title: Analysis of gene expression data using a linear mixed model/finite mixture model approach: application to regional differences in the human brain.
abstract: BACKGROUND
  Gene expression data exhibit common information over the genome. This article shows how data can be analysed from an efficient whole-genome perspective. Further, the methods have been developed so that users with limited expertise in bioinformatics and statistical computing techniques could use and modify this procedure to their own needs. The method outlined first uses a large-scale linear mixed model for the expression data genome-wide, and then uses finite mixture models to separate differentially expressed (DE) from non-DE transcripts. These methods are illustrated through application to an exceptional UK Brain Expression Consortium involving 12 human frozen post-mortem brain regions.
  
  
  RESULTS
  Fitting linear mixed models has allowed variation in gene expression between different biological states (e.g. brain regions, gender, age) to be investigated. The model can be extended to allow for differing levels of variation between different biological states. Predicted values of the random effects show the effects of each transcript in a particular biological state. Using the UK Brain Expression Consortium data, this approach yielded striking patterns of co-regional gene expression. Fitting the finite mixture model to the effects within each state provides a convenient method to filter transcripts that are DE: these DE transcripts can then be extracted for advanced functional analysis.
  
  
  BACKGROUND
  The data for all regions except HYPO and SPCO are available at the Gene Expression Omnibus (GEO) site, accession number GSE46706. R code for the analysis is available in the Supplementary file.
authors: Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK, Department of Genetics, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia and ReproGen - Animal Bioscience Group, Faculty of Veterinary Science, The University of Sydney, 425 Werombi Road, Camden, NSW 2570, AustraliaDepartment of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK, Department of Genetics, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia and ReproGen - Animal Bioscience Group, Faculty of Veterinary Science, The University of Sydney, 425 Werombi Road, Camden, NSW 2570, Australia.
authors: Daniah
authors: D
authors: Trabzuni
authors: Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK, Department of Genetics, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia and ReproGen - Animal Bioscience Group, Faculty of Veterinary Science, The University of Sydney, 425 Werombi Road, Camden, NSW 2570, Australia.
authors: Peter C
authors: PC
authors: Thomson
mesh_terms: D001921:Brain / Q000378:metabolism*; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D015894:Genome, Human; D006801:Humans; D016014:Linear Models; D008297:Male