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Related Experiment Video

Updated: Jun 12, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

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Published on: July 27, 2021

Asymptotic conditional singular value decomposition for high-dimensional genomic data.

Jeffrey T Leek1

  • 1Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205-2179, USA. jleek@jhsph.edu

Biometrics
|June 22, 2010
PubMed
Summary
This summary is machine-generated.

This study identifies latent factors in high-dimensional genomic data using a conditional factor model. A new method consistently estimates the number of factors, improving analysis of gene expression and other complex biological datasets.

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Last Updated: Jun 12, 2026

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08:27

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Published on: August 21, 2016

Area of Science:

  • Genomics
  • Statistical Modeling
  • Bioinformatics

Background:

  • Genomic data (gene expression, sequencing) is high-dimensional with many features and few samples.
  • Identifying factors associated with multiple features is crucial for genomic analysis.
  • Determining the number of factors is essential for unsupervised learning methods like clustering.

Purpose of the Study:

  • To develop methods for identifying and estimating latent factors in high-dimensional genomic data.
  • To propose a consistent estimator for the dimension of conditional factor models.
  • To provide a practical approach for selecting the number of factors in real-world datasets.

Main Methods:

  • Utilized a conditional factor model for genomic data analysis.
  • Applied asymptotic consistency of right singular vectors for latent factor estimation.
  • Developed a scaled eigen-decomposition method for dimension estimation.
  • Employed the dependence kernel approach for practical factor selection.

Main Results:

  • Right singular vectors consistently estimate unobserved latent factors as features increase.
  • A novel, consistent estimator for the conditional factor model dimension was proposed.
  • Demonstrated utility in capturing batch effects in microarray data.

Conclusions:

  • The proposed methods provide robust estimation of latent factors and model dimension in high-dimensional genomics.
  • This work enhances the analysis of complex genomic datasets, including the identification of unmodeled effects.
  • The findings are applicable to various genomic data types, including gene expression, SNPs, and methylation data.