Yes, using the transpose and supplying two matrices to cor() is doing the trick. Thanks again Chris ________________________________________ From: James W. MacDonald [jmacdon@uw.edu] Sent: Tuesday, January 22, 2013 17:02 To: Forst, Christian Cc: bioconductor at r-project.org Subject: Re: [BioC] ExpressionSet Time-series correlation stuff On 1/22/2013 4:42 PM, Forst, Christian wrote: > Thanks but it doesn't really do what I want. First I still need to do > sp<- cor(exprs(es[,ts])) > > instead of > sp<- cor(es[,ts]) > > Otherwise I get > Error in cor(es[,ts]) : > supply both 'x' and 'y' or a matrix-like 'x' > > Then, sp is a square matrix over ts and not over the time-correlated genes which I need. > > -------------------------------------- > And what I really want is doing time-forward/backward correlation. Can this be done elegantly with cor()? Or would I have to go back to my for-loops? I don't know. You gave an example below that is apparently not what you are trying to do, as that results in a square matrix and will give the same results as simply using cor(). So evidently you are trying to do something else, and I am not sure what that might be. Maybe you could use the fact that cor() will accept two matrices of compatible dimensions? You can easily reorder columns of a matrix to do whatever you want. > > Chris > > > ________________________________________ > From: James W. MacDonald [jmacdon at uw.edu] > Sent: Tuesday, January 22, 2013 16:21 > To: Forst, Christian > Cc: bioconductor at r-project.org > Subject: Re: [BioC] ExpressionSet Time-series correlation stuff > > Hi Christian, > > On 1/22/2013 4:01 PM, Forst, Christian wrote: >> Is there an easier way to do time-series correlation between genes of an ExpressionSet other than using for-loops and cor()? especially if I want to play with the particular time-series? >> And I am not really happy with the packages I found so far: bioDist, qpgraph, qvalue >> >> I have: >> >> es...ExpressionSet >> ts<- c("t1", "t2", "t3", "t4", "t5") some time series from es (out of many) >> >> sp<- matrix(nrow=10,ncol=10) >> for(i in 1:10) { >> sp[i,i]<- 1. >> for(j in i:10) { >> sp[i,j]<- cor(as.vector(exprs(es[i,ts])), as.vector(exprs(es[j,ts])), method="spearman") >> sp[j,i]<- sp[i,j] >> } >> } >> >> And I actually want to do this for all the 40000 genes in es and not 10 as given in the example. > If you are just trying to compute the correlation matrix then you are > doing things the hard way. Note from ?cor > > cor(x, y = NULL, use = "everything", > method = c("pearson", "kendall", "spearman")) > > > > Arguments: > > x: a numeric vector, matrix or data frame. > > So you can just use > > sp<- cor(es[,ts]) > > HOWEVA, this may be slow and may well require more RAM than you have if > you are doing all 40K genes (which might be sort of silly - you will > have high correlations between genes that never change at any time > point; is that interesting?). > > There is a faster version of cor() implemented in the WGCNA package that > is designed for these larger scale computations. > > Best, > > Jim > > > >> Thanks - Chris >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor > -- > James W. MacDonald, M.S. > Biostatistician > University of Washington > Environmental and Occupational Health Sciences > 4225 Roosevelt Way NE, # 100 > Seattle WA 98105-6099 > > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- James W. MacDonald, M.S. Biostatistician University of Washington Environmental and Occupational Health Sciences 4225 Roosevelt Way NE, # 100 Seattle WA 98105-6099