similar to: Bioconductor, merging annotation with list of probeids

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,

Hi, I have been trying to find a solution to this issue, but have not been able to so ! I am trying to use sapply on the function getSYMBOL, an extract from the list is: > test.goP[13:14] $`GO:0000050` IEA IEA IEA IEA TAS TAS TAS IEA "5270753" "5720725" "1690128" "4850681" "110433" "2640544"

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,

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,

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, 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 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

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, 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 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 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

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 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

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

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