R help - Apr 2011 - In need of help with correlations

I am in need of someone's help in correlating gene expression. I'm
somewhat
new to R, and can't seem to find anyone local to help me with what I think
is a simple problem.

I need to obtain pearson and spearman correlation coefficients, and
corresponding p-values for all of the genes in my dataset that correlate to
one specific gene of interest. I'm working with mouse Affymetrix Mouse 430
2.0 arrays, so I've got about 45,000 probesets (rows; with 1st column
containing identifiers) and 30 biological replicates (columns; with the top
row containing the header information).

I've looked through several Intro manuals and the R help files.

I know that "cor(x,y, use ="everything", method =
c("pearson")) " can help
obtain the coefficients.

I also know that "cor.test()" is supposed to test the significance of
a
single correlation coefficients.

I've also found the bioconductor package "genefilter" /
"genefinder" that
looks for correlations to a given gene (although I can't get it to work).

So far I've been able to:

#Read in the csv file
data<-read.csv("my data.csv")

#Check the dimensions, names, class, fix(data)  to ensure the file was
loaded properly
dim(data)
names(data)
class(data)
fix(data)

#So far I've been able to successfully correlate the entire 'column'
matrix
through:
x <- data[,2:30]
y <- data[,2:30]

corr.data<-cor(x,y, use = "everything", method =
c("pearson"))

write.csv(corr.data, file = "correlation of my data by columns.csv")

-----------------------------------

Now if I try and run the 'cor.test()' function on the same matrix, I get
and
error message with 'x' must be a numeric vector. This I don't
understand.
And this is not my goal, but rather me trying to learn how to go about doing
correlation analysis in R.

I've also tried transposing the data.frame using
"as.data.frame(t(data))"
and doing so gives the same error message as above.

Can anyone help me with figuring out how to conduct a correlation analysis
for specific gene/probeset, and help me understand why I get the above error
message? I know it probably is a simple analysis, that is probably just over
my head right now since I'm still new to R. But I can't figure it out
and
have been trying with a bunch of different variations for the past week.

Thank you in advance for your help.

	[[alternative HTML version deleted]]

I do not know the bioconductor packages you mentioned, but the corr.test
function in the psych package, or the rcorr function in the Hmisc package
should do the work.
Also note that the c() in method=c("pearson") is redundant. Just write
method="pearson" instead (or nothing, since this is the default for
both
functions).

HTH,
  Denes

> I am in need of someone's help in correlating gene expression. I'm
> somewhat
> new to R, and can't seem to find anyone local to help me with what I
think
> is a simple problem.
>
> I need to obtain pearson and spearman correlation coefficients, and
> corresponding p-values for all of the genes in my dataset that correlate
> to
> one specific gene of interest. I'm working with mouse Affymetrix Mouse
430
> 2.0 arrays, so I've got about 45,000 probesets (rows; with 1st column
> containing identifiers) and 30 biological replicates (columns; with the
> top
> row containing the header information).
>
> I've looked through several Intro manuals and the R help files.
>
> I know that "cor(x,y, use ="everything", method =
c("pearson")) " can help
> obtain the coefficients.
>
> I also know that "cor.test()" is supposed to test the
significance of a
> single correlation coefficients.
>
> I've also found the bioconductor package "genefilter" /
"genefinder" that
> looks for correlations to a given gene (although I can't get it to
work).
>
> So far I've been able to:
>
> #Read in the csv file
> data<-read.csv("my data.csv")
>
> #Check the dimensions, names, class, fix(data)  to ensure the file was
> loaded properly
> dim(data)
> names(data)
> class(data)
> fix(data)
>
> #So far I've been able to successfully correlate the entire
'column'
> matrix
> through:
> x <- data[,2:30]
> y <- data[,2:30]
>
> corr.data<-cor(x,y, use = "everything", method =
c("pearson"))
>
> write.csv(corr.data, file = "correlation of my data by
columns.csv")
>
> -----------------------------------
>
> Now if I try and run the 'cor.test()' function on the same matrix,
I get
> and
> error message with 'x' must be a numeric vector. This I don't
understand.
> And this is not my goal, but rather me trying to learn how to go about
> doing
> correlation analysis in R.
>
> I've also tried transposing the data.frame using
"as.data.frame(t(data))"
> and doing so gives the same error message as above.
>
> Can anyone help me with figuring out how to conduct a correlation analysis
> for specific gene/probeset, and help me understand why I get the above
> error
> message? I know it probably is a simple analysis, that is probably just
> over
> my head right now since I'm still new to R. But I can't figure it
out and
> have been trying with a bunch of different variations for the past week.
>
> Thank you in advance for your help.
>
> 	[[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

Hi


r-help-bounces at r-project.org napsal dne 09.04.2011 19:24:38:
> I am in need of someone's help in correlating gene expression. I'm 
somewhat
> new to R, and can't seem to find anyone local to help me with what I 
think
> is a simple problem.
> 
> I need to obtain pearson and spearman correlation coefficients, and
> corresponding p-values for all of the genes in my dataset that correlate 
to
> one specific gene of interest. I'm working with mouse Affymetrix Mouse 
430
> 2.0 arrays, so I've got about 45,000 probesets (rows; with 1st column
> containing identifiers) and 30 biological replicates (columns; with the 
top
> row containing the header information).
> 
> I've looked through several Intro manuals and the R help files.
> 
> I know that "cor(x,y, use ="everything", method =
c("pearson")) " can
help
> obtain the coefficients.
> 
> I also know that "cor.test()" is supposed to test the
significance of a
> single correlation coefficients.
> 
> I've also found the bioconductor package "genefilter" /
"genefinder"
that
> looks for correlations to a given gene (although I can't get it to 
work).
> 
> So far I've been able to:
> 
> #Read in the csv file
> data<-read.csv("my data.csv")
> 
> #Check the dimensions, names, class, fix(data)  to ensure the file was
> loaded properly
> dim(data)
> names(data)
> class(data)
> fix(data)
> 
> #So far I've been able to successfully correlate the entire
'column'
matrix
> through:
> x <- data[,2:30]
> y <- data[,2:30]
> 
> corr.data<-cor(x,y, use = "everything", method =
c("pearson"))
> 
> write.csv(corr.data, file = "correlation of my data by
columns.csv")
> 
> -----------------------------------
> 
> Now if I try and run the 'cor.test()' function on the same matrix,
I get
and
> error message with 'x' must be a numeric vector. This I don't 
understand.

In cor.test help page it is said

x, y: numeric vectors of data values.  ?x? and ?y? must have the
          same length.

however your data[,2:30] is most probably data frame, see

str(data[,2:20])

To be able to do cor.test  you need to do cor.test like

cor.test(data[,2], data[,3])

or to do it in some cycle (untested)
result <- matrix(NA, 20,20)

for( i in 2:20) {
for(j in i+1:20) {

result[i,j] <- cor.test(data[,i], data[,j])
}}

But most probably there are other ways.

Regards
Petr

> And this is not my goal, but rather me trying to learn how to go about 
doing
> correlation analysis in R.
> 
> I've also tried transposing the data.frame using 
"as.data.frame(t(data))"
> and doing so gives the same error message as above.
> 
> Can anyone help me with figuring out how to conduct a correlation 
analysis
> for specific gene/probeset, and help me understand why I get the above 
error
> message? I know it probably is a simple analysis, that is probably just 
over
> my head right now since I'm still new to R. But I can't figure it
out
and
> have been trying with a bunch of different variations for the past week.
> 
> Thank you in advance for your help.
> 
>    [[alternative HTML version deleted]]
> 
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide 
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

On Sat, Apr 9, 2011 at 10:24 AM, Sean Farris <farrissp2 at vcu.edu>
wrote:
> I am in need of someone's help in correlating gene expression. I'm
somewhat
> new to R, and can't seem to find anyone local to help me with what I
think
> is a simple problem.
>
> I need to obtain pearson and spearman correlation coefficients, and
> corresponding p-values for all of the genes in my dataset that correlate to
> one specific gene of interest. I'm working with mouse Affymetrix Mouse
430
> 2.0 arrays, so I've got about 45,000 probesets (rows; with 1st column
> containing identifiers) and 30 biological replicates (columns; with the top
> row containing the header information).
Sean,

I'm the maintainer of the package WGCNA that does correlation network
analysis of gene expression data. I recommend you check out the
package and the tutorials at

http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/index.html

The package contains a couple useful functions for correlation
p-values. Unlike cor.test which only takes two vectors (not matrices),
you can use the function corAndPvalue to calculate Pearson
correlations and the corresponding p-values for matrices. If you
already have the correlation matrix pre-calculated AND you have no
missing data (i.e., constant number of observations), you can also use
corPvalueStudent to calculate the p-values.

We don't use Spearman correlations much (we prefer the biweight
midcorrelation, functions bicor and bicorAndPvalue, as a robust
alternative to Pearson correlation), but you can approximate the
Spearman p-values by the Student p-values (that are used for Pearson
correlations). Statisticians who read this, please don't execute me
for this suggestion :)

To use the function cor(), you need to transpose the data so that
genes are in columns and samples in rows.
Just be aware that to correlate all probe sets at a time you need a
40k+ times 40k+ matrix to hold the result. Only a large computer (at
least 32GB of memory, possibly needing 64GB) will be able to handle
such a matrix and the necessary manipulations. The WGCNA package
contains methods to construct co-expression networks on such big sets
if necessary.

HTH,

Peter