search for: probeids

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,

...he 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, and 16 probenos nested within each of the probeids. I have specified in the SAS Proc Mixed procedure that the variance-covariance structure is to be compound symmetric. Therefore, the variance-covariance matrix is a block diagonal matrix of the form, V_1 0 0 0 V_...

Hi all, Im very new to R so please forgive my poor language! I've been trying to map on my list of probeids the relative annotation but unsuccessfully. I get this error symbols <- mget(probes,mouse4302SYMBOL,ifnotfound=NA) Error in .checkKeysAreWellFormed(keys) : keys must be supplied in a character vector with no NAs Thanks for your help! Brawni -- View this message in context: http://r.7896...

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

...he 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, and 16 probenos nested within each of the probeids. I have specified in the SAS Proc Mixed procedure that the variance-covariance structure is to be compound symmetric. Therefore, the variance-covariance matrix is a block diagonal matrix of the form, V_1 0 0 0 V_...

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

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

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

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

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, Am using maanova package for doing anova.But am getting error like this..plz, help me regarding this.. > TGR=read.madata("rmaexpr.dat",designfile="design.dat") Reading one color array. Otherwise change arrayType='twoColor' then read the data again Warning messages: 1: In read.madata("rmaexpr.dat", designfile = "design.dat") : Assume that

Dear R users, I am using the beadarray package. I am trying to upload raw bead-level data using these commands: ######################################################## library(beadarray) datadir <- ("C:/Computer_programs/R/beadarray/cecilia") targets = read.table("targets.txt", sep = "\t", header = TRUE, as.is = TRUE) BLData = readIllumina(arrayNames =NULL,

inputfille snpid indid genotype gvariable probeid gene geneexpression rs1040480 CHB_NA18524 C/T 2 GI_19743926-I PTPRT 5.850586 rs1040480 CHB_NA18526 C/C 1 GI_19743926-I PTPRT 6.028641 rs1040480 CHB_NA18529 C/C 3 GI_19743926-I PTPRT 5.944392 rs1040481 CHB_NA18532 C/C 1 GI_19743926-I PTPRT 5.938578 rs1040481 CHB_NA18537 C/C 2 GI_19743926-I PTPRT 5.874439 rs1040481 CHB_NA18540 C/C 3 GI_19743926-I

Hi, I am looking for a solution to boot MY system on any PC. To store most of the system and all of my data I want to use an USB storage (in my case an external USB harddisk (2.0 capable)). Since booting off an USB device is not an universal thing I would prefer to have a boot disk with a minimal system - just enough to load most (all?) of the system from the attached USB device. Is this an

Hi Rong, This is a very interesting proposal. We've also observed profile quality degradation from CFG destructive pass like loop rotate, and I can see how this proposal would help improve quality of profile that drives later optimization passes in the pipeline. I have a few questions. * How does this affect today's AutoFDO? Specifically, can users upgrade compiler with FS-AutoFDO

Hi all, Here I include an RFC for control flow sensitive AutoFDO (FS-AFDO). This is a joint work with David Li. Questions and feedback are welcome. Thanks, Rong ============= [RFC] Control Flow Sensitive AutoFDO (FS-AFDO) 1. Motivation AFDO profile is derived from PMU samples from running an earlier build binary. PMU samples are indexed by the IP addresses. An offline tool uses the debug

Greetings list, I am new to programming in R, and am using nnet() function for a project on neural networking. Firstly I wish to ask if there is any pdf explaining the algorithm nnet uses, which could tell me what the objects of the nnet class, like 'conn', 'nconn, 'nsunits', n and 'nunits' mean, and how weights are calculated. The package pdf has little or no

Hello everybody, I have a a matrix of 2 columns and over 27k rows. some of the rows are double , so I tried to remove them with the command unique(): > Workbook5 <- read.delim(file = "Workbook5.txt") > dim(Workbook5) [1] 27748 2 > Workbook5 <- unique(Workbook5) > dim(Workbook5) [1] 20101 2 it removed a lot of line, but unfortunately not all of them. I wanted