[BioC] Calculation times

[Claire Wilson]

THanks for that, I shall give it ago, I was just curious as to why it took a matter of seconds in the older version of R/BioConductor and nearly an hour in the current release.

Claire

> -----Original Message-----
> From: Stephen Henderson [mailto:s.henderson at ucl.ac.uk]
> Sent: 06 June 2003 15:10
> To: Claire Wilson
> Subject: RE: [BioC] Calculation times
> 
> 
> esApply is slow. You best remove all the conditions from the 
> calc.func,
> which have to be repeated 22,000 times. 
> 
> or go back to pData and add factor 'groups' for controlA, 
> controlB, treatA,
> treatB.
> 
> >test<-function(x){by(x, groups, mean)}
> 
> >test.matrix<- esApply(SE.exprs,1,test) (20 sec?)
> 
> this will give you four columns of means by factor
> doing calculations on a matrix is quick 
> 
> >testout[1,]/testout[2,]		## would be 1 mean div 
> another (2
> secs?)
> 
> etc.. etc..
> 
> hope it helps
> 
> Stephen
> WIBR, UCL
> 
> 
> 
> 
> 
> -----Original Message-----
> From: Claire Wilson [mailto:ClaireWilson at picr.man.ac.uk] 
> Sent: Friday, June 06, 2003 1:57 PM
> To: BioC mailing list
> Subject: [BioC] Calculation times
> 
> Dear all,
> 
> I have recently upgraded my linux machine from 
> R1.6.2/Bioconductor 1.1 to
> R1.7/Bioconductor 1.2 and have found that the new version 
> runs considerabley
> slower than the old version. So much so that I have a 
> function to calculate
> fold change for an expression set that takes a few minutes to run in
> R1.6.2/BioC 1.1, but well over half an hour to run on the 
> latest version. 
> 
> here is the code..
>  calc.fc <- function(y, x) {
> 	  # split y according to treatment
>     	     exprs.values <- split(y, treatment)
> 	  # exprs.values has 2 elements control in [[1]] and 
> treatment in
> [[2]]
> 	  # exprs.values[[1]][1:3] is the control group for a
> 	  # exprs.values[[1]][4:6] is the control group for b
> 	  # exprs.values[[2]][1:3] is the treatment group for a
> 	  # exprs.values[[2]][4:6] is the treatment group for b
>     if (x == "a") {
>         # Calculate the means expression level for each group in a
>            m1 <- log2(mean(exprs.values[[1]][1:3]))
>            m2 <- log2(mean(exprs.values[[2]][1:3]))
>     }else{
>        # Calculate the means expression level for each group in b
>           m1 <- log2(mean(exprs.values[[1]][4:6]))
>           m2 <- log2(mean(exprs.values[[2]][4:6]))
>      }
>     # Calculate the fold change
>        log.fold.change <- m1 - m2
>     # If it is less than 0 it is a negative fold change
>     # Need to make it positive, take the anti-log and then 
> make it negative
> again
>     # Else just calculate the anti-log
>       if (log.fold.change < 0 ) {
>           x <- -1 * log.fold.change
>           fold.change <- -1 * (2^x)
>       }else {
>           fold.change <- (2^log.fold.change)
>       }
>       return (t(fold.change))
>   }
>   
> # Run the function
>  esApply(eset,1,calc.fc, x="a")
> 
> Any ideas?
> Thanks
> 
> claire
> --
> Claire Wilson   
> Bioinformatics group  
> Paterson Institute for Cancer Research  
> Christies Hospital NHS Trust  
> Wilmslow Road,  
> Withington  
> Manchester  
> M20 4BX  
> tel: +44 (0)161 446 8218  
> url: http://bioinf.picr.man.ac.uk/
>  
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