similar to: Affymetrix data analysis

Dear List, I'm an [R] novice starting analysis of an ecological dataset containing the basal areas of different tree species in a number of research plots. Example data follow: > Trees<-data.frame(SppID=as.factor(c(rep('QUEELL',2), rep('QUEALB',3), 'CORAME', 'ACENEG', 'TILAME')), BA=c(907.9, 1104.4, 113.0, 143.1, 452.3, 638.7, 791.7, 804.3),

Hi all LLVMdev! Here is a question: I try to understand how I can realize a debugging in LLVM!? I have written a back end for my target and now I need a debug. I asked developers Chris Lattner and Robert L. Bocchino. They recommend me ask the LLVMdev. Is there a tool like "llvm-db" about Robert said? Here a mail from Robert: On Jun 29, 2009, at 17:14 PM, Robert L. Bocchino

mogene10stprobeset.db is generated with AnnotationDbi for mouse gene array. I don't find a package that seems generated by AnnotationDbi for exon arrays on the webpage you mentioned. Is it correct? On Tue, Oct 27, 2009 at 7:00 PM, Marc Carlson <mcarlson at fhcrc.org> wrote: > Hi Peng, > > I am not completely clear from your post what you want. ?But most of our > annotation

I am looking for an online (easily accessible) form of the samples of genetic data for those with cancer and those without cancer. Can anyone direct me in the right place? Thanks, Jim [[alternative HTML version deleted]]

Hi everyone, My purpose is to read a .CEL file into R. The .CEL file was created from a .CAB by using DTT software found on Affymetrix website I read the .CEL file in R using ReadAffy as follows: > d2=ReadAffy(widget=T) and I complete the fields as required. It does not complain. For example I could find the description: > description(d2) Experimenter name: BB Laboratory: FFL Contact

I have CEL files from Affymetrix Mouse Array 430_2 and am trying to get the the individual PM intensities (11 per gene) for each sample. I would like to write out this into a tab delimited text file. Where am I stalling? This is what I've done: Change dir(to where CEL files are saved) Data <- ReadAffy() eset <- rma(Data) write.exprs(eset, file="mydata.txt") With this I am

I don't know if this is the correct forum to ask the following question; however, when I search the aroma.affymetrix discussion group, it suggested that I should posted the question to r-help. Here it goes. I followed the instructions on aroma.affymetrix trying to install the packages; following are the steps: > install.packages(c("R.oo", "R.utils"),

Hi List, I'm trying to install aroma.affymetrix, which depend on many packages, affyio is one of them. The task is done on 32-bit Linux without problem. The same task was done on Solaris 9 with R-2.5.1 without problem. Following is the error message when installation affyio. I did try to put a file stdint.h I got from the web in the path, but got more error messages. Jun root@biostatsun1

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 many options within each method. I would appreciate a hint on the best

Hi, My R version is old, and I would like to update it. How can I update R and keep all the libraries? Thanks Best, Yunfei Li -------------------------------------------------------------------------------------- Research Assistant Department of Statistics & School of Molecular Biosciences Biotechnology Life Sciences Building 427 Washington State University Pullman, WA 99164-7520 Phone:

All, I am working with NIR spectral data and it was great to find that the example in ?plsr also used spectral data. Unfortunately, I am having difficulty figuring out how the "yarn" dataset is structured to allow for the plsr model to read: library(pls) data(yard) yarn.oscorespls <- mvr(density ~ NIR, 6, data = yarn, validation = "CV", method = "oscorespls")

We have what may be a very, very bad situation here and I'm hoping someone may be able to point out either where I'm misinterpreting this or where I missed the memo. We're testing 3.0.23d so we can upgrade from 3.0.14a. Our servers are all currently Solaris 9, and we build samba from source with MIT krb5 and openldap libraries. We have used security = ads since 3.0 after having

I am attempting to create a formula using as.formula for a PLS analysis. I have used the code below successfully, but in a previous R version and with many fewer predictors. Any help getting all of these predictors into one formula would be greatly appreciated. TC.fmla <- as.formula(paste("TC ~ ", paste(vars, collapse= "+"))) As I mentioned, this code worked fine in a

Dear R-Help, I am using the function "topTable" from the LIMMA package. To estimate adjusted P-values there are several options (adjust="fdr" , adjust="BH") as shown below: topTable(fit, number = 10, adjust = "BH", fit$Name) I guess any of these options (fdr, BH, etc.) is using a default of FDR=0.05 which is quite conservative (i.e., very

1. You have levels(A) as "2" and "4", yet you showed equations for A=0 and A=1? 2. y = A + X + A*X means you're allowing the different groups of A to have different slopes. Probably not what you intended. 3. It's probably best to provide a small sample of the data (and R code) so we know how you got what you got. Andy From: Ben Ridenhour > > Hello, >

I am attempting to run principal components analysis on a dataset of spectral reflectance (6 decimal places). I imported the data using read.table and there are both column and row headers. When I run princomp I receive the following error: Error in cov.wt(z) : 'x' must contain finite values only Where am I going wrong? Ross

All, I have been using a data set to build pls models for three different soil properties. Two of the three models run fine; however I receive the following error for the final model. > libs.IC.cal <- mvr(libs.IC.fmla, data = libsdata.cond.cal, ncomp=20,validation = "LOO", method = "oscorespls") Error in colMeans(x, n, prod(dn), na.rm) : 'x' must

Hi, I am studying using SVM functions of e1071 package to do prediction, and I found during the training data are "factor" type, then svm.predict() can predict data directly by categories; but if response variables are "numerical", the predicted value from svm will be continuous quantitative numbers, then how can I connect these quantitative numbers to categories? (for

Hello, I am trying to compile the R package on a sun. I get the error message rbitmap.c: In function 'my_png_error': rbitmap.c:73: structure has no member named 'jmpbuf' rbitmap.c: In function 'R_SaveAsPng': rbitmap.c:122: structure has no member named 'jmpbuf' make[4]:*** [rbitmap.lo] Error 1 Has anyone encountered this problem? Any solutions? Is there are

Dear All, How to cite the PDF user's guide for the LIMMA package? This is not about how to cite the LIMMA package. Roger Roger L. Vallejo, Ph.D. Computational Biologist & Geneticist U.S. Department of Agriculture, ARS National Center for Cool & Cold Water Aquaculture 11861 Leetown Road Kearneysville, WV 25430 Voice: (304) 724-8340 Ext. 2141 Email: roger.vallejo@ars.usda.gov