[BioC] ExpressionSet Time-series correlation stuff

[James W. MacDonald]

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