[BioC] reduce soychip cel file size
djguo at vbi.vt.edu
Fri Nov 5 17:00:41 CET 2004
Thanks so much for your great suggestion. I used the following expresso
command and it worked! But i have one more question regarding the
normalize.method choice: should i use "quantiles" or "quantiles.robust"?
What's the difference between them? Following is my command:
> eset<-expresso(data, normalize.method="quantiles",
Also, I'd like to thank Robert Gentleman, Vince Carey, and Holger
Schwender for their kind help regarding this issue
Laurent Gautier wrote:
> Robert Gentleman wrote:
>> On Thu, Nov 04, 2004 at 02:08:09PM -0500, Dianjing Guo wrote:
>>> We constantly experienced problems with rma function with soybean
>>> chip. Since the possible reason being the chip is too huge, i wonder
>>> whether there's a way to reduce the cel file size by taking only
>>> part of the raw intensity info for normalization. Any one can
>>> comment /addvise on that?
>> That does not seem like a very good idea.
> That's right. This is _really_ not a good idea, unless you really know
> the guts of the 'affy' package (there is a rewrite of some of the
> package on its way that will make that this kind of tricks more easy,
> but we are not there yet).
>> I have not seen any
>> postings that suggest that size is the issue; have you made them?
> According to Dian-Jing's previous post, the segfault occurs when the
> summary values are computed. I do not think either that the size is an
> issue: the tough part for memory usage is usually the handling of
> probe level data.
> Robert is probably right: there is memory leak or an array
> out-of-bound problem. At first sight I think that the problem comes
> from somewhere in 'do_RMA' (file rma2.c), but it is hard to tell
> (comment on line 410 is a hint of an out-of-bound thing, but it refers
> to a value '200' that I cannot see anywhere).
> If Dian-Jing is not into all, the use of 'expresso' (see my previous
> mail) is segfault safe (currently at the cost of a bit of memory
> usage, but this will improve very soon).
>> None of this needs to be mysterious in any way.
>> You should 1) make sure you have an up to date R, and an up to date
>> version of the package. If you get errors, such as segmentation
>> faults then you can use R -d gdb provided you have compiled R
>> with the -g option (and if not then you
>> will need to recompile it). From there you can track down the source
>> of the bug and it can be fixed.
>> For other bugs (such as problems in R code) there are options such
>> as using debug etc.
>> It is generally much better to figure out what is wrong, and why
>> than to invent rather peculiar one-off solutions.
>>> Many thanks,
>>> Bioconductor mailing list
>>> Bioconductor at stat.math.ethz.ch
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