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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

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Published on: August 16, 2017

Gene set enrichment analysis using linear models and diagnostics.

Assaf P Oron1, Zhen Jiang, Robert Gentleman

  • 1Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109-1024, USA. assaf.oron@gmail.com

Bioinformatics (Oxford, England)
|September 16, 2008
PubMed
Summary
This summary is machine-generated.

Linear model diagnostics enhance gene-set enrichment analysis (GSEA) by identifying sample issues and biological insights. This method revealed hyperdiploidy and copy number variations in an acute lymphoblastic leukemia (ALL) dataset.

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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Published on: September 18, 2021

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • Gene-set enrichment analysis (GSEA) is a powerful tool for interpreting high-throughput genomic data.
  • Traditional GSEA methods may not fully account for sample-specific effects or model fit.
  • Linear model diagnostics offer a robust approach to enhance GSEA by identifying influential samples and evaluating model assumptions.

Purpose of the Study:

  • To demonstrate the utility of linear model diagnostics for improving gene-set enrichment analysis.
  • To identify problematic samples and potential data errors in genomic datasets.
  • To uncover biological insights, such as chromosomal abnormalities and differential gene expression, using enhanced GSEA.

Main Methods:

  • Application of linear model (regression) diagnostic techniques to GSEA.
  • Utilizing chromosome-band mapping of genes for analysis.
  • Analysis of residuals grouped by chromosomal loci to identify outliers and assess model fit.
  • Comparison of gene expression between hyperdiploid and diploid groups.

Main Results:

  • The methodology successfully identified problematic samples and potential data-entry errors in an adult acute lymphoblastic leukemia (ALL) dataset.
  • Hyperdiploidy was identified as a key factor influencing gene expression in the ALL dataset.
  • Specific DNA copy number abnormalities were pinpointed in samples and chromosomes (X, 21, 14).
  • Significant gene expression differences, not linked to copy number, were found between hyperdiploid and diploid groups on chromosomes 19, 22, 3, and 13.

Conclusions:

  • Linear model diagnostics significantly enhance GSEA by improving sample quality assessment and revealing biological drivers.
  • The approach facilitates the discovery of chromosomal abnormalities and complex gene expression patterns in cancer datasets.
  • The GSEAlm Bioconductor package provides accessible software for implementing these advanced GSEA methods.