Questions about clusterization from the newbie
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tiv_ ▴ 10
@tiv_-897
Last seen 9.6 years ago
Hello, All! First I'd like to thank all people who are working on Bioconductor project - you are doing great job! I'm new to this list, to R, to all that biocondactor things, and actually to the statistics it self... so please excuse me if I'll ask smith trivial. I'm doing the clusterization of the microarray data. Here is the example of my routine sample.files<-c("1.cel","2.cel","3.cel","4.cel") sample.names<-c("1.cel","2.cel","3.cel","4.cel") data<-ReadAffy(filenames=sample.files) data_rma<-rma(data) write.exprs(data_rma, file="data_norm_rma.txt") x<-read.delim("data_norm_rma.txt") # strange, but mt.teststat(data_rma,data.c) its not working # without writing and reading back, is it ok? data.c<-c(0,0,1,1) teststat<-mt.teststat(x,data.c) rawp0<-2*(1-pnorm(abs(teststat))) procs<-c("Bonferroni") res<-mt.rawp2adjp(rawp0,procs) adjp<-res$adjp[order(res$index),] which<-mt.reject(adjp,0.01)$which[,2] results<-table2[which,2] #table2 is the annotation table saveText(results,"sorted_bonf.txt") As a result I have a list of genes that changed their expression. Can you give me a hint how can I separate "up regulated" from "down regulated", must be smiting with t-statistics? Also I wonder is there any opportunity to cluster .res files? I mean something like this: 1000_at 75.5 P 447.75085 P 166.3828 P 147.51279 P 1001_at 6.3 A 34.502922 P 48.250244 P 16.545227 A I tried just to move out all columns with A&P and use the result as sours for mt.teststat(x,data.c), but... seems that it's not the right way. I'll be happy to read any comments on all this :) Thanks for your attention, Tanya
Microarray Annotation Microarray Annotation • 645 views
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@james-w-macdonald-5106
Last seen 15 hours ago
United States
tiv_ wrote: > # strange, but mt.teststat(data_rma,data.c) its not working > # without writing and reading back, is it ok? mt.teststat is expecting your data to be in a matrix or a data.frame, so you have to first extract using the exprs() function, e.g., mt.teststat(exprs(data_rma), data.c) > > data.c<-c(0,0,1,1) > teststat<-mt.teststat(x,data.c) > rawp0<-2*(1-pnorm(abs(teststat))) This is not correct. The p-values for a t-statistic are calculated based on the t-distribution rather than the normal distribution (which will make a big difference with only two degrees of freedom). rawp0 <- 2*(1-pt(abs(teststat), df=2) > procs<-c("Bonferroni") > res<-mt.rawp2adjp(rawp0,procs) > adjp<-res$adjp[order(res$index),] > which<-mt.reject(adjp,0.01)$which[,2] > results<-table2[which,2] #table2 is the annotation table > saveText(results,"sorted_bonf.txt") > > As a result I have a list of genes that changed their expression. Can > you give me a hint how can I separate "up regulated" from "down > regulated", must be smiting with t-statistics? Maybe something like this: sig.genes <- teststat[which] ups <- sig.genes > 0 downs <- sig.genes < 0 up.genes <- results[ups] down.genes <- results[downs] -or- results <- cbind(results, sig.genes) results <- results[order(results[,2]),] HTH, Jim -- James W. MacDonald Affymetrix and cDNA Microarray Core University of Michigan Cancer Center 1500 E. Medical Center Drive 7410 CCGC Ann Arbor MI 48109
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