ArticleScientific reports2015
Highlighting nonlinear patterns in population genetics datasets.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
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- Computing linkage disequilibrium aware genome embeddings using autoencoders.Bioinformatics (Oxford, England) · 2024Article
- Machine learning based combination of multi-omics data for subgroup identification in non-small cell lung cancer.Scientific reports · 2023Article
- Integration of pan-cancer multi-omics data for novel mixed subgroup identification using machine learning methods.PloS one · 2023Article
- KLFDAPC: a supervised machine learning approach for spatial genetic structure analysis.Briefings in bioinformatics · 2022Article
- A deep learning framework for characterization of genotype data.G3 (Bethesda, Md.) · 2022Article
- Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome.Nature communications · 2021Article
- Performance of model-based multifactor dimensionality reduction methods for epistasis detection by controlling population structure.BioData mining · 2021Article
- How to increase our belief in discovered statistical interactions via large-scale association studies?Human genetics · 2019Review
- IPCAPS: an R package for iterative pruning to capture population structure.Source code for biology and medicine · 2019Article
- Ceruloplasmin replacement therapy ameliorates neurological symptoms in a preclinical model of aceruloplasminemia.EMBO molecular medicine · 2018Article
- Geometric characterisation of disease modules.Applied network science · 2018Article
- Clustering: how much bias do we need?Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2017Article
- Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies.Scientific reports · 2017Article
- Integrated Computational Analysis of Genes Associated with Human Hereditary Insensitivity to Pain. A Drug Repurposing Perspective.Frontiers in molecular neuroscience · 2017Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 individuals into ethnic groups in cases in which PCA fails to reveal any clear structure. This increased discrimination power arises from ncMCE's ability to better capture the phylogenetic signal in the samples, whereas PCA better reflects their geographic relation. We also demonstrate how ncMCE can discover interesting patterns, even when the data has been poorly pre-processed. The juxtaposition of PCA and ncMCE visualisations provides a new standard of analysis with utility for discovering and validating significant linear/nonlinear complementary patterns in genetic data.
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