title: Detecting multivariate differentially expressed genes.
abstract: BACKGROUND
  Gene expression is governed by complex networks, and differences in expression patterns between distinct biological conditions may therefore be complex and multivariate in nature. Yet, current statistical methods for detecting differential expression merely consider the univariate difference in expression level of each gene in isolation, thus potentially neglecting many genes of biological importance.
  
  
  RESULTS
  We have developed a novel algorithm for detecting multivariate expression patterns, named Recursive Independence Test (RIT). This algorithm generalizes differential expression testing to more complex expression patterns, while still including genes found by the univariate approach. We prove that RIT is consistent and controls error rates for small sample sizes. Simulation studies confirm that RIT offers more power than univariate differential expression analysis when multivariate effects are present. We apply RIT to gene expression data sets from diabetes and cancer studies, revealing several putative disease genes that were not detected by univariate differential expression analysis.
  
  
  CONCLUSIONS
  The proposed RIT algorithm increases the power of gene expression analysis by considering multivariate effects while retaining error rate control, and may be useful when conventional differential expression tests yield few findings.
authors.affiliation: Computational Biology, Department of Physics, Linköping University, Linköping, Sweden. rolle@ifm.liu.se
authors.forename: Roland
authors.initials: R
authors.lastname: Nilsson
authors.forename: José M
authors.initials: JM
authors.lastname: Peña
authors.forename: Johan
authors.initials: J
authors.lastname: Björkegren
authors.forename: Jesper
authors.initials: J
authors.lastname: Tegnér
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics; D030541:Databases, Genetic; D003920:Diabetes Mellitus / Q000235:genetics; D005260:Female; D005786:Gene Expression Regulation / Q000235:genetics*; D006801:Humans; D015999:Multivariate Analysis; D040081:Protein Array Analysis / Q000379:methods