Hi, slow response but answers below.
On Wed, Apr 2, 2014 at 8:20 AM, hr <hr...@nyu.edu> wrote:
> Dear Henrik,
>
> Is there a way to read in CEL.gz files rather than .CEL files?
Unfortunately not, only non-compressed CEL files are supported. This
is mainly because the Affymetrix Fusion SDK that we utilize via the
'affxparser' package does not support compressed files.
>
> Also, do you provide an example (couldn't find one on the aroma homepage) on
> how to go from a CDF file and a bunch of CEL files to a final expression
> matrix normalized with RMA? Essentially I am looking for the equivalent of:
>
>
> library(affy)
> library(annotate)
> library(hgu133plus2.db)
>
> files = c("A.CEL.gz", "B.CEL.gz", "C.CEL.gz")
> setwd("datadir")
>
> eset <- justRMA(filenames = files)
> mat <- exprs(eset)
>
>
> What is the most efficient way to do this with Aroma?
I've added justRMA() to aroma.affymetrix 2.12.2 so you can do:
cs <- AffymetrixCelSet$byName("AbcDataSet", chipType="HG-U133_Plus_2")
eset <- justRMA(cs)
mat <- exprs(eset)
which replicates affy::justRMA() very well. This is done keeping the
memory usage constant regardless of the number of arrays during the
preprocessing. At the end, before justRMA() returns a
Biobase::ExpressionSet it needs to load the *summarized* data in, so
then memory becomes an issue, but that hits you order of magnitudes
later compared to the probe-level data. For example, on
HG-U133_Plus_2 there is 54,675 units and 'eset' contains both signals
and standard errors (8+8 bytes), so one array requires ~0.84 MB of
RAM. Thus, 1,000 arrays occupies roughly 0.84 GB of RAM. To
efficiently being able to manipulate in Bioconductor, you should
double or triple the memory requirements.
You can install/update to the most recent version of aroma.affymetrix as:
source('http://aroma-project.org/R/install#aroma.affymetrix')
Hope this helps
Henrik
>
> Many thanks and best wishes!
>
> Helge
>
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