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P-value calculation on difference in correlation

Hi,

I have a slightly basic statistics question. I have two large correlation matrices (PCC) (one in wildtype condition, other in treatment condition) of related features. They are about 10000x10000. What i'm interested in is mainly how treatment affects the correlated nature of these features. In other words, the change in correlation. For downstream analysis, I have to use an in-house tool of our group that requires significance values (p-values). Therefore, i'm interested in the probability of a particular deltaPCC occurring due to chance.

Does it make any sense to calculate a random set of deltaPCC by selecting random features from two matrices, calculate difference of correlation from these matching or unmatching features, repeat this process for like 100000 times and calculate p-value from this distribution by looking at the percentage of generated deltaPCC's that are larger (or smaller in the other tail) than the deltaPCC of interest?

Thanks!

statistics
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updated by Jean-Karim Heriche Jean-Karim Heriche • written by jth jth

1 answer

You can use the Mantel test if the two matrices are independent. The Mantel test measures the correlation between two distance/similarity matrices and gives an associated p-value. The test can be found in several R packages: ape, ade4, vegan.

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written by Jean-Karim Heriche Jean-Karim Heriche

Looks interesting! Thanks a lot!

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written by jth jth

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