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Related Experiment Video

Updated: Jul 5, 2026

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
10:50

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

Mapping gene expression quantitative trait loci by singular value decomposition and independent component analysis.

Shameek Biswas1, John D Storey, Joshua M Akey

  • 1Department of Genome Sciences, University of Washington, 1705 NE Pacific Street, Seattle, WA 98195, USA. sbiswas@u.washington.edu

BMC Bioinformatics
|May 22, 2008
PubMed
Summary

Dimension reduction techniques like SVD and ICA analyze multiple gene expression traits simultaneously, improving the power to detect gene expression quantitative trait loci (eQTL). This approach identified novel eQTLs by revealing underlying genetic architecture.

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Area of Science:

  • Genetics
  • Systems Biology
  • Bioinformatics

Background:

  • Gene expression profiling combined with linkage analysis is key for mapping gene expression quantitative trait loci (eQTL).
  • Analyzing individual gene expression traits reduces statistical power due to multiple testing corrections.
  • The inherent correlation structure among gene expression traits is often overlooked in single-trait analyses.

Purpose of the Study:

  • To overcome limitations of single-trait eQTL analysis by employing multivariate dimension reduction techniques.
  • To explore the utility of Singular Value Decomposition (SVD) and Independent Component Analysis (ICA) for eQTL mapping.
  • To investigate the genetic architecture of gene expression variation in Saccharomyces cerevisiae.

Main Methods:

  • Applied Singular Value Decomposition (SVD) and Independent Component Analysis (ICA) to gene expression data from a Saccharomyces cerevisiae cross.

Related Experiment Videos

Last Updated: Jul 5, 2026

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
10:50

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

  • Decomposed gene expression data into meta-traits, which are linear combinations of individual expression traits.
  • Performed genome-wide linkage analysis on the top meta-traits derived from SVD and ICA.

    • Main Results:

    • Meta-traits derived from SVD and ICA were enriched for biologically relevant Gene Ontology categories.
    • Identified a total of 21 eQTLs, with 11 being novel discoveries.
    • Observed both cis- and trans-linkages between genetic loci and the derived meta-traits.

    Conclusions:

    • Dimension reduction methods offer a powerful and complementary approach to traditional eQTL mapping.
    • These techniques effectively probe the complex genetic architecture underlying gene expression variation.
    • The study highlights the utility of multivariate methods for uncovering novel genetic associations in gene expression.