search for: rowttests

...wo groups each time for t-test, a vs. c or b vs. c, but I dont know how to write correct codes. Below is my codes, the last two lines are needed to be corrected.... library("genefilter") ef <- exprs(esetsub) kk <- factor(esetsub$genotype == c("a", "c")) tt <- rowttests(ef[,c(1,2,5,6)], kk) ps. column 1-6 is a,a,b,b,c,c depending on the document, the kk should be a factor.. Any suggestions are really appreciated!! Best regards, Hui-Yi [[alternative HTML version deleted]]

...000 rows and 100 columns. I want to count correlation (p-value and cor.coef) between each row of dataset and some vector (of course length of this vector is equal to number of columns of dataset). In short words: For t-test we have: "normal" algorithm - t.test "fast" algorithm - rowttests For correlation: "normal" algorithm - cor.test "fast" algorithm - ??? Thank's for help -- View this message in context: http://www.nabble.com/%22Fast%22-correlation-algorithm-tp23548016p23548016.html Sent from the R help mailing list archive at Nabble.com.

Hi all, I have some microarray data on 40 samples that fall into two groups. I have a value for 480k probes for each of those samples. I performed a t test (rowttests) on each row(giving the indices of the columns for each group) then used p.adjust() to adjust the pvalues for the number of tests performed. I then selected only the probes with adj-p.value<=0.05. I end up with roughly 2000 probes to do the clustering on but using pvclust, and hclust, the sample...

I'm working with a genomic data-set with ~31k end-points and have performed an F-test across 5 groups for each end-point. The QA measurments on the individual micro-arrays all look good. One of the first things I do in my work-flow is take a look at the p-valued distribution. it is my understanding that, if the findings are due to chance alone, the p-value distribution should be uniform. In

Hello, I have a question regarding how to speed up the t.test on large dataset. For example, I have a table "tab" which looks like: a b c d e f g h.... 1 2 3 4 5 ... 100000 dim(tab) is 100000 x 100 I need to do the t.test for each row on the two subsets of columns, ie to compare a b d group against e f g group at each row. subset 1: a b d 1 2 3 4 5 ... 100000

Dear R gurus, I have a very embarrassingly parallelizable job that I am trying to speed up with snow on our local cluster. Basically, I am doing ~50,000 t.test for a series of micro-array experiments, one gene at a time. Thus, I can easily spread the load across multiple processors and nodes. So, I have a master list object that tells me what rows to pick up for each genes to do the t.test from

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