An eigenvalue ratio approach to inferring population structure from whole genome sequencing data-Reference-Cited by-同舟云学术

An eigenvalue ratio approach to inferring population structure from whole genome sequencing data

Published:2022-05-19 Issue: Volume: Page:
ISSN:0006-341X
Container-title:Biometrics
language:en
Short-container-title:Biometrics

Author:

Xu Yuyang¹,Liu Zhonghua¹^ORCID,Yao Jianfeng²

Affiliation:

1. Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong SAR China

2. School of Data Science The Chinese University of Hong Kong (Shenzhen) Shenzhen China

Funder

Research Grants Council, University Grants Committee

Publisher

Wiley

Subject

Applied Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13691

Reference33 articles.

1. A global reference for human genetic variation

2. Eigenvalue Ratio Test for the Number of Factors

3. A Random Matrix Theory Approach to Denoise Single-Cell Data

4. On Wigner's semicircle law for the eigenvalues of random matrices;Arnold L.;Probability Theory and Related Fields,1971

5. Central limit theorems for eigenvalues in a spiked population model

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1. ERStruct: a fast Python package for inferring the number of top principal components from whole genome sequencing data;BMC Bioinformatics;2023-05-02

2. ERStruct: A Python Package for Inferring the Number of Top Principal Components from Whole Genome Sequencing Data;2022-08-15