How to Correlate one variable to all other variables on R

user_3071533 0
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I want to correlate one variable (say tyrosine) with all the other variables (about 200 other metabolites, like urea, glucose, inosine, etc) on R, and I'm not sure how to go about it. I'm new to R.

I've learned the pair function, but that pairs every metabolite in the range specified to the other.

Thanks!

correlation r variables
3 Answers

Since you mention "metabolites", I assume your metric is "concentration", e.g. that you have a matrix, call it data that has one column for every metabolite, and one row for every sample.

So, something like this:

# just generates example - YOU SHOULD PROVIDE THIS!!!
data <- data.frame(tyrosine=1:10 + rnorm(10,sd=2), 
                   urea    =2*1:10 + rnorm(10,sd=2),
                   glucose =30 -2*1:10 +rnorm(10,sd=2),
                   inosine =25 -1:10 + rnorm(10,sd=2))
data
     tyrosine      urea  glucose  inosine
1  -0.2529076  5.023562 29.83795 26.71736
2   2.3672866  4.779686 27.56427 22.79442
3   1.3287428  4.757519 24.14913 22.77534
4   7.1905616  3.570600 18.02130 20.89239
5   5.6590155 12.249862 21.23965 17.24588
6   4.3590632 11.910133 17.88774 18.17001
7   7.9748581 13.967619 15.68841 17.21142
8   9.4766494 17.887672 11.05850 16.88137
9  10.1515627 19.642442 11.04370 18.20005
10  9.3892232 21.187803 10.83588 16.52635

To get correlation coefficients, just type:

cor(data)
           tyrosine       urea    glucose    inosine
tyrosine  1.0000000  0.8087897 -0.9545523 -0.8512938
urea      0.8087897  1.0000000 -0.8577782 -0.8086910
glucose  -0.9545523 -0.8577782  1.0000000  0.8608000
inosine  -0.8512938 -0.8086910  0.8608000  1.0000000

To generate a scatterplot matrix, just type:

pairs(data)

In future, please include an example of your data that can be imported into R.

user_29850070

In the following example, I simply split a data frame that contains all of the variables into two matrices. These can be entered into the cor function to obtain your correlation values:

set.seed(1)
n=20
df <- data.frame(tyrosine=runif(n), urea=runif(n), glucose=runif(n), inosine=runif(n))
df

COR <- cor(as.matrix(df[,1]), as.matrix(df[,-1]))
COR
#           urea    glucose    inosine
#[1,] -0.2373854 -0.3672984 -0.3393602
user_11992890

similar to Marc in the box using the apply and column names

> set.seed(1)
> n=20
> df <- data.frame(tyrosine=runif(n), urea=runif(n), glucose=runif(n), 
  inosine=runif(n))

> apply(df,2, function(col)cor(col, df$tyrosine))

tyrosine       urea    glucose    inosine 
1.0000000 -0.2373854 -0.3672984 -0.3393602

It's a good question, and pattern to know for data of reasonable size, as it's efficient if you only want tyrosine cors (what the OP specifically asked) to only calculate tyrosine cors (n time + space), not all vs all (~n^2 time + space).

user_15274030
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