title: Selection of higher order regression models in the analysis of multi-factorial transcription data.
abstract: BACKGROUND
  Many studies examine gene expression data that has been obtained under the influence of multiple factors, such as genetic background, environmental conditions, or exposure to diseases. The interplay of multiple factors may lead to effect modification and confounding. Higher order linear regression models can account for these effects. We present a new methodology for linear model selection and apply it to microarray data of bone marrow-derived macrophages. This experiment investigates the influence of three variable factors: the genetic background of the mice from which the macrophages were obtained, Yersinia enterocolitica infection (two strains, and a mock control), and treatment/non-treatment with interferon-γ.
  
  
  RESULTS
  We set up four different linear regression models in a hierarchical order. We introduce the eruption plot as a new practical tool for model selection complementary to global testing. It visually compares the size and significance of effect estimates between two nested models. Using this methodology we were able to select the most appropriate model by keeping only relevant factors showing additional explanatory power. Application to experimental data allowed us to qualify the interaction of factors as either neutral (no interaction), alleviating (co-occurring effects are weaker than expected from the single effects), or aggravating (stronger than expected). We find a biologically meaningful gene cluster of putative C2TA target genes that appear to be co-regulated with MHC class II genes.
  
  
  CONCLUSIONS
  We introduced the eruption plot as a tool for visual model comparison to identify relevant higher order interactions in the analysis of expression data obtained under the influence of multiple factors. We conclude that model selection in higher order linear regression models should generally be performed for the analysis of multi-factorial microarray data.
authors.affiliation: Institute for Medical Microbiology, Immunology and Hygiene, Technische Universität München, Munich, Germany.
authors.forename: Olivia
authors.initials: O
authors.lastname: Prazeres da Costa
authors.affiliation: St. Mary's hospital, Department of Medicine, Frankfurt, Germany; Medical Department, Johannes Gutenberg University, Mainz, Germany.
authors.forename: Arthur
authors.initials: A
authors.lastname: Hoffman
authors.affiliation: Medical Department, Johannes Gutenberg University, Mainz, Germany.
authors.forename: Johannes W
authors.initials: JW
authors.lastname: Rey
authors.affiliation: Institute for Medical Informatics, Biometry and Epidemiology (IBE), Ludwig-Maximilians-Universität München, Munich, Germany.
authors.forename: Ulrich
authors.initials: U
authors.lastname: Mansmann
authors.affiliation: Institute for Medical Microbiology, Immunology and Hygiene, Technische Universität München, Munich, Germany.
authors.forename: Thorsten
authors.initials: T
authors.lastname: Buch
authors.affiliation: Department of Plant Breeding and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany; Institute for Genetics, University of Cologne, Cologne, Germany.
authors.forename: Achim
authors.initials: A
authors.lastname: Tresch
mesh_terms: D000818:Animals; D001665:Binding Sites; D020869:Gene Expression Profiling; D016014:Linear Models; D008264:Macrophages / Q000378:metabolism; D051379:Mice; D008810:Mice, Inbred C57BL; D020411:Oligonucleotide Array Sequence Analysis; D012044:Regression Analysis; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism; D015009:Yersinia Infections / Q000235:genetics*
chemical_list: D014157:Transcription Factors