An Eigenvalue test for spatial principal component analysis.

ABSTRACT:

Background

The spatial Principal Component Analysis (sPCA, Jombart (Heredity 101:92-103, 2008) is designed to investigate non-random spatial distributions of genetic variation. Unfortunately, the associated tests used for assessing the existence of spatial patterns (global and local test; (Heredity 101:92-103, 2008) lack statistical power and may fail to reveal existing spatial patterns. Here, we present a non-parametric test for the significance of specific patterns recovered by sPCA.

Results

We compared the performance of this new test to the original global and local tests using datasets simulated under classical population genetic models. Results show that our test outperforms the original global and local tests, exhibiting improved statistical power while retaining simi

SUBMITTER: Montano V 

PROVIDER: S-EPMC5732370 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature