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POST: A framework for set-based association analysis in high-dimensional data.

Xueyuan Cao1, E Olusegun George2, Mingjuan Wang3

  • 1Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, USA; Department of Acute and Tertiary Care, University of Tennessee Health Science Center, Memphis, USA.

Methods (San Diego, Calif.)
|May 20, 2018
PubMed
Summary
This summary is machine-generated.

Projection onto the Orthogonal Space Testing (POST) is a new method to link gene sets with various clinical data. POST effectively identifies biological processes associated with diseases like pediatric acute myeloid leukemia.

Keywords:
Data integrationGene networkGene profilingOrthogonal projection

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Gene-set analysis is crucial for biological discovery.
  • Current methods are limited to categorical phenotypes (k>=2 groups).
  • This restricts analysis of clinically relevant quantitative or survival data.

Purpose of the Study:

  • To introduce Projection onto the Orthogonal Space Testing (POST) as a general method for gene-set association analysis.
  • To enable robust evaluation of gene sets against diverse phenotypic data types (categorical, ordinal, continuous, censored).
  • To provide a flexible tool for identifying biologically meaningful gene-set associations.

Main Methods:

  • POST transforms gene profiles into eigenvectors.
  • Statistical modeling computes z-statistics for phenotype association.
  • A gene-set statistic is derived from weighted, squared z-statistics.
  • Bootstrapping generates p-values for significance testing.
  • Covariate adjustment is optionally incorporated.

Main Results:

  • POST performance matches or exceeds existing methods for categorical phenotypes in simulations.
  • POST identified the WNT signaling pathway as a top hit for pediatric acute myeloid leukemia relapse.
  • The method successfully associated 875 biological processes with clinical data.

Conclusions:

  • POST is a versatile and robust tool for gene-set association studies.
  • It expands the scope of gene-set analysis to include various clinical phenotypes.
  • An R package 'POST' is available on Bioconductor for broader accessibility.