similar to: Random divisions

Hi, I am a Ph.D. student from Québec, Canada. I’m a beginner with R and Bioconductor. Until now the only experience I have is in analyzing microarray data using affy and limma packages. Now I am trying to analyze Rat Gene 10 st arrays and I would like to run RMA analysis and Smyth moderated t test on those arrays. Since no cdf official package is available for those arrays, after reading many

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,

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

Hello, I'm currently using MCRestimate package and I have a question regarding the MCRestimate function. Here is my code: NestedCV.rf<-MCRestimate(eset, "Class", classificatin.fun="RF.wrap", variableSel.fun="varSel.highest.var", poss.parameters= list(var.numbers=c(100), mtry=c(10,50), cross.outer=10,cross.inner=10,cross.repeat=3) I'm pretty sure that I

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

Hallo, I just installed all needed packages for my project on my PC. But I cannot load all at one time. I now want to load limma. How can I realize the following plan: I want to install for example limma inclusive all needed other sub packages (add-on). Can anyone tell me the corresponding command? Thanks, Corinna Here is the result of the command library(): Pakete in Library

Hey, I have code that can check the quality of a data set we're working with (expression data), and I'm having some trouble writing code that would make the expression data we have tie to other data we want to link it to (called phenotype data). Does anyone have any advice on how I could make a single object that would do this? Other relevant info: I want to use the pdata() function,

Hello, After running R CMD check on my package I received the following error on package dependencies: * using log directory 'C:/z-zBackup/Nuvera Bio on Iatros01/Development/RPackages/nvNormalize/nvNormalize.Rcheck' * using R version 2.9.1 (2009-06-26) * using session charset: ISO8859-1 * checking for file 'nvNormalize/DESCRIPTION' ... OK * checking extension type ... Package *

For any given pre-specified gene or short list of genes, yes the Cox model works fine. Two important caveats: 1. Remeber the rule of thumb for a Cox model of 20 events per variable (not n=20). Many microarray studies will have very marginal sample size. 2. If you are looking at many genes then a completely different strategy is required. There is a large and growing literature; I like Newton

Hello, I am using the following code to plot an MVA plot. library(affy) library(Biobase) library(limma) library(gcrma) pd<-read.phenoData("Clk.targets.2.txt",header=TRUE, row.names=1,as.is=TRUE,sep="\t") Data <- ReadAffy(filenames=pData(pd)$FileName,phenoData=pd) Print(Data) eset <- gcrma(Data) write.exprs(eset,

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

At first I would like to express the gratitude toward Oosting, J. and James MacDonal for their help solving the previous problem I posted on this forum. Now I have a new problem. I was trying to normalize set of microarray data on HGU133 paltform. I put all the HGU133A platform chips in one directory and all the other HGU133B chips on another directory. When I tried to normalized them using same

Thanks, Martin. Based on my previous post, I thought of a more general formulation of my question that I think would be helpful to ask here. What's the best way to build an R object that links multiple datapoints about different people? I mean, I happen to have datasets that have individual gene expression data tied to individual patient characteristics (how long they survived, age, gender,

Hi, I was working on a classification problem using the pamr package. I used the pamr.adaptthresh() function to find the optimal accuracy of the classifier. I must not be doing it right, since it doesn't return the threshold values for optimum classification. For example,if I run it on a dataset, I get the following result using pamr.adaptthresh(): predicted true

Hi, I was working on a classification problem using the pamr package. I used the pamr.adaptthresh() function to find the optimal accuracy of the classifier. I must not be doing it right, since it doesn't return the threshold values for optimum classification. For example,if I run it on a dataset, I get the following result using pamr.adaptthresh(): predicted true (1)

Hi, Sorry about the earlier formatting errors... I was working on a classification problem using the pamr package. I used the pamr.adaptthresh() function to find the optimal accuracy of the classifier. I must not be doing it right, since it doesn't return the threshold values for optimum classification. For example,if I run it on a dataset, I get the following result using

Hi: I have clustered microarray gene expression data and trying to map between microarray probe, gene, pathway, gene ontology, and homology for a set of (affy) microarray probes. Is there any package in R which facilitates this? I am looking at bioconductor, but till now could not find a solution. A link to some worked example would be appreciated. Thanks and regards. John [[alternative HTML

Hi, I must be missing something here...Essentially, a short piece of code works if it's standalone, but doesn't work if it's divided into two functions. The code that works is: ################### WORKS ############### library(pamr) set.seed(120) x <- matrix(rnorm(1000*20),ncol=20) y <- sample(c(1:4),size=20,replace=TRUE) mydata <- list(x=x,y=y)

Hi, I have tried some time trying to figure out how to use pamr to plot multiclass Estimated probabilities for the training data and test data? Specifically, how to recreate the PAMR publication on PNAS with Tibshrani et al. The publication is as attached. The plot I want to do is Figure 5. I have downloaded the pamr package and the function which gives similar plot is pamr.plotcvprob

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