title: EMDomics: a robust and powerful method for the identification of genes differentially expressed between heterogeneous classes.
abstract: BACKGROUND
  A major goal of biomedical research is to identify molecular features associated with a biological or clinical class of interest. Differential expression analysis has long been used for this purpose; however, conventional methods perform poorly when applied to data with high within class heterogeneity.
  
  
  RESULTS
  To address this challenge, we developed EMDomics, a new method that uses the Earth mover's distance to measure the overall difference between the distributions of a gene's expression in two classes of samples and uses permutations to obtain q-values for each gene. We applied EMDomics to the challenging problem of identifying genes associated with drug resistance in ovarian cancer. We also used simulated data to evaluate the performance of EMDomics, in terms of sensitivity and specificity for identifying differentially expressed gene in classes with high within class heterogeneity. In both the simulated and real biological data, EMDomics outperformed competing approaches for the identification of differentially expressed genes, and EMDomics was significantly more powerful than conventional methods for the identification of drug resistance-associated gene sets. EMDomics represents a new approach for the identification of genes differentially expressed between heterogeneous classes and has utility in a wide range of complex biomedical conditions in which sample classes show within class heterogeneity.
  
  
  METHODS
  The R package is available at http://www.bioconductor.org/packages/release/bioc/html/EMDomics.html.
authors.affiliation: Center for Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
authors.forename: Sheida
authors.initials: S
authors.lastname: Nabavi
authors.affiliation: Department of Pathology and Cancer Research Institute, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.
authors.forename: Daniel
authors.initials: D
authors.lastname: Schmolze
authors.affiliation: Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA and.
authors.forename: Mayinuer
authors.initials: M
authors.lastname: Maitituoheti
authors.affiliation: Department of Pathology and Cancer Research Institute, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA, The Harker School, San Jose, CA, USA.
authors.forename: Sadhika
authors.initials: S
authors.lastname: Malladi
authors.affiliation: Department of Pathology and Cancer Research Institute, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.
authors.forename: Andrew H
authors.initials: AH
authors.lastname: Beck
mesh_terms: D000970:Antineoplastic Agents / Q000494:pharmacology; D014408:Biomarkers, Tumor / Q000235:genetics*; D016208:Databases, Factual; D019008:Drug Resistance, Neoplasm / Q000235:genetics*; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic / Q000187:drug effects; D053263:Gene Regulatory Networks; D006801:Humans; D010051:Ovarian Neoplasms / Q000188:drug therapy / Q000235:genetics*; D012680:Sensitivity and Specificity; D012984:Software
chemical_list: D000970:Antineoplastic Agents; D014408:Biomarkers, Tumor