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.

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