Article

Why most Principal Component Analyses (PCA) in population genetic studies are wrong

2021-04-12

Abstract excerpt

Principal Component Analysis (PCA) is a multivariate analysis that allows reduction of the complexity of datasets while preserving data covariance and visualizing the information on colorful scatterplots, ideally with only a minimal loss of information. PCA applications are extensively used as the foremost analyses in population genetics and related fields (e.g., animal and plant or medical genetics), implemented...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: fd6c5e85-4728-55e5-90b9-83577dfc45a5
DOI: 10.1101/2021.04.11.439381

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Why most Principal Component Analyses (PCA) in population genetic studies are wrongDOI 10.1101/2021.04.11.439381Read

Evaluation of population structure inferred by principal component analysis or the admixture model.Semantic proximity Read

Exploring Population Structure with Admixture Models and Principal Component Analysis.Semantic proximity Read

Evaluation of population structure inferred by principal component analysis or the admixture modelSemantic proximity Read

Modelling complex population structure using <i>F</i> -statistics and Principal Component AnalysisSemantic proximity Read

Limitations of principal components in quantitative genetic association models for human studiesSemantic proximity Read

Limitations of principal components in quantitative genetic association models for human studies.Semantic proximity Read

Consequences of PCA graphs, SNP codings, and PCA variants for elucidating population structureSemantic proximity Read

Interpreting principal component analyses of spatial population genetic variation.Semantic proximity Read

A genealogical interpretation of principal components analysis.Semantic proximity Read

A roadmap to robust discriminant analysis of principal components.Semantic proximity Read

Genome-wide insights into the patterns and determinants of fine-scale population structure in humans.Semantic proximity Read

Guidelines for standardizing the application of discriminant analysis of principal components to genotype data.Semantic proximity Read