similar to: filtering probesets with Bioconductor?

Hi, I have a question concerning the analysis of some affymetrix chips. I downloaded some of the data from GEO GSE11324 (see below). In doing so I'm stuck after I identified the probesets with significant changes. I have problems in assigning probeset specific gene names as well as getting the genomic coordinates. Furthermore I have no clue how to deal with the fact, that most genes have

I try to send this message To Gordon Smyth at smyth at vehi,edu.au but it bounced back, so here it is to r-help I am trying to use limma, just downloaded it from CRAN. I use R 2.0.1 on Win XP see the following: > library(RODBC) > chan1 <- odbcConnectExcel("D:/Data/mgc/Chips/Chips4.xls") > dd <- sqlFetch(chan1,"Raw") # all data 12000 > # > nzw <-

Dear R users, I'am using marray and Limma packages to analyze genepix output. 1) how can I filter bad spots from my data (data contains 3 types of bad spots). my experiment contains 12 samples and the bad spot are not associated to the same probes 2) how can I remove control probes from my data ? I'm sorry, i'm new with R and I can't find answer in packages doc. best regards,

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

Hi, I have built R (current development version) and BioConductor 1.7 with portland group compiler on a AMD Opteron. When I ran qc assessment on Affymetrix latin square data set, I got the following output, Loading required package: affy Loading required package: Biobase Loading required package: tools Welcome to Bioconductor Vignettes contain introductory material. To view,

Hi, I have been using 'read.table' regularly to read tab-delimited text files with data. No problem, until now. Now I have a file that appeared to have read fine, and the data inside looks correct (structure etc), except I only had 15000+ rows out of the expected 24000. Using 'readLines' instead, and breaking up the data by tabs, gives me the expected result. I do not

Hallo, > fit12<-lmFit(qrg[,1:2]) > t12<-toptable(fit12,adjust="fdr",number=25,genelist=qrg$genes[,1]) > t12 ID logFC t P.Value adj.P.Val B 522 PLAU_OP -6.836144 -8.420414 5.589416e-05 0.01212520 2.054965 1555 CD44_WIZ -6.569622 -8.227938 6.510169e-05 0.01212520 1.944046 Can anyone tell me what the difference is between P.Value

I think that you misunderstood me. As far as I know, RMA does three things: background correction, quantile normalization, and summary from probes to probesets. I want the probe values after background correction and quantile normalization but before the summary. On Sat, Dec 26, 2009 at 12:07 PM, Benilton Carvalho <bcarvalh at jhsph.edu> wrote: > pm(data) > > b > > On Dec

Hi all. I've got a really dumb question for anyone. How do I write the output of a limma analysis (basically the topTable) to a text file? I want to output the topTable for the entire microarray (not really a topTable anymore I suppose..). Thanks for any advice! -Josh

Hi Guys, I am having a real hard time trying to figure out for microarry. Here is my code One-Sample t-Test dim(data.sub) [1] 10000 140 ##there are 10000 probesets and 140 columns hist(data.sub) ## Histogram. Identify if the probesets are normal distributed q<-rnorm(10000) ##generate 10000 random, normal distributed values qqplot(data.sub,q)) ##Show the plot of the probeset

Hello: I am trying to work with a couple of microarray data sets, using platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 8.1 year 2003 month 11 day 21 language R In the shortcut for invoking R I have set

Hi R guru coders I wrote a bit of code to add a new column onto a "topTable" dataframe. That is a list of genes processed using the limma package. I used a for loop but I kept feeling there was a better way using a more vector oriented approach. I looked at several commands such as "apply", "by" etc but could not find a good way to do it. I have this feeling there

On Wed, December 22, 2004 12:11 am, r.ghezzo at staff.mcgill.ca said: > ----- Forwarded message from r.ghezzo at staff.mcgill.ca ----- > Date: Mon, 20 Dec 2004 15:45:11 -0500 > From: r.ghezzo at staff.mcgill.ca > Reply-To: r.ghezzo at staff.mcgill.ca > Subject: [R] problems with limma > To: r-help at stat.math.ethz.ch > > I try to send this message To Gordon

I have the following code, from which I get the following error message: Error in dim(data) <- dim : attempt to set an attribute on NULL I think the error is coming from the part of my code in BOLD RED. The script works fine until then. #Load libraries source("http://bioconductor.org/biocLite.R") biocLite() library(limma) library(Biobase) #change directory to folder where

Hi all, What are current methods people use in R to identify mis-spelled column names when selecting columns from a data frame? Alice Johnson recently tackled this issue (see [BioC] posting below). Due to a mis-spelled column name ("FileName" instead of "Filename") which produced no warning, Alice spent a fair amount of time tracking down this bug. With my fumbling fingers

Dear all, I would like to know how to subset a data.frame by rows. Example: Probesets 34884 34888 34892 1 100009676_at A A A 2 10001_at P P P 3 10002_at A A A 4 10003_at A A

>>>>> "GS" == Gordon K Smyth <smyth@wehi.edu.au> >>>>> on Sat, 8 Jan 2005 01:11:30 +1100 (EST) writes: <.............> GS> p.adjust() unfortunately gives incorrect results when GS> 'p' includes NAs. The results from topTable are GS> correct. topTable() takes care to remove NAs before GS> passing

Dear All, I am using the LIMMA package to create 2 contrasts for my data and then calculating the vennCounts of the decideTests from the contrast.fit to be able to create venn Diagrams. The code works fine but the summary(results) shows zeros for all i.e. no gene were up regulated or downregulated. This is not true for my data since toptable output shows Log fold change greater than > 2. I am

Here is my R Code x<-1:20000 y<-2:141 data.matrix<-data.matrix(data[,y])#create data.matrix variableprobe<-apply(data.matrix[x,],1,var) variableprobe #output variance across probesets hist(variableprobe) #displaying histogram of variableprobe write.table(cbind(data[1], Variance=apply(data[,y],1,var)),file='c://variance.csv') #export as a .csv file. Output in Excel all in 1

I am in need of someone's help in correlating gene expression. I'm somewhat new to R, and can't seem to find anyone local to help me with what I think is a simple problem. I need to obtain pearson and spearman correlation coefficients, and corresponding p-values for all of the genes in my dataset that correlate to one specific gene of interest. I'm working with mouse Affymetrix