hi Martin,
along the lines of the situation that Kasper describes, my use case is
that GSVA transforms a matrix of expression values of genes-by-samples
into a matrix of summary expression values of pathways-by-samples. When
the input is an 'ExpressionSet' object, GSVA simply does something like
this (sketched):
pwysbysamplesmatrix <- gsva(exprs(inputGeneExpressionSet), geneSets)
outputPwyEset <- inputGeneExpressionEset
exprs(outputPwyEset) <- pwysbysamplesmatrix
annotation(outputPwyEset) <- ""
so the first dimension of the ExpressionSet object is reduced from tens
of thousands of genes to hundreds or thousands of pathways (=gene sets).
i thought this was a valid way to do it, let me know what should be the
correct route.
robert.
On 01/10/2014 03:59 PM, Kasper Daniel Hansen wrote:
Unfortunately, you may want to be careful about making it too robust
(depending on what you mean). For example filtering methods could very
well be seen as replacing one matrix with a smaller one. Not sure if
these methods use exprs<-, but that is probably how I would do it.
What would be better is if in the process of doing the replacement, the
other slots are harmonized in a relevant way. That would require row
names on the replacement matrix.
Kasper
On Fri, Jan 10, 2014 at 9:52 AM, Martin Morgan <mtmor...@fhcrc.org
<mailto:mtmor...@fhcrc.org>> wrote:
On 01/10/2014 12:07 AM, Maintainer wrote:
dear maintainers of Biobase and lumi,
this is a question raised from a different thread a few days ago
here:
https://stat.ethz.ch/__pipermail/bioc-devel/2014-__January/005129.html
<https://stat.ethz.ch/pipermail/bioc-devel/2014-January/005129.html>
the matrix of genes by samples expression values in an
'ExpressionSet'
object can be accessed and replaced with the "exprs<-" method
and this
changes automatically the dimension of the 'ExpressionSet' object as
illustrated in the following example:
library(Biobase)
data(sample.ExpressionSet)
dim(sample.ExpressionSet)
Features Samples
500 26
exprs(sample.ExpressionSet) <- matrix(rnorm(2600), nrow=100)
dim(sample.ExpressionSet)
Features Samples
100 26
however, this does not seem to be happening with 'LumiBatch' objects
which are an extension of 'ExpressionSet' objects:
library(lumi)
data(example.lumi)
dim(example.lumi)
Features Samples
8000 4
exprs(example.lumi) <- matrix(rnorm(400), nrow=100)
dim(example.lumi) ## NOTE NOW THAT DIMENSION DOES NOT CHANGE!!
Features Samples
8000 4
actually, the expression values have been replaced
> dim(exprs(example.lumi))
[1] 100 4
but the object is no longer valid
> validObject(example.lumi)
Error in validObject(example.lumi) :
invalid class "LumiBatch" object: row numbers differ for
assayData members
This is also true of the ExpressionSet example (the object is no
longer valid), it's just that the dims are taken from a different
location so reflect the addition.
The user should be providing expression values with the same
dimensions (and with the same dimnames) as the original. I think the
intended use case was more along the lines of replacing the
expression matrix with say a transformed version, exprs(x) =
log(exprs(x)).
Obviously Biobase shouldn't be allowing the user to see an invalid
object, but the design of the package does not make it easy to
enforce this; I'll work on providing something more robust, but the
end result of trying the above assignment will be an error.
Martin
a couple of diagnostics are that the expression matrix itself it
does
seem to have been replaced:
dim(exprs(example.lumi))
[1] 100 4
and the "exprs<-" method does not seem to be explicitely defined for
'LumiBatch' objects:
showMethods("exprs")
Function: exprs (package Biobase)
object="AffyBatch"
object="ExpressionSet"
object="MethyLumiSet"
object="SnpSet"
so i guess something is not working at the interaction of
Biobase::exprs<- and LumiBatch-class, whether this should be
fixed at
Biobase or lumi, have no idea.
thanks!
robert.
ps: sessionInfo()
> sessionInfo()
R version 3.0.2 (2013-09-25)
Platform: x86_64-unknown-linux-gnu (64-bit)
locale:
[1] LC_CTYPE=en_US.UTF8 LC_NUMERIC=C
LC_TIME=en_US.UTF8
[4] LC_COLLATE=en_US.UTF8 LC_MONETARY=en_US.UTF8
LC_MESSAGES=en_US.UTF8
[7] LC_PAPER=en_US.UTF8 LC_NAME=C
LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF8
LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats graphics grDevices utils datasets
methods
base
other attached packages:
[1] lumi_2.14.1 Biobase_2.22.0 BiocGenerics_0.8.0
vimcom_0.9-92
[5] setwidth_1.0-3 colorout_1.0-1
loaded via a namespace (and not attached):
[1] affy_1.40.0 affyio_1.30.0
annotate_1.40.0
[4] AnnotationDbi_1.24.0 base64_1.1 beanplot_1.1
[7] BiocInstaller_1.12.0 biomaRt_2.18.0
Biostrings_2.30.1
[10] bitops_1.0-6 BSgenome_1.30.0 bumphunter_1.2.0
[13] codetools_0.2-8 colorspace_1.2-4 DBI_0.2-7
[16] digest_0.6.4 doRNG_1.5.5 foreach_1.4.1
[19] genefilter_1.44.0 GenomicFeatures_1.14.2
GenomicRanges_1.14.4
[22] grid_3.0.2 illuminaio_0.4.0 IRanges_1.20.6
[25] iterators_1.0.6 itertools_0.1-1
KernSmooth_2.23-10
[28] lattice_0.20-24 limma_3.18.7 locfit_1.5-9.1
[31] MASS_7.3-29 Matrix_1.1-1.1
matrixStats_0.8.14
[34] mclust_4.2 methylumi_2.8.0 mgcv_1.7-27
[37] minfi_1.8.9 multtest_2.18.0 nleqslv_2.1
[40] nlme_3.1-113 nor1mix_1.1-4 pkgmaker_0.17.4
[43] preprocessCore_1.24.0 RColorBrewer_1.0-5 RCurl_1.95-4.1
[46] registry_0.2 reshape_0.8.4 R.methodsS3_1.6.1
[49] rngtools_1.2.3 Rsamtools_1.14.2 RSQLite_0.11.4
[52] rtracklayer_1.22.0 siggenes_1.36.0 splines_3.0.2
[55] stats4_3.0.2 stringr_0.6.2 survival_2.37-4
[58] tools_3.0.2 XML_3.98-1.1 xtable_1.7-1
[61] XVector_0.2.0 zlibbioc_1.8.0
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