similar to: Questions of Significance Analysis of Microarrays(SAM){siggenes}

Hi BioC user, I have a problem extracting the gene set I would like to work with. Here is I work with my data: normData <- read.delim("normalizedData.txt",sep ="\t") ######### two class unpaired comparison # y must take values 1,2 classes <- c(-1,-2,1,2) #prepere the data for the samr analysis data.x <-as.matrix(normData[,8:11]) d=list(x=data.x,y=classes,

Dear all, I am running R 2.4.1. > library(siggenes); > library(multtest); > cl<-rep(c(0,1),c(3,3)); > sub<-exprs(AffyExpData[,c(1:3,7:9)]); > gn<-geneNames(AffyRAwData); > sam.out<-sam(sub,cl,rand=123,gene.names=gn); We're doing 20 complete permutations > sam.out SAM Analysis for the Two-Class Unpaired Case Assuming Unequal Variances Delta p0

Hi, I am using SAM (from siggenes_1.2.11 package) method to select genes from a microarray data set. After installing the latest R2.4.0 on my computer, to my surprise the results are totally different from that calculated using R2.1.1. Even the example code doesn't work the same way under these two versions of R. Does anybody know what is going on? Thanks for any suggestions.

You asked the same question on the Bioconductor mailing list back in August. At that time, you suggested yourself a solution for how the adjusted p-values should be interpreted. I answered your query and told you that your interpretation was correct. So I'm not sure what more can be said, except that you should read the article Wright (1992), which is cited in the help entry for p.adjust(),

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 list, I am trying to perform a significance analysis of a microarray experiment with survival data using the {samr} package. I have a matrix containing my data which has 17816 rows corresponding to genes, and 286 columns corresponding to samples. The name of this matrix is data.matrix2. Some of the first values of this matrix are: data.matrix2[1:3,1:5] GSM36777 GSM36778 GSM36779

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

I am getting the following error in my script. I am very very new to R and have obtained this script from another person. #read file in (dummy data) starburst.plot<-function(affy.fold, affy.FDR)(ifelse( ((affy.fold) >=0), -1*log10(affy.FDR), 1*log10(affy.FDR))) starburst.plot<-function(meth.fold, meth.FDR)(ifelse( ((meth.fold) >=0), -1*log10(meth.FDR), 1*log10(affy.FDR))) At my next

Dear R-people! I??m trying to write a C program that write to the standard input of R and read the standard output. I can perfectly read the R output, but I??m not able of writing anything to R. This program really works with the 'cat?? UNIX command, but it does not work with R. What I??m doing wrong??? It is possible to do it??? I want to start R once and use it thousands of times...

Dear R-people! I??m trying to write a C program that write to the standard input of R and read the standard output. I can perfectly read the R output, but I??m not able of writing anything to R. This program really works with the 'cat?? UNIX command, but it does not work with R. What I??m doing wrong??? It is possible to do it??? I want to start R once and use it thousands of times...

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 All, It is not a typical R question (though I use R for this) but I thought someone will help me. For the list of P values, I have calculated FDR using p.adjust() in R (bioconductor). But my FDR values are same for all the P values. When do we get same FDR values? Does the smallest P values should less than 1/N? (where N is the number of P values) Thanks in advance. Kind regards, Ezhil

We are currently using the t-test in Package stats, t.test(x, y = NULL, alternative = c("two.sided", "less", "greater"), mu = 0, paired = FALSE, var.equal = FALSE, conf.level = 0.95, ...) but have some troubles : 1. why does the t-test take so a long time to perform a single test on a row of a data.frame ? Is there any alternative function to perform

Hello, I've a question about the fdr method in p.adjust: What is the threshold of the FDR, and is it possible to change this threshold? As I understand the FDR (please correct) it adjusts the p-values so that for less than N% (say the cutoff is 25%) of the alternative hypothesis the Null is in fact true. thanks a lot for help, +regards, Arne

I have developed a problem with the postscript output of plot on Windows. My code still works properly with R 2.3 but, with R 2.4, the white text on red background does not show up. It does, however, show up when output is sent to the screen. Below is my code and sessionInfo. R version 2.4.0 Under development (unstable) (2006-08-29 r39012) i386-pc-mingw32 locale: LC_COLLATE=English_United

Hello all, I am doing SAM and the median of positive genes in the permutated sets is = false positives, and the "parent set" gives true positives. FDR = FP/TP * 100. My FDR comes to greater than 100. Is that possible? Please help! Thanks, -D. [[alternative HTML version deleted]]

The Partek package (www.partek.com) allows only two selections for Multiple Test Correction: Bonferroni and Dunn-Sidak. Can anyone suggest why Partek implemented Dunn-Sidak and not the other methods that R has? Is there any particular advantage to the Dunn-Sidak method? R knows about these methods (in R 2.1.1): > p.adjust.methods [1] "holm" "hochberg" "hommel"

Dear R users, I am wondering about the following results: > p.adjust(c(0.05,0.05,0.05),"fdr") [1] 0.05 0.05 0.05 > p.adjust(c(0.05,0.04,0.03),"fdr") [1] 0.05 0.05 0.05 Why does p.adjust(..., "fdr") not adjust p-values, if they are constant? Does somebody have an explanation or can point to a reference? Thanks in advance, Will

> Note that "stripe" is not tested much and practically unmaintained. Ah, this was what I suspected. Understood. I'll be happy with "shard". Having said that, "stripe" works fine with transport=tcp. The failure reproduces with just 2 RDMA servers (with InfiniBand), one of those acts also as a client. I looked into logs. I paste lengthy logs below with

On Wed, Aug 16, 2017 at 4:44 PM, Hatazaki, Takao <takao.hatazaki at hpe.com> wrote: >> Note that "stripe" is not tested much and practically unmaintained. > > Ah, this was what I suspected. Understood. I'll be happy with "shard". > > Having said that, "stripe" works fine with transport=tcp. The failure reproduces with just 2 RDMA servers