IPCAPS: an R package for iterative pruning to capture population structure.
Fine-scale structure
Iterative pruning
Outlier detection
Population clustering
Population genetics
Journal
Source code for biology and medicine
ISSN: 1751-0473
Titre abrégé: Source Code Biol Med
Pays: England
ID NLM: 101276533
Informations de publication
Date de publication:
2019
2019
Historique:
received:
25
07
2017
accepted:
21
02
2019
entrez:
3
4
2019
pubmed:
3
4
2019
medline:
3
4
2019
Statut:
epublish
Résumé
Resolving population genetic structure is challenging, especially when dealing with closely related or geographically confined populations. Although Principal Component Analysis (PCA)-based methods and genomic variation with single nucleotide polymorphisms (SNPs) are widely used to describe shared genetic ancestry, improvements can be made especially when fine-scale population structure is the target. This work presents an R package called IPCAPS, which uses SNP information for resolving possibly fine-scale population structure. The IPCAPS routines are built on the iterative pruning Principal Component Analysis (ipPCA) framework that systematically assigns individuals to genetically similar subgroups. In each iteration, our tool is able to detect and eliminate outliers, hereby avoiding severe misclassification errors. IPCAPS supports different measurement scales for variables used to identify substructure. Hence, panels of gene expression and methylation data can be accommodated as well. The tool can also be applied in patient sub-phenotyping contexts. IPCAPS is developed in R and is freely available from http://bio3.giga.ulg.ac.be/ipcaps.
Sections du résumé
BACKGROUND
BACKGROUND
Resolving population genetic structure is challenging, especially when dealing with closely related or geographically confined populations. Although Principal Component Analysis (PCA)-based methods and genomic variation with single nucleotide polymorphisms (SNPs) are widely used to describe shared genetic ancestry, improvements can be made especially when fine-scale population structure is the target.
RESULTS
RESULTS
This work presents an R package called IPCAPS, which uses SNP information for resolving possibly fine-scale population structure. The IPCAPS routines are built on the iterative pruning Principal Component Analysis (ipPCA) framework that systematically assigns individuals to genetically similar subgroups. In each iteration, our tool is able to detect and eliminate outliers, hereby avoiding severe misclassification errors.
CONCLUSIONS
CONCLUSIONS
IPCAPS supports different measurement scales for variables used to identify substructure. Hence, panels of gene expression and methylation data can be accommodated as well. The tool can also be applied in patient sub-phenotyping contexts. IPCAPS is developed in R and is freely available from http://bio3.giga.ulg.ac.be/ipcaps.
Identifiants
pubmed: 30936940
doi: 10.1186/s13029-019-0072-6
pii: 72
pmc: PMC6427891
doi:
Types de publication
Journal Article
Langues
eng
Pagination
2Déclaration de conflit d'intérêts
Not applicable.Not applicable.The authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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