Correlation Test Between Microarray And Mass Spec Data?

Question

[James Smith]

I have datasets from microarray and mass spec experiments on the same tissue. I'd like to see if what was quantified from these experiments are correlated. I'm not sure what statistical test or correlation metric to use. I took a look at Pearson, but I know that there won't be a linear relation between these datasets. Can someone point me to some resources where I can read more about analysis of datasets like this?

[seidel]

How do you know there won't be a linear relationship? I've used Pearson in the past for crude comparisons of the two. What did you find with Pearson?

[James Smith]

I didn't try, I assumed that the ordering of the identifiers would affect the correlations calculated using Pearson. Am I wrong about this? @neifws

Answer 1

[Neilfws]

Pearson correlation has been used for this purpose; see e.g. Exon level integration of proteomics and microarray data. However, it's generally used because it's the "default" correlation method that everyone knows, rather than because it's appropriate.

You should probably investigate canonical correlation analysis. There are implementations in many statistics packages; see e.g. CCP in R. CCA is commonly used in bioinformatics for multivariate correlation.

Another useful R package is DRI. It's designed for correlation of copy number and expression data, but I think it will give you useful ideas and perhaps could be adapted to use a matrix of values from proteomics.

One more R package: iCluster, for "integrative clustering of multiple genomic data types." Just requires 2 data matrices; one with your microarray data, the other with the proteomics data.