[R] finding most highly transcribed genes - ranking, sorting and subsets?

From: alison waller <alison.waller_at_utoronto.ca>
Date: Thu, 6 Dec 2007 14:03:28 -0500


Hello,  

I am not only interested in finding out which genes are the most highly upor  down-regulated (which I have done using the linear models and Bayesian statistics in Limma), but I also want to know which genes are consistently highly transcribed (ie. they have a high intensity in the channel of interest eg. Cy5 or Cy3 across the set of experiments). I might have missed a straight forward way to do this, or a valuable function, but I've been using my own methods and going around in circles.  

So far I've normalized within and between arrays, then returned the RG values using RG<-RG.MA, then I ranked each R and G values for each array as below.

rankRG<-RG

rankRG$R[,1]<-rank(rankRG$R[,1])

rankRG$R[,2]<-rank(rankRG$R[,2]) .. and so on for 6 columns(ie. arrays, as well as the G's)  

then I thought I could pull out a subset of rankRG using something like;

topRG<-rankRG

topRG$R<-subset(topRG$R,topRG$R[,1]<500&topRG$R[,2]<500&topRG$R[,5]<500)  

However, this just returned me a matrix with one row of $R (the ranks were <500 for columns 1,2, and 5 and greater than 500 for 3,4,and 6). However, I can't believe that there is only one gene that is in the top 500 for R intensitiy among those three arrays.  

Am I doing something wrong? Can someone think of a better way of doing this?  

Thanks  

Alison    



Alison S. Waller M.A.Sc.
Doctoral Candidate
awaller_at_chem-eng.utoronto.ca
416-978-4222 (lab)
Department of Chemical Engineering
Wallberg Building
200 College st.
Toronto, ON
M5S 3E5     

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