Ontology highlight
ABSTRACT:
SUBMITTER: Alanis-Lobato G
PROVIDER: S-EPMC4311249 | biostudies-literature | 2015 Jan
REPOSITORIES: biostudies-literature
Scientific reports 20150130
Detecting structure in population genetics and case-control studies is important, as it exposes phenomena such as ecoclines, admixture and stratification. Principal Component Analysis (PCA) is a linear dimension-reduction technique commonly used for this purpose, but it struggles to reveal complex, nonlinear data patterns. In this paper we introduce non-centred Minimum Curvilinear Embedding (ncMCE), a nonlinear method to overcome this problem. Our analyses show that ncMCE can separate individual ...[more]