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

Content Provider	Springer Nature : BioMed Central
Author	Zhang, Miao Liu, Yiwen Zhou, Hua Watkins, Joseph Zhou, Jin
Abstract	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 transformation incorporates the uncertainty in calling between heterozygotes and the common homozygotes for loci having a rare allele and is more linear when both variants are common. Conclusions We apply MCPCA_PopGen to samples from two indigenous Siberian populations and reveal hidden population structure accurately using only a single chromosome. The MCPCA_PopGen package is available on https://github.com/yiwenstat/MCPCA_PopGen .
Related Links	https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-021-04265-7.pdf
Ending Page	13
Page Count	13
Starting Page	1
File Format	HTM / HTML
ISSN	14712105
DOI	10.1186/s12859-021-04265-7
Journal	BMC Bioinformatics
Issue Number	1
Volume Number	22
Language	English
Publisher	BioMed Central
Publisher Date	2021-06-26
Access Restriction	Open
Subject Keyword	Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Dimension reduction Non-linear kernel Low-coverage Population structure Data-adaptive Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	3.6/2023

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