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Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis.

Yan Zhou1, Pei Wang2, Xianlong Wang3

  • 1Merck & Co, North Wales, PA, USA.

Genetic Epidemiology
|November 19, 2016
PubMed
Summary
This summary is machine-generated.

We introduce a new sparse multivariate factor analysis regression model (smFARM) to analyze complex molecular marker associations. This method improves signal detection and accuracy in genomic data, aiding disease pathway understanding.

Keywords:
EM-blockwise coordinate descenthigh-dimensional datalatent factorsregularization

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Multivariate regression models are crucial for understanding complex associations between molecular markers and disease etiology.
  • Accounting for dependency in correlated response variables enhances statistical power in genomic analyses.
  • Existing methods face challenges with high-dimensional data where association parameters exceed sample size.

Purpose of the Study:

  • To propose a novel sparse multivariate factor analysis regression model (smFARM) for integrative genomic data analysis.
  • To address challenges in high-dimensional genomic data, including the number of parameters exceeding sample size.
  • To adjust for unobserved factors influencing response-predictor associations in molecular marker studies.

Main Methods:

  • Developed the sparse multivariate factor analysis regression model (smFARM).
  • Implemented smFARM using the Expectation-Maximization (EM) algorithm and blockwise coordinate descent.
  • Evaluated smFARM performance through extensive simulation studies and comparison with existing methods.

Main Results:

  • smFARM improves sensitivity in signal detection compared to existing methods.
  • The proposed method enhances the accuracy of sparse association map estimation.
  • Identified two trans-hub regions in breast and ovarian cancer datasets, linking DNA copy number variations to gene expression.

Conclusions:

  • Accounting for latent factors via smFARM enhances the analysis of complex molecular marker associations.
  • smFARM provides a powerful tool for integrative genomics, revealing genetic regulatory patterns relevant to cancer.
  • The identified trans-hub regions offer insights into breast cancer gene regulation and ovarian cancer chemoresistance.