I'm reviewing a paper. In it the authors use a genetic distance metric to created a distance matrix between subjects, then they run classic MDS on this distance matrix. Throughout the MS they call this a principal components analysis (PCA). I was trained to call this principal coordinates analysis (PCoA) or classic Torgerson's multidimensional scaling. Can anyone clarify which terms are correct, and if more than one, which is preferred?

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    $\begingroup$ See stats.stackexchange.com/questions/14002/… $\endgroup$ Aug 21, 2013 at 20:34
  • $\begingroup$ In PCA, you usually have a objects by variables dataset as the input. In PCoA, the input is a square distance matrix.This is their difference. Main math operations and theory behind both are essentially the same. $\endgroup$
    – ttnphns
    Oct 3, 2020 at 14:13


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