search for: expresso

Hello, I want to use expresso for preprocessing the hgu133a-spikein data from affycompII. But there is an error: > library(affy) > path <- "z:/Microarray/hgu133a-spikein/rawdata" > celFile <- list.celfiles(path=path,full.names=TRUE); > affyBatch <- ReadAffy(filenames=celFile[1:6]); > ese...

Hi All, I have microarray data that does not come in a CEL file. Currently it is in the form of columns = individual samples and rows = individual probes. There are about 79 columns and it is in a tab delimited text file. Is there a way to convert this file into an AffyBatch so that I can run expresso with it? Thanks, George

Dear R Users, In the expresso function, which combination of these methods for data pre-processing (when using affymetrix oligo arrays) is the best: bgcorrect.metod = rma rma2 mas normalize.method = qspline quantiles loess pmcorrect.method = pmonly subtractmm mas summary.method = liwong avgdiff medianpolish mas There are m...

Hi. I can''t seem to get debugging of specs working in Netbeans. AFAIK, I''m supposed to be able to just do Command+Shift+F5 with the spec open in the editor but i get this error: /Users/admin/src/expresso/vendor/plugins/rspec/lib/spec/test/unit/autorunner.rb:2:in `remove_method'': method `process_args'' not defined in Test::Unit::AutoRunner (NameError) from /Users/admin/src/expresso/vendor/plugins/rspec/lib/spec/test/unit/autorunner.rb:2 from /usr/local/lib/ruby/site_...

...a feature of namespaces. Needless to say, searching the archives for "print & method" retrieved a cornucopia of useless information. I wonder if anyone would be kind enough to inform me where to look for the proper incantation? Thanks. RedHat EL3 L:inux R-1.9.1 Last.Drink - Aurora Expresso Jim

...long with a free script so you can see how your machine stacks up. Looks like R is substantially faster than S-plus. My problem is this: with 512Mb and an overclocked AMD Athlon XP 1800+, running at 588 SPEC-FP 2000, it still takes me 30 minutes to analyze 4Mb .cel files x 120 files using affy (expresso). Running svm takes a mighty long time with more than 500 genes, 150 samples. Questions: 1) Would adding RAM or processing speed improve performance the most? 2) Is it possible to run R on a cluster without rewriting my high-level code? In other words, 3) What are we going to do when we start c...

Can anybody explain why RMA has to have a default normalization method: quantile-quantile? Why don't leave the choices to users? If I just want to use RMA to do a background correction without normalization, how should I specify the ? in the normalize.method="?" ? Hairong [[alternative HTML version deleted]]

Full_Name: Edward J. Oakeley Version: 1.7.1 OS: Windows XP Submission from: (NULL) (212.47.183.3) This bug occurs when using the (D)COM server to connect to the "expresso" command of the Bioconductor Affy package. It may be a bug of R, (D)COM or Affy ut I will report it here anyway as it feels like an R bug. The Affy package when invoked will read a series of large (10Mb) text files from the working directory. These files contain information about a microarray...

...all physical memory can be utilized? Our system is like this: System type: IBM AIX Symmetric Multiprocessing (SMP) OS version: SuSe 8 SP3a CPU: 8 Memory: 64GB The codes are as follows: > Data <- ReadAffy(filenames = paste(HOME, "CelData/", fname, sep="")) > eset <- expresso(Data, normalize.method="invariantset", bg.correct=FALSE, pmc\ orrect.method="pmonly", summary.method="liwong") normalization: invariantset PM/MM correction : pmonly expression values: liwong normalizing...Error: cannot allocate vector of size 594075 Kb > gc()...

...M286038.CEL six 6h3 GSM286039.CEL six library(affy) library(limma) library(vsn) pd <- read.AnnotatedDataFrame("er_for_affy.txt", header = TRUE, row.names = 2) pData(pd) #### load a <- ReadAffy(filenames = rownames(pData(pd)), phenoData = pd, verbose = TRUE) #### normalize x <- expresso(a, bg.correct = FALSE, normalize.method = "vsn", normalize.param = list(subsample = 1000), pmcorrect.method = "pmonly", summary.method = "medianpolish") ### genes with highest variation library(hgu133plus2.db) rsd <- apply(exprs(x), 1, sd) sel <- order(rsd, decre...

...as like this: > Data <- ReadAffy(filenames = paste(HOME, "CelData/", fname, sep="")) > mem.limits() nsize vsize NA NA > gc() used (Mb) gc trigger (Mb) Ncells 530216 14.2 899071 24.1 Vcells 76196137 581.4 243983468 1861.5 > eset <- expresso(Data, normalize.method="invariantset", bg.correct=FALSE, pmc\ orrect.method="pmonly", summary.method="liwong") normalization: invariantset PM/MM correction : pmonly expression values: liwong normalizing...Error: cannot allocate vector of size 594075 Kb > gc()...

Full_Name: Eric Libby Version: 2.1.1 OS: OS Tiger Submission from: (NULL) (65.93.158.117) I am trying to analyze microarray data of 42 human arrays. I typed in the following instructions: library(affy) Data <-ReadAffy() eset <- expresso(Data, normalize.method="invariantset", bg.correct=FALSE, pmcorrect.method="pmonly",summary.method="liwong") And I get some sort of memory error that crashes the R program. Specifically it states: R(1763) malloc: *** vm_allocate(size=346857472) failed (error code=3) R...

...up. > > > > Looks like R is substantially faster than S-plus. > > > > My problem is this: with 512Mb and an overclocked AMD > Athlon XP 1800+, > > running at 588 SPEC-FP 2000, it still takes FOREVER to > > analyze multiple > > .cel files using affy (expresso). Running svm takes a mighty > > long time > > with more than 500 genes, 150 samples. > > > > Questions: > > 1) Would adding RAM or processing speed improve performance > the most? > > 2) Is it possible to run R on a cluster without rewriting my > >...

....1 features: ===================== * All packages from the 1.0 release are included. All current bug fixes have been applied, and most have upgraded and provide enhanced functionality. * New packages and major upgrades for release 1.1: affy: read.MIAME, a widget for entering phenoData. expresso, a flexible function for computing expression measures as well as providing widgets to choose among options in the various steps of preprocessing. rma, a fast and efficient function to compute the multi-array average expression measure. * Biobase: vignette and openVignette, functions t...

....1 features: ===================== * All packages from the 1.0 release are included. All current bug fixes have been applied, and most have upgraded and provide enhanced functionality. * New packages and major upgrades for release 1.1: affy: read.MIAME, a widget for entering phenoData. expresso, a flexible function for computing expression measures as well as providing widgets to choose among options in the various steps of preprocessing. rma, a fast and efficient function to compute the multi-array average expression measure. * Biobase: vignette and openVignette, functions t...

...nd most have been upgraded and provide enhanced functionality. * affy: The affy package now provides for the automatic downloads of necessary CDF packages. There is also a new function, justRMA() which will use less system resources if one just wants RMA vs. using ReadAffy() and then rma/expresso - this will solve some users' problem when they do not have enough memory for working with the full method. The package's implementation of MAS 5.0 now agrees better with Affymetrix's then in previous releases, and now includes a function mas5() to make it easier for a user to...

Hi there what''s the status of the 0.13.4 release on Windows? I want to wrap up a project in the next 2 weeks and I''d wait for the new release if it''s coming in that timeframe. Otherwise I''d stay with the current windows version. thanks Jens-Christian Fischer