similar to: Export csv data

Hello, Thanks everyone for helping me with the previous queries. step 1: Here is the orginal data: short sample ProbeID Sample_1_D Sample_1_C Sample_2_D Sample_2_C 1 224588_at 2.425509867 11.34031409 11.46868531 11.75741478 step 2: i calculate the variance of the sample using this R code x<-1:20000 y<-2:141 data.matrix<-data.matrix(data[,y])#create data.matrix

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

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

***********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, 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 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

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 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

Hello: I am trying to work with a couple of microarray data sets, using platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 8.1 year 2003 month 11 day 21 language R In the shortcut for invoking R I have set

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

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,

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

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

[Moderator's Note: This message needed manual interaction by me, since the attachment originally was declared as ``application/octet-stream'' even though it was only plain text. We do not allow octet-stream (aka binary!) attachments on our mailing list -- for virus/spam filtering reasons. -- MM] We have also encountered the problem Douglas

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 am using "*Maanova* package" to do anova. I have created *datafile* with probeID as the first column, which is a tab limited text file and also created *designfile*. I have created *readma object* which is named as abf1. >From that readma object, i have to create data object by using *createData*function and also i hav to create model object by using *makemodel* function,

Hi, I am interested in using the cast function in R to perform some aggregation. I did once manage to get it working, but have now forgotten how I did this. So here is my dilemma. I have several thousands of probes (about 180,000) corresponding to each gene; what I'd like to do is obtain is a frequency count of the various occurrences of each probes for each gene. The data would look

I am trying to use lme to fit a mixed effects model to get the same results as when using the following SAS code: proc mixed; class refseqid probeid probeno end; model expression=end logpgc / ddfm=satterth; random probeno probeid / subject=refseqid type=cs; lsmeans end / diff cl; run; There are 3 genes (refseqid) which is the large grouping factor, with 2 probeids nested within each refseqid,

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

I have a file with a data in columnar format like below: probeID rc_AI104113_at rc_AI178259_f_at rc_AI179134_i_at rc_AI179134_f_at rc_AI104113_at rc_AA819429_f_at How can I rewrite it in the format below: 'rc_AI104113_at', 'rc_AI178259_f_at', 'rc_AI179134_i_at', 'rc_AI179134_f_at', 'rc_AI104113_at', 'rc_AA819429_f_at' Is there any function to do