title: Gene set enrichment analysis using linear models and diagnostics.
abstract: BACKGROUND
  Gene-set enrichment analysis (GSEA) can be greatly enhanced by linear model (regression) diagnostic techniques. Diagnostics can be used to identify outlying or influential samples, and also to evaluate model fit and explore model expansion.
  
  
  RESULTS
  We demonstrate this methodology on an adult acute lymphoblastic leukemia (ALL) dataset, using GSEA based on chromosome-band mapping of genes. Individual residuals, grouped or aggregated by chromosomal loci, indicate problematic samples and potential data-entry errors, and help identify hyperdiploidy as a factor playing a key role in expression for this dataset. Subsequent analysis pinpoints suspected DNA copy number abnormalities of specific samples and chromosomes (most prevalent are chromosomes X, 21 and 14), and also reveals significant expression differences between the hyperdiploid and diploid groups on other chromosomes (most prominently 19, 22, 3 and 13)--differences which are apparently not associated with copy number.
  
  
  BACKGROUND
  Software for the statistical tools demonstrated in this article is available as Bioconductor package GSEAlm.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109-1024, USA. assaf.oron@gmail.com
authors.forename: Assaf P
authors.initials: AP
authors.lastname: Oron
authors.forename: Zhen
authors.initials: Z
authors.lastname: Jiang
authors.forename: Robert
authors.initials: R
authors.lastname: Gentleman
mesh_terms: D002875:Chromosomes / Q000235:genetics; D020869:Gene Expression Profiling; D006801:Humans; D007945:Leukemia, Lymphoid / Q000175:diagnosis / Q000235:genetics; D016014:Linear Models; D008957:Models, Genetic; D010641:Phenotype
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