diff (Beginner's question)
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@peter-robinson-529
Last seen 9.6 years ago
Dear all, I have analyzed one data with (8 arrays of 4 wildtype and 4 ko) set basically according to the methods outlined in the End2Endlab vignette. The analysis includes a function: scores <- esApply(eset,1,function(x) { tmp <- t.test(x[index2],x[index1],var.equal=T) c(mean(tmp$estimate),-diff(tmp$estimate),tmp$statistic, tmp$p.value) }) I would like to compare this with another dataset with one array each (pooled RNAs) wt vs ko (for a related gene being knocked out). I used the rma function to produce an eset of the second dataset, and then tried to use e <- -diff(exprs(eset2)) in order to produce the average log ratio for the second dataset with the intention of adding it to "scores" as follows: scores2 <- cbind(scores,e) However, e is missing the first line as compared to the first dataset dim(e) 12487 2 dim(scores) 12488 5 and in fact 100001_at is missing from e. I would greatly appreciate comments or suggestions! Peter Robinson
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@adaikalavan-ramasamy-437
Last seen 9.6 years ago
3rd line of the "Value" section of help(diff) : " If x is a matrix then the difference operations are carried out on each column separately. " i.e. the difference returned as c( row2 -row1, row3 - row2, row4 - row3, ... ) and naturally row_(n+1) - row_n is undefined as there is no row_(n+1), thus the resulting data will have one less row than the original one. If you use diff(exprs(x)), then you are calculating difference between consecutive genes which does not have any meaning. Try diff( t( exprs(x) ) ) or simply (x)[,1] - exprs(x)[,2]. -- Adaikalavan Ramasamy -----Original Message----- From: bioconductor-bounces@stat.math.ethz.ch [mailto:bioconductor-bounces@stat.math.ethz.ch] On Behalf Of peter robinson Sent: Wednesday, November 26, 2003 9:07 AM To: bioconductor@stat.math.ethz.ch Subject: [BioC] diff (Beginner's question) Dear all, I have analyzed one data with (8 arrays of 4 wildtype and 4 ko) set basically according to the methods outlined in the End2Endlab vignette. The analysis includes a function: scores <- esApply(eset,1,function(x) { tmp <- t.test(x[index2],x[index1],var.equal=T) c(mean(tmp$estimate),-diff(tmp$estimate),tmp$statistic, tmp$p.value) }) I would like to compare this with another dataset with one array each (pooled RNAs) wt vs ko (for a related gene being knocked out). I used the rma function to produce an eset of the second dataset, and then tried to use e <- -diff(exprs(eset2)) in order to produce the average log ratio for the second dataset with the intention of adding it to "scores" as follows: scores2 <- cbind(scores,e) However, e is missing the first line as compared to the first dataset dim(e) 12487 2 dim(scores) 12488 5 and in fact 100001_at is missing from e. I would greatly appreciate comments or suggestions! Peter Robinson _______________________________________________ Bioconductor mailing list Bioconductor@stat.math.ethz.ch https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
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Dear BioC, I would be grateful for your help, I wish to filter genes which are invariant across all arrays. I have been looking at genefilter. To start with I tried to set a threshold value of 100 (default of genefilter) across all arrays. However if you ask for a min value of 100 across all arrays, genefilter return null. I have 24 arrays in an exprs called mas.x1 > a<-genefilter(mas.x1, filterfun(kOverA(24))) > sum(a) [1] 0 > a<-genefilter(mas.x1, filterfun(pOverA(1))) > sum(a) However if I make an artifical value just less than the count of arrays it returns a gene list. Am I missing something here? > a<-genefilter(mas.x1, filterfun(pOverA(.999999))) > sum(a) [1] 8950 > a<-genefilter(mas.x1, filterfun(kOverA(23.9))) > sum(a) [1] 8950 Thanks for your help in this, Aedin
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