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Article

A novel nonlinear dimension reduction approach to infer population structure for low-coverage sequencing data.

BMC bioinformatics - 26 Jun 2021

Zhang Miao, Liu Yiwen, Zhou Hua, Watkins Joseph, Zhou Jin

Abstract excerpt

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 choice of nonlinear transformations of dosages to maximize the Ky Fan norm of the covariance matrix. The...

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Identifiers

PMID: 34174829
DOI: 10.1186/s12859-021-04265-7
PMCID: PMC8236193

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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