Article

A Probabilistic Approach to Visualize the Effect of Missing Data on PCA in Ancient Human Genomics

2025-03-11

Abstract excerpt

<title>Abstract</title> <p>Principal Component Analysis (PCA) is widely used in population genetics to visualize genetic relationships. Methods like SmartPCA enable the projection of ancient samples despite missing genotype data due to degraded DNA, but do not quantify projection uncertainty, risking misinterpretation. We introduce TrustPCA, a probabilistic framework that models the impact of missing loci and pro...

Topics

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

Identifiers and source

Literature Corpus work: 4a6631b8-253d-5a8f-87d4-abdfb16da56e
DOI: 10.21203/rs.3.rs-6105854/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

A Probabilistic Approach to Visualize the Effect of Missing Data on PCA in Ancient Human GenomicsDOI 10.21203/rs.3.rs-6105854/v1Read

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

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

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

Ancient ancestry informative markers for identifying fine-scale ancient population structure in EurasiansSemantic proximity Read

A likelihood method for estimating present-day human contamination in ancient DNA samples using low-depth haploid chromosome dataSemantic proximity Read

Fast and reliable ancestral reconstruction on ancient genotype data with non-negative Least square and Principal Component AnalysisSemantic proximity Read

Sparse sampling and rare-variant depletion distort PCA visualizations of population structure: recovery with objective-guided manifold learningSemantic proximity Read

A Fast, Provably Accurate Approximation Algorithm for Sparse Principal Component Analysis Reveals Human Genetic Variation Across the WorldSemantic proximity Read

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

Why most Principal Component Analyses (PCA) in population genetic studies are wrongSemantic proximity Read

yallHap: Modern Y-chromosome haplogroup inference with probabilistic scoring and ancient DNA supportSemantic proximity Read

A likelihood-free estimator of population structure bridging admixture models and principal components analysisSemantic proximity Read