Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Highlighting nonlinear patterns in population genetics datasets.

Scientific reports - 30 Jan 2015

Alanis-Lobato Gregorio, Cannistraci Carlo Vittorio, Eriksson Anders, Manica Andrea, Ravasi Timothy

Abstract excerpt

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 comple...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Asian People
Share to Databases, Genetic

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetics, Population

Share to Genotype

Share to Nonlinear Dynamics

Share to Principal Component Analysis

Share to Statistics, Nonparametric

Source

Open in PubMed Open full text

Identifiers

PMID: 25633916
DOI: 10.1038/srep08140
PMCID: PMC4311249

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026. Prepared record publication-25633916-2026-09-04.2. Manifest publication-graph-cohort-2026-09-04.2.

Source receipt: PubMed via Helena Literature Corpus, retrieved 4 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Highlighting nonlinear patterns in population genetics datasets.PMID 25633916Read ↗

Revealing multi-scale population structure in large cohortsSemantic proximity Read ↗

Inherent Nonlinear Distribution of High-Dimensional Genotypic Data Identified as a Possible Source of Confounding Factors in Population Structure Analysis.Semantic proximity Read ↗

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

KLFDAPC: A Supervised Machine Learning Approach for Spatial Genetic Structure AnalysisSemantic proximity Read ↗

UMAP reveals cryptic population structure and phenotype heterogeneity in large genomic cohorts.Semantic proximity Read ↗

A deep learning framework for characterization of genotype data.Semantic proximity Read ↗

Application of t-SNE to Human Genetic DataSemantic proximity Read ↗

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

Manifold learning for human population structure studies.Semantic proximity Read ↗

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

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

Application of t-SNE to human genetic data.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.