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A novel nonlinear dimension reduction approach to infer population structure for low-coverage sequencing data
BACKGROUND: Low-depth sequencing allows researchers to increase sample size at the expense of lower accuracy. To incorporate uncertainties while maintaining statistical power, we introduce MCPCA_PopGen to analyze population structure of low-depth sequencing data. RESULTS: The method optimizes the ch...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8236193/ https://www.ncbi.nlm.nih.gov/pubmed/34174829 http://dx.doi.org/10.1186/s12859-021-04265-7 |