Hi Elham,
It looks to me as though you are looking for matches in the name field
and then asking for all columns except the first (-1). If you only
have two columns in "coding.rpkm", and "name" is the first column, you
will get whatever is in the second column that has a match in the
"name" column.
Hi Dear Jim,
I did it, both return a vector of name of the genes with different length,as I
said before I have list of coding and noncoding so the length are not same.
where is number?!
and at the end of print there is this error :
<0 rows> (or 0-length row.names)
On Tuesday, January 31,
Hi Elham,
On Tue, Jan 31, 2017 at 7:28 PM, Elham - wrote:
> Hi Dear Jim,
>
> I did it, both return a vector of name of the genes with different length,as
> I said before I have list of coding and noncoding so the length are not
> same.
>
> where is number?!
>
Not in the values you are extracting
Hi Elham,
What I meant is to simply copy these two expressions into the R command line:
coding.rpkm[grep("23.C",coding.rpkm$name),-1]
ncoding.rpkm[grep("23.C",ncoding.rpkm$name),-1]
and see what comes out. If both return a vector of numbers of the same
length with no NA values, my guess was wron
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$name)". This
text select - among all column names - those that contain ".C". in my files, I
named C1, C2, C3, etc all columns that correspond to controls.
Hi Elham,
This is about the same as your first message. What I meant was, what
do these two expressions return? Is whatever is returned suitable
input for the "cor" function?
coding.rpkm[grep("23.C",coding.rpkm$name),-1]
ncoding.rpkm[grep("23.C",ncoding.rpkm$name),-1]
Jim
On Tue, Jan 31, 2017
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
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
wrote:
> for calculating correlation
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
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