similar to: Memory allocation

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

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 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

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

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 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

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

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''m using ruby on rails on a Macbook Pro with sqlite3. I''m finding that memory for the ruby process stays about 20mb until I hit an error after which the mermory doesn''t seem to be released and keeps going up until I can''t use my machine. What could be causing this? is it using sqlite3? have I set up rails or ruby wrong? Tim -------------- next part

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,

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 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

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

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

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. There

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,

***********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

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

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

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.