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Last edited by Eric Coissac
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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 :

https://www.biorxiv.org/content/10.1101/842070v1

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.

Results:

Here we propose a corrected version of the Procrustean correlation coefficient that is robust to high dimensional data. This allows for a correct estimation of the shared variation between two data sets and the partial correlation coefficients between a set of matrix data.

Availability

The proposed corrected coefficients are implemented in the ProcMod R package available on https://git.metabarcoding.org/lecasofts/ProcMod

Releases

  • 1.0.0 : Initial release for submission to CRAN
  • 1.0.1 : Bug release to able installation on Windows
  • 1.0.2 : Adds documentation on many public functions
  • 1.0.3 : Adds reference to the bioRxiv manuscript doi: https://doi.org/10.1101/842070

Installation

To install the latest version of ProcMod on your R from that git repository you need to install first the devtools package.

install.package("devtools")

then you can use the following command to install ProcMod

`devtools::install_git("https://git.metabarcoding.org/lecasofts/ProcMod.git",ref="Release/1.0.3")`

Citing

To cite the ProcMod package please use the reference provided by the following R command

citation("ProcMod")