search for: probesets

...r invoking R I have set --max-mem-size=1024M, so that I get > memory.limit() [1] 1073741824 Below is an example of what keeps happening as I am working. Any suggestions as to how I can stop running out of mermory? > memory.size() [1] 502904736 > log2.harvAD <- log2.harvAD[log2.harvAD$Probesets %in% harvard.genes$probeset,] Error: cannot allocate vector of size 49 Kb > log2.harvAD <- log2.harvAD[,c(1,1+order(names(log2.harvAD)[-1]))] Error: cannot allocate vector of size 49 Kb > log2.harvAD.annot <- unlist(lapply(strsplit(names(log2.harvAD),split=":"), + function(L...

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 within my eset file. Can anyone tell me how I would begin to approach this? I have looked into using the genefilter() function but can't figure o...

...and breaking up the data by tabs, gives me the expected result. I do not understand why this is happening and I can't find anything obvious in the data to explain the bahaviour... Does anybody have an explanation? something to watch out for? If I run this I get the incomplete set: > oldprobesets<-read.table("All_norm_calls.txt",sep="\t",header=T,stringsAsFactors=F) > dim(oldprobesets) [1] 15733 11 but I get the right data if I use: > probesets<-readLines("All_norm_calls.txt") > tmp<-matrix(ncol=11,nrow=24000) > for (i in 1:24000) tmp[...

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 qqline(data.sub,q) ##Show the line going through the plot t.test(data.sub,mu=0) #...

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 26, 2009, at 2:21 PM, Peng Yu wrote: > >> I use the f...

Suspect that this is easier than I realize, but taking some figuring out currently. Any help would be appreciated. I have a data frame (testhm) with many rows such as: ProbeSet.ID.F ProbeSet.ID Feature.ID G.S X0030V120810.14 X0143V120110.14 X0258V111710.14 X0283V111710.14 X0430V120710.14 X0472V111610.14 X0520V111610.14 X0546V113010.14 X0578V111810.14 X0624V111810.14 2 7892501_943979

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 different probesets with differential transcriptional outcomes. I did this based on the affy a...

Hi Everyone, Thanks for all the help with the previous queries. Here is what i want to do. i have 20000 probesets-->calculate all the variance accross all the probesets-->filter out probesets that are low so now i ended up with only 10000. The 10000 is fine but when i export to excel, it is missing the probeID. Here are my code and examples. #########calculate the variance across the probesets and plot...

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...

...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 Mouse 430 2.0 arrays, so I've got about 45,000 probesets (rows; with 1st column containing identifiers) and 30 biological replicates (columns; with the top row containing the header information). I've looked through several Intro manuals and the R help files. I know that "cor(x,y, use ="everything", method = c("pearson")) &...

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 column. ProbeID "Variance" 1 "224588_at" 21.5825745738848 How do i separat...

Hi Everyone, I need some simple help. Here are my codes ##########will give me 10000 probesets#################### data.sub = data.matrix[order(variableprobe,decreasing=TRUE),][1:10000,] dim(data.sub) data_output<-write.table(data.sub, file = "c://data_output.csv", sep = ",", col.names = NA) When i export to excel, it shows me this. This is just a short version. Ther...

...or for: 0403_YH40_H_a1MCF10A_r3.CEL 4.02474777803345 Getting probe level data... Computing p-values Doing PMA Calls TEST 1 : time Elapsed = 0 1 20 Background correcting Retrieving data from AffyBatch...done. Computing expression calls... .........Error in FUN(X[[9]], ...) : Expecting 22283 unique probesets, found 22284 Can anyone advise me on how to fix this problem? I was able to run the same data set with gcc-compiled R2.1.1 and BioConductor 1.6 successfully. Here is the code that I ran, if it helps to diagnose the problems. library(simpleaffy); library(affy); ampli.data <- ReadAffy() #...

Hi I'm running the latest R on a presumably up to date Linux server. 'Doing something silly I'm sure, but can't see why my saved PLMset objects come out all wrong. To use an example: Setting up an example PLMset (I have the same problem no matter what example I use) > library(affyPLM) > data(Dilution) # affybatch object > Dilution = updateObject(Dilution)

I have a data matrix with genes as columns and samples as rows. I want to create all possible gene ratios.Is there an elegant and fast way to do it in R and write it to a dataframe? Thanks for any help. Som. -- View this message in context: http://r.789695.n4.nabble.com/Calculating-all-possible-ratios-tp4627405.html Sent from the R help mailing list archive at Nabble.com. [[alternative HTML

dear members, i would like to write a variable in a plot title (main="") but i don't know the right syntax:(...i tried a lot of different ways without success. here my example: y=30 z=33 for (i in 10:length(tissue)) { png(filename = tissues[i], width = 1024, height = 768, pointsize = 12, bg = "white") gene.graph("ENSG00000115252", rma.affy, gps=list(1:3,

Hello All, I need your help. I am analysing affymetrix data and have to select the probe-set that has median expression among all the probe-sets for same gene. This way I want to remove the redundancy by keeping the analysis to single gene entry level. I am fully aware that it is not a nice thing to do but I just have to do it. To do so, I came across 'findLargest' function of

***********reading in data********** data<-read.table("microarray.txt",header=T, sep="\t") head(data) dim(data) attach(data) ***********creating matrix and calculating variance across probesets******** x<-1:20000 y<-2:141 data.matrix<-data.matrix(data[,y]) variableprobe<-apply(data.matrix[x,],1,var) hist(variableprobe) **************filter out low variance************* data.sub = data.matrix[order(variableprobe,decreasing=TRUE),][1:10000,] dim(data.sub) [1] 10000 14...

Dear all, I have run into a problem when running some code implemented in the Bioconductor panp-package (applied to my own expression data), whereby gene expression values of known true negative probesets (x) are interpolated onto present/absent p-values (y) between 0 and 1 using the *approxfun - function*{stats}; when I have used R version 2.8, everything had worked fine, however, after updating to R 2.11.1., I got unexpected output (explained below). Please correct me here, but as far as I unders...

hi, I have a conversion table for colnames like this: Probe_ID HUMAN_LLID 1 AF106325_PROBE1 7052 2 NM_019386_PROBE1 7052 3 NM_012907_PROBE1 339 4 AW917796_PROBE1 84196 5 L27651_PROBE1 10864 The Probe_ID contains a list of colnames for another data.frame, say x1. I need to convert such colnames to another ID's system, HUMAN_LLID by using the table.