PCA projection bias fix

A new version of EIGENSOFT has just been posted at GitHub (see here). It offers two flags to minimize the problem of Principal Component Analysis (PCA) projection bias or shrinkage: shrinkmode: YES and autoshrink: YES. For more details refer to the contents of the tarball here.

Thus, when running the new EIGENSOFT and you're wanting to project a sample or a set of samples onto the variation of another set of samples, include the lsqproject: YES flag to account for missing data, and then either shrinkmode: YES or autoshrink: YES. I haven't tried this myself yet, but according to the README file in the tarball linked to above, shrinkmode: YES gives better results but takes up much more CPU time.

PCA projection bias is a problem that I've been whining about for a while now (for instance, see here). I actually have my own simple techniques to get around it that appear to work very well, so I'm not sure if I'll be using the new flags. But I might after I try them out. I'd certainly urge the authors of upcoming ancient DNA papers to do so.