gen_tibble
objectsgt_pca.Rd
There are a number of PCA methods available for gen_tibble
objects. They
are mostly designed to work on very large datasets, so they only compute
a limited number of components. For smaller datasets, gt_partialSVD
allows
the use of partial (truncated) SVD to fit the PCA; this method is suitable when
the number of individuals is much smaller than the number of loci. For larger
dataset, gt_randomSVD
is more appropriate. Finally, there is a method
specifically designed for dealing with LD in large datasets, gt_autoSVD
.
Whilst this is arguably the best option, it is somewhat data hungry, and so
only suitable for very large datasets (hundreds of individuals with several hundred
thousands markers, or larger).
NOTE: using gt_pca_autoSVD with a small dataset will likely cause an error, see man page for details.