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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

Surrogate variable analysis using partial least squares (SVA-PLS) in gene expression studies.

Sutirtha Chakraborty1, Somnath Datta, Susmita Datta

  • 1Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202, USA.

Bioinformatics (Oxford, England)
|January 13, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new method, SVA-PLS, to accurately identify differentially expressed genes in expression profiling studies. SVA-PLS improves sensitivity and specificity by accounting for hidden variables, outperforming standard ANOVA and surrogate variable analysis.

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Published on: June 24, 2021

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Gene expression profiling aims to find differentially expressed genes between sample types.
  • Standard methods like ANOVA are flawed by unaccounted latent variables, causing false positives and negatives.
  • These hidden factors distort true differential expression, reducing study sensitivity and specificity.

Purpose of the Study:

  • To develop a novel method for identifying true differential gene expression.
  • To address limitations of standard analysis in the presence of hidden variability.
  • To improve the accuracy of gene expression profiling studies.

Main Methods:

  • Utilized Partial Least Squares (PLS) to identify hidden variable effects.
  • Applied Analysis of Covariance (ANCOVA) with PLS-identified covariates.
  • Developed a novel method named SVA-PLS for gene expression analysis.

Main Results:

  • SVA-PLS demonstrated superior sensitivity across various simulation settings.
  • The method maintained reasonable specificity, false discovery rate, and false non-discovery rate.
  • Applied to acute megakaryoblastic leukemia, SVA-PLS identified six additional relevant genes missed by other methods.

Conclusions:

  • SVA-PLS effectively identifies true differential gene expression by accounting for latent variables.
  • The method offers improved sensitivity and accuracy in gene expression profiling.
  • This approach enhances the reliability of findings in complex biological studies.