caculate correlation
this script automatically recognizes what is control among cod and lnc. Note that this script contains a?piece of text that is "grep(".C",cod$name)". This text select - among all column names - those that contain ".C". in my files, I named C1, C2, C3, etc all columns that correspond to controls. In the same manner, I get controls among the lnc, with the text: "grep(".C",lnc$name)"
I`m so sorry,maybe I do not understand you again.
On Tuesday, January 31, 2017 1:27 AM, Jim Lemon <drjimlemon at gmail.com> wrote:
Hi Elham,
This is about the same as your first message. What I meant was, what
do these two expressions return? Is whatever is returned suitable
input for the "cor" function?
coding.rpkm[grep("23.C",coding.rpkm$name),-1]
ncoding.rpkm[grep("23.C",ncoding.rpkm$name),-1]
Jim
On Tue, Jan 31, 2017 at 8:45 AM, Elham - <ed_isfahani at yahoo.com> wrote:
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 <drjimlemon at 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 at 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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