ?1.9.61?
R> SE <- SummarizedExperiment(assays=wts, rowData=wts.locs)
Error in function (classes, fdef, mtable) :
unable to find an inherited method for function "SummarizedExperiment",
for signature "dgCMatrix"
R> SE <- SummarizedExperiment(assays=SimpleList(weights=wts),
rowData=wts.locs)
dimnames(.) <- NULL: translated to
dimnames(.) <- list(NULL,NULL) <==> unname(.)
R> show(SE)
class: SummarizedExperiment
dim: 35855 35855
exptData(0):
assays(1): weights
rownames(35855): feat1 feat2 ... feat35854 feat35855
rowData metadata column names(10): egid signed ... DHS_DGF chromHMM_state
colnames(35855): feat1 feat2 ... feat35854 feat35855
colData names(0):
R> class(assays(SE, 'weights'))
[1] "SimpleList"
attr(,"package")
[1] "IRanges"
R> class(assays(SE)$weights)
[1] "dgCMatrix"
attr(,"package")
[1] "Matrix"
Very cool! Albeit a tad disorienting. I was going to patch this up and
then realized, hey, if someone's going to stuff a Matrix into their assays,
they ought to give it a name and know what's going on under the hood, at
least for now. It seems to work correctly, too.
Oh oh, I spoke too soon. If I split by chromosome, the assay falls out:
R> head(assays(SE)$weights)
6 x 35855 sparse Matrix of class "dgCMatrix"
[[ suppressing 82 column names ?feat1?, ?feat2?, ?feat3? ... ]]
feat1 1 . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . .
feat2 . 1 . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . .
feat3 . . 1.0000000 0.5737488 . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . .
feat4 . . 0.5737488 1.0000000 . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . .
feat5 . . . . 1.00000000 0.04300373 . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . .
feat6 . . . . 0.04300373 1.00000000 . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . .
feat1 . . . . . . . . . . . . . . ......
feat2 . . . . . . . . . . . . . . ......
feat3 . . . . . . . . . . . . . . ......
feat4 . . . . . . . . . . . . . . ......
feat5 . . . . . . . . . . . . . . ......
feat6 . . . . . . . . . . . . . . ......
.....suppressing columns in show(); maybe adjust 'options(max.print= *)'
..............................
R> SE.by.chr <- split(SE, as.vector(seqnames(SE)))
R> head(assays(SE.by.chr)$weights)
Error in head(assays(SE.by.chr)$weights) :
error in evaluating the argument 'x' in selecting a method for function
'head': Error in function (classes, fdef, mtable) :
unable to find an inherited method for function "assays", for signature
"list"
Ideas? I would love to do something useful here instead of just fussing.
It's really a great structure.
I love the use of a reference class to reference the internal pieces of an
object, it's such a clever hack. Thanks much for adding it.
--t
On Tue, Sep 4, 2012 at 7:58 PM, Martin Morgan <mtmorgan at fhcrc.org> wrote:
On 09/04/2012 07:02 PM, Tim Triche, Jr. wrote:
This is really cool.
R> head(lsos(), 1)
Type Size Rows Columns
LAML.RNAseq SummarizedExperiment 38666528 23529 173
R> LAML.RNAseq.updated <- updateObject(LAML.RNAseq)
R> head(lsos(), 2)
Type Size Rows Columns
LAML.RNAseq SummarizedExperiment 38666528 23529 173
LAML.RNAseq.updated SummarizedExperiment 6101488 23529 173
I think either the sizes are misleading (missing the content of the
environment that the reference classes contain, probably
LAML.RNAseq.updated at assays$data has size 38666528 - 6101488) or the data has
not been updated correctly :\. Nonetheless manipulation of especially
non-assay but also assay data should be faster.
I added a brief section (1.9.60) to ?SummarizedExperiment describing what
was done. Also, I tried (w/out actually testing...) to add support for
Matrix instances.
As a miscellaneous 'tip', when working with large data I find it useful to
start R with --min-vsize=2048M --min-nsize=20M. These are documented in
RShowDoc("R-intro") as 'for expert use only' and influence how much memory R
allocates for vectors ('vsize'; vsize can be very large) and for
S-expressions ('nsize' 50000000) and help get up to large memory allocations
without innumerable garbage collections. Of course having big memory is of
primary importance.
Martin
R> str(LAML.RNAseq.updated at assays)
Reference class 'ShallowSimpleListAssays' [package "GenomicRanges"] with
1 fields
$ data:Formal class 'SimpleList' [package "IRanges"] with 4 slots
.. ..@ listData :List of 1
.. .. ..$ rpm: num [1:23529, 1:173] 0 0 8.12 10.84 27.73 ...
.. ..@ elementType : chr "ANY"
.. ..@ elementMetadata: NULL
.. ..@ metadata : list()
and 11 methods,R>
R> packageVersion('GenomicRanges')
[1] ?1.9.59?
A slightly bigger one:
R> head(lsos(),2)
Type Size Rows Columns
LAML SummarizedExperiment 1950606472 485577 192
LAML.updated SummarizedExperiment 458912760 485577 192
R> system.time(LAML.updated$foo <- rep('bar', 192))
user system elapsed
0.132 0.116 0.248
R> system.time(LAML$foo <- rep('bar', 192))
user system elapsed
2.728 2.144 7.519
That is a really clever hack you added. I think I will have to figure
out how it works :-)
Thanks!!!
On Sun, Sep 2, 2012 at 2:47 PM, Martin Morgan <mtmorgan at fhcrc.org
<mailto:mtmorgan at fhcrc.org>> wrote:
On 08/30/2012 08:47 AM, Vincent Carey wrote:
I am all in favor of the dialogue and the aim. I reproduced
your first
timing, and note
unix.time(ss2 at colData$tx <- 1:1000)
user system elapsed
5.963 4.520 10.483
unix.time(colData(ss2)$tx2 <- 1:1000)
user system elapsed
17.937 13.074 31.016
BTW did you really mean to put the meth/unmeth in exptData, or
should it be
in assays?
From an experimentation perspective, I would want to redo
these
computations above with an environment holding the assay data
and see how
it changes. I suppose one can infer from first principles that
ss4 at assays[[1]]$meth[1:4,1:4]
[,1] [,2] [,3] [,4]
[1,] 0.2 0.2 0.2 0.2
[2,] 0.2 0.2 0.2 0.2
[3,] 0.2 0.2 0.2 0.2
[4,] 0.2 0.2 0.2 0.2
ss4 at assays[[2]]$unmeth[1:4,1:__4]
[,1] [,2] [,3] [,4]
[1,] 0.2 0.2 0.2 0.2
[2,] 0.2 0.2 0.2 0.2
[3,] 0.2 0.2 0.2 0.2
[4,] 0.2 0.2 0.2 0.2
unix.time(colData(ss4)$tx2 <- 1:1000)
user system elapsed
0.010 0.002 0.012
where ss4 has a SimpleList of environments holding the assay
data.
ss4
class: SummarizedExperiment
dim: 450000 1000
exptData(0):
assays(2): '' ''
rownames: NULL
rowData values names(0):
colnames: NULL
colData names(4): sampleID treatment tx tx2
ss4 at assays[[1]]
<environment: 0x1f4e0ba0>
This can't be attempted without disabling validity checking, and
the stack
trace from the attempt is instructive.
I don't think it will be too difficult to establish a customized
SummarizedExperiment that permits this, using the
lockedEnvironment
approach of ExpressionSets. I also do not know the down sides
but it seems
you/we could get some mileage on the approach and see.
GenomicRanges v. 1.9.59 has some preliminary changes that address
the performance issue. Serialized (saved) objects 'x' need to be
updated with updateObject(x) (and be careful if these are your only
instances of some hard work). There are some loose ends to tidy up.
Martin
On Thu, Aug 30, 2012 at 10:53 AM, Kasper Daniel Hansen <
kasperdanielhansen at gmail.com
<mailto:kasperdanielhansen at gmail.com>> wrote:
My perspective (and the reason for me email) is pretty
simple.
First off, let me say that we will always be working on
trading off
memory and IO and development time and all the other stuff.
I mean,
clearly the environment stuff for ExpressionSets did not
help all
people, because we have xps, aroma.affymetrix and oligo all
using some
kind of file based backend.
I am not trying to solve something "big". Putting
methylation data
inside an environment is not suddenly going to make my
algorithms go
_significantly_ faster or the memory usage to go
_significantly_ down.
But it makes life faster when you explore data - I
believe.
Now for two realistic examples (one minfi, one bsseq) that I
encounter
all the time:
library(GenomicRanges)
nRows <- 450000
nCols <- 1000
SSet <- SummarizedExperiment(rowData = GRanges(seqnames =
rep("chr1",
nRows),
ranges = IRanges(1:nRows,
width = 1)),
colData = DataFrame(sampleID =
paste0("person", 1:nCols)),
exptData = SimpleList(meth =
matrix(0.2,
ncol = nCols, nrow = nRows),
unmeth = matrix(0.2, ncol =
nCols, nrow =
nRows)))
print(object.size(SSet), units = "auto") # 6.7GB
system.time({
colData(SSet)$treatment = rep(c("a", "b"), nCols / 2)
}) # around 26 sec
nRows <- 28000000
nCols <- 10
SSet <- SummarizedExperiment(rowData = GRanges(seqnames =
rep("chr1",
nRows),
ranges = IRanges(1:nRows,
width = 1)),
colData = DataFrame(sampleID =
paste0("person", 1:nCols)),
exptData = SimpleList(meth =
matrix(0.2,
ncol = nCols, nrow = nRows),
unmeth = matrix(0.2, ncol =
nCols, nrow =
nRows)))
print(object.size(SSet), units = "auto") # 4.4GB
system.time({
colData(SSet)$treatment = rep(c("a", "b"), nCols / 2)
}) # around 20 sec
This is of course not that slow, especially not when
compared to the
massive amount of computation I had to do in order to arrive
at the
SSet in the first place. But the difference is that I
staring at the
screen when I work interactively, but I am out partying when
I run my
computations (overnight). And I do stuff like the above
surprisingly
many times after I have generated the objects.
My perspective is that this was essentially "solved" with the
ExpressionSet class, and I feel we are regressing a bit. I
also
believe that it should be "easy" to re-use some of the
ideas/code from
ExpressionSet and that it is worthwhile to do so, for
something that
may very well be an extremely important core class.
SummarizedExperiment is such a nice class that I essentially
have a
parallel implementation in bsseq and Pete has one in genoset.
So essentially, I was interested in fixing a pretty small
problem, but
something that I am a bit irritated about. And I feel we
almost have
a ready to use solution.
The reason I ask about it here, and not just do it myself,
is twofold
(1) it is tricky to get the environment right, and I do not
have
enough experience on the environment part of ExpressionSet,
that I
feel comfortable doing it
(2) I really think it would be beneficial to the community,
and
experience tells us, that what happens in these core
infrastructure
packages are much more likely to be widely used.
While I think that it may be worthwhile for people to play
with
reference classes, I think that going down that route is
unexplored
and may or may not be worth it. There is something to be
said for
building on something we have experience with. A reference
class idea
can be developed in parallel, if people are willing to
commit the
time.
Kasper
On Thu, Aug 30, 2012 at 10:12 AM, Tim Triche, Jr.
<tim.triche at gmail.com <mailto:tim.triche at gmail.com>>
wrote:
I am interested in how best to benchmark such
operations (and another
thought that I recently had, "why can't I put a Doug
Bates style 'Matrix'
in the SimpleList of assay data)? Kasper is the person
who convinced me
that ff, rhdf5, etc.should be a last resort, since RAM
is cheap.
For large data, the TCGA BRCA samples are a hair under
800 (last I
looked)
450k arrays with matching RNAseq and DNAseq, and that's
not even a big
study by breast cancer standards (IIRC, Gordon Mills was
rounding up 5000
tumors) Both minfi and methylumi can work off of the raw
data in these
studies as a "practical" example, but when it really
gets interesting is
when 3-4 ragged assays exist for each patient.
Exon-wise (or splice-graph-wise) RNAseq data with splice
junction read
counts is another place to find big SE-centric data with
ambiguities.
One
of the reasons I keep coming back to the idea of
subsetting an SE by a
GRanges[List], e.g. SE.totalRNA[ UCSC.lincRNAs, ], is
its speed. In any
event, after my obligations for the day are dispatched,
I'll write up an
example of doing this off of TCGA data, perhaps from
colorectal (only
about
400 subjects but at least it is published already). You
are right, a
single use case is often worth 1000 specifications.
--t
On Thu, Aug 30, 2012 at 6:42 AM, Vincent Carey
<stvjc at channing.harvard.edu
<mailto:stvjc at channing.harvard.edu>>__wrote:
On Thu, Aug 30, 2012 at 9:16 AM, Sean Davis
<sdavis2 at mail.nih.gov <mailto:sdavis2 at mail.nih.gov>>
wrote:
On Thu, Aug 30, 2012 at 8:59 AM, Martin Morgan
<mtmorgan at fhcrc.org <mailto:mtmorgan at fhcrc.org>
wrote:
On 08/30/2012 04:42 AM, Vincent Carey wrote:
On Thu, Aug 30, 2012 at 6:27 AM, Tim
Triche, Jr. <
tim.triche at gmail.com <mailto:tim.triche at gmail.com>
wrote:
nb. one of the reasons for the
existence of the MergedDataSet
class in
regulatoR (to be submitted for
review shortly) is that, while SEs
are
absolutely fantastic for managing
data matrices that are stapled to
a
GRanges, what is less awesome is
having a relatively light-weight
DataFrame
for phenotypic data that requires
the entire memory footprint be
recreated
upon writing a new column into said
DataFrame.
If R5 classes didn't spook me a
little, I would already have done
something
We don't often use this R5 terminology
but I see Hadley has made an
accessible document referring to
reference classes in this way.
To me the challenge is more conceptual --
pass-by-reference and the
way
that two variables pointing to the instance
are updated at the same
time --
and I had been thinking of a
LockedEnvironment-style implementation
where
some operations were free ('copying') but
others weren't (subset,
subset
assign). But maybe there are some more
direct approaches...
My 2c: This is a situation where some
experimental data would be
helpful.
Perhaps Tim or Kasper can share a largish
(unpublished) dataset and a
typical workflow with the Seattle folks. Even
that level of detail
would
give some sense of the scope of the problem.
For clarification, I did not mean large data,
although that would be
welcome. I meant data on computational experiments
with different
approaches -- that is, performance statistics that
would indicate where
scalability fails, what steps have been taken to
recover it, and how
costly
those steps are in terms of complexity, reliability,
maintainability,
risk
at the user/application end.
Sean
Yes, for instance where in the interactive
use is time being spent? Is
it copying the assays, or validity, or
actually updating the row
data? Is
500000 x 800 an appropriate scale to be
thinking about?
The main avenues for a developer seem
to be a) use environments or
reference classes; there are some costs
and we should understand
them,
and
b) use an out-of-memory approach like
rhdf5 or ff. Again there will
be
some costs. It should be relatively
easy to experiment with these.
One
thing I just learned about is
setValidity2 and disableValidity
(defined
in
IRanges IIRC) ... these allow you to
construct certain variations on
SummarizedExperiment with less attention
to deeper infrastructure.
probably I can make better use of the
insights the IRanges guys have
had
in their careful development and application
of validity methods,
though I
feel a bit like these are 'attractive
hazards' that tempt us to do
unsafe
things and then pay the price later. This is
likely the first
direction
I'll explore.
Exploring a little I already see that there
are some pretty dumb
things
being done in assignment.
Martin
whereby the assays/libraries for a given
study subject are all
pointed
to
as SEs (i.e. RNAseq, BSseq,
expression/methylation arrays,
CNV/SNP
arrays,
WGS or exomic DNAseq) and the column
(phenotype) data can avoid
being
subject to these constraints. Truth
be told I *still* want to do
that
because, most of the time, updates
to the latter are independent of,
and
happen subsequently to, loading the
former.
Suggestions would be welcome,
because other than these minor
niggles,
the
SummarizedExperiment class is almost
perfect for many tasks.
On Wed, Aug 29, 2012 at 9:57 PM, Tim
Triche, Jr. <
tim.triche at gmail.com <mailto:tim.triche at gmail.com>
wrote:
assigning new colData columns, or
overwriting old ones, in a
sizable
(say
500000 row x 800 column) SE is
nauseatingly slow.
There has to be a better way --
I'm willing to write it if
someone
can
point out an obvious way to do it
On Wed, Aug 29, 2012 at 9:52 PM,
Kasper Daniel Hansen <
kasperdanielhansen at gmail.com
<mailto:kasperdanielhansen at gmail.com>>
wrote:
On Thu, Aug 30, 2012 at 12:44
AM, Martin Morgan <
mtmorgan at fhcrc.org <mailto:mtmorgan at fhcrc.org>>
wrote:
On 08/29/2012 06:46 PM,
Kasper Daniel Hansen
wrote:
There is a lot of
good stuff to say
about
SummarizedExperiments,
and
from a certain point
of view I have a
parallel
implementation in
bsseq
(and there is also one in
genoset).
However, I really
like having the
assayData inside an
environment.
This helps some on
memory and - equally
important - speed at
the
command line. I
certainly need to
very heavily
consider using
an
environment in bsseq.
After some
discussion with Tim
(Triche) we have
agreed that
something
like
SummarizedExperiments
is
the way to go at
least for the
methylation arrays.
We need to be able
to easily handle
1000s
of
samples.
What is the chance
that we can get the
option of having the
assayData
inside an
environment, perhaps
by
Making a class
that is an
environment and
inherits from
SimpleList.
Using a classUnion
between the existing class of
the assayData
and
an environment.
Third option
that is probably
better than the
proceeding
two,
but
which I cannot come
up with right now.
Probably something can /
will be done. I guess
the slowness
you're
talking
about is when rowData /
colData columns are
manipulated; any
kind of
subsetting would mean a
'deep' copy. Martin
Yes, for example
manipulating colData -
something that
conceptually
should be quick and easy.
Of course, this will not
affect any
real
computation on the assayData
matrices, but it will make
life at
the
command prompt more pleasant.
Kasper
This would - in my
opinion - be very
nice and worthwhile.
Kasper
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--
Computational Biology / Fred Hutchinson Cancer Research Center
1100 Fairview Ave. N.
PO Box 19024 Seattle, WA 98109
Location: Arnold Building M1 B861
Phone: (206) 667-2793 <tel:%28206%29%20667-2793>
--
/A model is a lie that helps you see the truth./
/
/
Howard Skipper
<http://cancerres.aacrjournals.org/content/31/9/1173.full.pdf>
-- Computational Biology / Fred Hutchinson Cancer Research Center 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 Location: Arnold Building M1 B861 Phone: (206) 667-2793
-- A model is a lie that helps you see the truth. Howard Skipper
Computational Biology / Fred Hutchinson Cancer Research Center 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 Location: Arnold Building M1 B861 Phone: (206) 667-2793