I have 9 experiments control/treatment that I analysed coding and lncoding,
after that I normalize expression value.as you know we have different known
number of coding and non -coding genes,so for calculating correlation first I
transposed data ,(rows become columns)so row is control&treatment and columns
are gene names.(so I have 2 matrix with same row and different column).This
information is enough?
On Tuesday, January 31, 2017 1:06 AM, Jim Lemon <drjimle...@gmail.com>
wrote:
Hi Elham,
Without knowing much about what coding.rpkm and ncoding.rkpm look
like, it is difficult to say. Have you tried to subset these matrices
as you do in the "cor" function and see what is returned?
Jim
On Tue, Jan 31, 2017 at 6:40 AM, Elham - via R-help
<r-help@r-project.org> wrote:
> for calculating correlation between coding and noncoding,first I transposed
> data ,(rows become columns) so row is control&treatment and columns are gene
> names.(so I have 2 matrix with same row and different column),I use these
> function for calculating correlation but all of spearman correlation are
> NA,why?
>
>
> control.corr=cor(coding.rpkm[grep("23.C",coding.rpkm$name),-1],ncoding.rpkm[grep("23.C",ncoding.rpkm$name),-1],method=
> "spearman")
>
>
>
>
>
>
>
> tumor.corr=cor(coding.rpkm [grep("27.T", coding.rpkm $name),-1], ncoding.rpkm
> [grep("27.T", ncoding.rpkm $name),-1],method = "spearman")
>
> [[alternative HTML version deleted]]
>
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