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An efficient concordant integrative analysis of multiple large-scale two-sample expression data sets.

Yinglei Lai1, Fanni Zhang1, Tapan K Nayak1

  • 1Department of Statistics, The George Washington University, Washington, DC 20052, USA.

Bioinformatics (Oxford, England)
|February 9, 2017
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Summary
This summary is machine-generated.

This study introduces a reduced mixture model for analyzing multiple gene expression datasets, improving gene set detection. The model simplifies parameter space, enabling more effective analysis of large-scale data like TCGA.

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Area of Science:

  • Bioinformatics and Computational Biology
  • Genomics and Transcriptomics
  • Statistical Genetics

Background:

  • Existing mixture models for integrative analysis of large-scale gene expression data face challenges with exponentially increasing parameter spaces as the number of datasets grows.
  • The current approach is applicable to both microarray and RNA-sequencing data, utilizing transformed differential expression P-values (z-scores).

Purpose of the Study:

  • To develop a reduced mixture model that addresses the scalability issues of previous models for concordant integrative analysis of multiple gene expression datasets.
  • To enhance the detection of gene sets and individual genes by simplifying the model's parameter space.

Main Methods:

  • Inspired by generalized estimating equations (GEEs), the study focuses on concordant components and imposes specific structures (exchangeable, multiset coefficient, autoregressive) on non-concordant components for model reduction.
  • Expectation-maximization (EM) algorithms are developed for parameter estimation within these reduced models.
  • The R-functions for the computer program are made freely available.

Main Results:

  • The reduced mixture model, particularly with the exchangeable structure, significantly increases the detection of gene sets (pathways) and individual genes compared to the general mixture model.
  • Analysis of Cancer Genome Atlas (TCGA) RNA sequencing data demonstrates the advantage of this approach for studying closely related cancer types.
  • The parameter space of the reduced model scales linearly with the number of datasets, overcoming the exponential increase of the general model.

Conclusions:

  • The proposed reduced mixture model offers a scalable and effective approach for the concordant integrative analysis of multiple large-scale gene expression datasets.
  • This method enhances the discovery of biologically relevant gene sets and genes, with demonstrated utility in cancer genomics using TCGA data.