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# **Assessing the shared variation among high-dimensional data matrices: a modified version of the Procrustean correlation coefficient**
The method is fully described in the following *bioRxiv* manuscript :
<center>https://www.biorxiv.org/content/10.1101/842070v1</center>
## Motivation ## Motivation
Molecular biology and ecology studies can produce high dimension data. Estimating correlations and shared variation between such data sets are an important step in disentangling the relationships between different elements of a biological system. Unfortunately, classical approaches are susceptible to producing falsely inferred correlations. Molecular biology and ecology studies can produce high dimension data. Estimating correlations and shared variation between such data sets are an important step in disentangling the relationships between different elements of a biological system. Unfortunately, classical approaches are susceptible to producing falsely inferred correlations.
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