[BioC] ExpressionSet Time-series correlation stuff

[Forst, Christian]

Yes, using the transpose and supplying two matrices to cor() is doing the trick.

Thanks again
Chris
________________________________________
From: James W. MacDonald [jmacdon at 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
>>
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> --
> James W. MacDonald, M.S.
> Biostatistician
> University of Washington
> Environmental and Occupational Health Sciences
> 4225 Roosevelt Way NE, # 100
> Seattle WA 98105-6099
>
>
> _______________________________________________
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> https://stat.ethz.ch/mailman/listinfo/bioconductor
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--
James W. MacDonald, M.S.
Biostatistician
University of Washington
Environmental and Occupational Health Sciences
4225 Roosevelt Way NE, # 100
Seattle WA 98105-6099