Pairwise correlation

Hi Michael,
I was able to solve this. I just used the WGCNA library which allows for
stringsAsFactors to be defined in the work space making everything stored as
strings remain strings. My problem now is parsing through the results to
pull out only significant correlations defined by a certain Pearson
correlation value say 0.8.

On 17 November 2011 15:32, R. Michael Weylandt <michael.weylandt at gmail.com>
wrote:

I can't see how it's stored like that and the email servers garble it
up. Use dput() to create a plain text representation and paste that
back in.

Thanks,
Michael

On Thu, Nov 17, 2011 at 9:37 AM, muzz56 <musahass at gmail.com> wrote:

Hi Michael,
Here is a sample of the data.

?Gene Array1 Array2 Array3 Array4 Array5 Array6 Array7 Array8 Array9
Array10
Array11 ?Fth1 26016.01 23134.66 17445.71 39856.04 27245.45 23622.98
37887.75
49857.46 25864.73 21852.51 29198.4 ?B2m 7573.64 7768.52 6608.24 8571.65
6380.78 6242.76 6903.92 7330.63 7256.18 5678.21 10937.05 ?Tmsb4x 6192.44
4277.22 5024.59 4851.51 3062.55 4562.43 7948.1 5018.58 3200.17 2855.77
6139.23 ?H2-D1 3141.41 3986.06 3328.62 4726.6 3589.89 2885.95 7509.88
5257.62 4742.26 3431.33 5300.72 ?Prdx5 3935.7 3938.9 3401.68 4193.14
4028.95
3438.19 6640.15 5486.61 4424.57 3368.83 5265.92
I want to retain the gene names in the data. What you've proposed will
take
them out and I'll have to append them back to the results after the
cor()

On 17 November 2011 09:33, Michael Weylandt [via R] <
ml-node+s789695n4080177h34 at n4.nabble.com> wrote:

I think something like this should do it, but I can't test without
data:

rownames(mydata) <- mydata[,1] # Put the elements in the first column
as rownames
mydata <- mydata[,-1] # drop the things that are now rownames

Michael

On Thu, Nov 17, 2011 at 9:23 AM, Musa Hassan <[hidden
email]<http://user/SendEmail.jtp?type=node&node=4080177&i=0>>
wrote:

Hi Michael,
Thanks for the response. I have noticed that the error occurred
during

my

data read. It appears that the rownames (which when the data is

transposed

become my colnames) were converted to numbers instead of strings as
they
should be. The original header names don't change, just the rownames.
I

have

to figure out how to import the data and have the strings not
converted.
Right now am using:
mydata = read.csv(mydata.csv, headers=T,stringsAsFactors=F)

then to convert the data frame to matrix
mydata=data.matrix(mydata)

Then I just do the correlation as Peter suggested.

expression=cor(t(expression))

Thanks.

On 17 November 2011 08:51, R. Michael Weylandt <[hidden
email]<http://user/SendEmail.jtp?type=node&node=4080177&i=1>>

wrote:

On Wed, Nov 16, 2011 at 11:22 PM, muzz56 <[hidden
email]<http://user/SendEmail.jtp?type=node&node=4080177&i=2>>

wrote:

Thanks to everyone who replied to my post, I finally got it to
work.

I

am
however not sure how well it worked since it run so quickly, but

seems

like
I have a 2000 x 2000 data set.

Behold the great and mighty power that is R! Don't worry -- on a
decent machine the correlation of a 2k x 2k data set should be
pretty
fast. (It's about 9 seconds on my old-ish laptop with a bunch of
other
junk running)

?My followup questions would be, how do I get
only pairs with say a certain pearson correlation value
additionally

it

seems like my output didn't retain the headers but instead
replaced

them

with numbers making it hard to know which gene pairs correlate.

This is a little worrisome: R carries column names through cor() so
this would suggest you weren't using them. Were your headers listed
as
part of your data (instead of being names)? If so, they would have
been taken as numbers.

Take a look at dimnames(NAMEOFDATA) -- if your headers aren't there,
then they are being treated as data instead of numbers. If they are,
can you provide some reproducible code and we can debug more fully.
The easiest way to send data is to use the dput() function to get a
copy-pasteable plain text representation. It would also be great if
you could restrict it to a subset of your data rather than the full
4M
data points, but if that's hard to do, don't worry.

You should have expected behavior like

X <- matrix(1:9,3)
colnames(X) <- c("A","B","C")
cor(X) # Prints with labels

Michael

On 16 November 2011 17:11, Nordlund, Dan (DSHS/RDA) [via R] <
[hidden email]
<http://user/SendEmail.jtp?type=node&node=4080177&i=3>>

wrote:

-----Original Message-----
From: [hidden
email]<http://user/SendEmail.jtp?type=node&node=4078114&i=0

[mailto:

r-help-bounces at r-

project.org] On Behalf Of muzz56
Sent: Wednesday, November 16, 2011 12:28 PM
To: [hidden
email]<http://user/SendEmail.jtp?type=node&node=4078114&i=1>
Subject: Re: [R] Pairwise correlation

Thanks Peter. I tried this after reading in the csv (read.csv)
and
converted the data to matrix (as.matrix). But when I tried the
correlation,
I keeping getting the error (x must be numeric) yet when I view

the

data,
its numeric.

What does R tell you if you execute the following?

str(x)

Just because the data looks like it is numeric when it prints

doesn't

mean
it is.


Dan

Daniel J. Nordlund
Washington State Department of Social and Health Services
Planning, Performance, and Accountability
Research and Data Analysis Division
Olympia, WA 98504-5204

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