cc:'ing bioc-list:

> dim(mymatrix)
> [1] 1009 9

Why is the original matrix 1009x9?
It should be 1000x9, yes?

> dim(cor.matrix)
> [1] 9 9

As far as why you are getting a 9x9 correlation matrix, you need to
transpose it prior to estimating the correlation. Eg

cor.matrix <- cor.shrink(t(mymatrix), lambda=0,verbose=TRUE)


Also, you probably want to vary lambda (perhaps by cross validation) to get
a reasonably useful estimate. If you don't shrink the estimate will be too
dense, if you shrink too much it will be overly sparse. I can't remember
off the top of my head whether cor.shrink() handles this or whether you
need to, so check the help page.

*He that would live in peace and at ease, *
*Must not speak all he knows, nor judge all he sees.*

 Benjamin Franklin, Poor Richard's
Almanack<http://archive.org/details/poorrichardsalma00franrich>


On Fri, Nov 1, 2013 at 4:51 PM, PARTH PATEL <parth1415@gmail.com> wrote:

> Hi Tim,
>
> I am trying to calculate co-relation matrix of 1000 genes X 9 timepoints
> and this is my code
>
> mymatrix <- as.matrix(read.table("FinalData_Parth.csv", header=TRUE,
> sep=",",row.names=1,as.is=TRUE))##
> dim(mymatrix)
> cor.matrix <- cor.shrink(mymatrix, lambda=0,verbose=TRUE)mymatrix <-
> as.matrix(read.table("FinalData_Parth.csv", header=TRUE,
> sep=",",row.names=1,as.is=TRUE))##
> dim(mymatrix)
> cor.matrix <- cor.shrink(mymatrix, lambda=0,verbose=TRUE)
>
> But the resulting cor.matrix dimension is different from original input
> matrix (mymatrix):
>
> dim(mymatrix)
>
> [1] 1009    9
>
> dim(cor.matrix)
>
> [1] 9 9
>
>
> Could you please tell me what I am doing wrong? Once I get co-relation
> matrix I can easily perform clustering.
>
>
> Thank you,
> Parth Patel.
>
>
> On Nov 1, 2013, at 4:29 PM, "Tim Triche, Jr." <tim.triche@gmail.com>
> wrote:
>
> how about directly estimating the correlation matrix via shrinkage (e.g.
> http://cran.r-project.org/web/packages/corpcor/index.html ) followed by
> hierarchical clustering?  The direct estimate of [anti]correlation is given
> by the former (i.e., the negative entries in the estimated correlation
> matrix after shrinkage); if you want to proceed with clustering, you can do
> the latter.
>
> Other approaches exist that are better for specific problems, but taken at
> face value, this would seem like the most straightforward for yours as
> stated.
>
>
>
> *He that would live in peace and at ease, *
> *Must not speak all he knows, nor judge all he sees.*
>
>  Benjamin Franklin, Poor Richard's Almanack<http://archive.org/details/poorrichardsalma00franrich>
>
>
> On Fri, Nov 1, 2013 at 7:52 AM, Parth Patel <parth1415@gmail.com> wrote:
>
>> Hi all,
>>
>>
>>
>> I have list of 1000 genes, and I am trying to perform anti-correlated
>> clustering using R. Basically, I am trying to find genes that are
>> anti-correlated out of 1000.
>>
>> Can you tell me what type of package should I use for doing
>> anti-correlated
>> clustering?
>>
>>
>>
>> Thanks in advance,
>>
>> Parth.
>>
>>
>>         [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> Bioconductor mailing list
>> Bioconductor@r-project.org
>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>> Search the archives:
>> http://news.gmane.org/gmane.science.biology.informatics.conductor
>>
>
>

	[[alternative HTML version deleted]]

