similar to: Fwd: problems with limma

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-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 am relatively new to R and Bioconductor and am trying to filter the topTable that I generated of differentially expressed genes from my normlized eset file comprised of ~ 40 HG-133A Affy microarrays . I would like to see if particular probesets are represented in this list. Alternatively I would like to generate a topTable of differentially expressed genes using only specified probesets

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. 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

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,

Hello, I have just completed an analysis of microarray data using the limma package. The analysis appears to have worked fine. However, when I use the topTable function to get the significant genes, I get the following error: > topTable(fit2,coef=5,adjust="fdr") Error in array(x, c(length(x), 1), if (!is.null(names(x))) list(names(x), : attempt to set an attribute on NULL

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

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

>>>>> "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

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

This works fine: fit42<-nls(Vfs~SSlogis(Months,Asym.Int+Asym.Group*Groupdum,xmid,scal), data=df, start=c(Asym.Int=22,Asym.Group=5,xmid=2,scal=6), na.action=na.omit) But this, identical except using gnls, doesn't converge: fit43<-gnls(Vfs~SSlogis(Months,Asym.Int+Asym.Group*Groupdum,xmid,scal), data=df, start=c(Asym.Int=22,Asym.Group=5,xmid=2,scal=6), na.action=na.omit) Error in gnls(Vfs

Hello - When I export my files using the write.table command and name the file.xls and use the sep="/t" to try and separate the columns in excel, everything just stays in one column. how do i get them all into separate columns (it did this before but i can't figure out what's different between this time exporting and other times when i enter the same thing) example: >

Hi Everyone, I have been trying to read some Arrayvision files( 2 channel cDNA) and am having some problem. My code is : setwd('C:/work/data/limma/ndd1'); files <- c('ndd1_1.txt','ndd1_2.txt','ndd1_3.txt'); RG=read.maimages(files,"arrayvision",sep="\t"); #Normalisation MA=normalizeWithinArrays(RG); #plotPrintTipLoess(MA); #Fit Linear

Hallo, how can I store a variable as a tab-delimited txt-file? I crated a variable with the following commands: > 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

Hi all, My name is Amy, I am a masters student in Bioinformatics at North Carolina State University. I am working on a project and I am trying to use the lumi R package for microarray data analysis. I have shown the sample code here and have questions about modifying the sample code for my own data. lumi package in R, example.lumi, the sample data has 8000 features and 4 samples I have

Dear all, I need some help on matrix design and B statistics by using limma package. I want to compare gene expression in 2 groups of cDNA samples. The experiment compares 4 treated mice(#1,#2,#3,#4) and 4 control mice (#5,#6,#7,#8). The target file is FileName Cy3 Cy5 mice1.spot Control_#5 Treat_#1 mice2.spot Treat_#1 Control_#5 mice3.spot Control_#6 Treat_#2

Hello, I am trying to run a code (see reference section) and when I run the line: fit<-lmFit(xen1dataeset,design), I get the error message: Error in lm.fit(design, t(M)) : incompatible dimensions I will really appreciate if someone can help me understand this error message and possibly help me debug the problem. Thanks manisha Reference section xen1data<-ReadAffy()

Hi all, My name is Amy, I am a masters student in Bioinformatics at North Carolina State University. I am working on a project and I am trying to use the lumi R package for microarray data analysis. I have shown the sample code here and have questions about modifying the sample code for my own data. lumi package in R, example.lumi, the sample data has 8000 features and 4 samples I have