title: Más-o-menos: a simple sign averaging method for discrimination in genomic data analysis.
abstract: BACKGROUND
  The successful translation of genomic signatures into clinical settings relies on good discrimination between patient subgroups. Many sophisticated algorithms have been proposed in the statistics and machine learning literature, but in practice simpler algorithms are often used. However, few simple algorithms have been formally described or systematically investigated.
  
  
  RESULTS
  We give a precise definition of a popular simple method we refer to as más-o-menos, which calculates prognostic scores for discrimination by summing standardized predictors, weighted by the signs of their marginal associations with the outcome. We study its behavior theoretically, in simulations and in an extensive analysis of 27 independent gene expression studies of bladder, breast and ovarian cancer, altogether totaling 3833 patients with survival outcomes. We find that despite its simplicity, más-o-menos can achieve good discrimination performance. It performs no worse, and sometimes better, than popular and much more CPU-intensive methods for discrimination, including lasso and ridge regression.
  
  
  METHODS
  Más-o-menos is implemented for survival analysis as an option in the survHD package, available from http://www.bitbucket.org/lwaldron/survhd and submitted to Bioconductor.
authors: Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61820, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115, City University of New York School of Public Health, Hunter College, New York, NY 10035, USA.
authors: Sihai Dave
authors: SD
authors: Zhao
authors: Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61820, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115, City University of New York School of Public Health, Hunter College, New York, NY 10035, USA Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61820, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115, City University of New York School of Public Health, Hunter College, New York, NY 10035, USA.
authors: Giovanni
authors: G
authors: Parmigiani
authors: Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61820, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115, City University of New York School of Public Health, Hunter College, New York, NY 10035, USA.
authors: Curtis
authors: C
authors: Huttenhower
authors: Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61820, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115, City University of New York School of Public Health, Hunter College, New York, NY 10035, USA.
authors: Levi
authors: L
authors: Waldron
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D023281:Genomics / Q000379:methods; D006801:Humans; D010051:Ovarian Neoplasms / Q000235:genetics; D016019:Survival Analysis; D001749:Urinary Bladder Neoplasms / Q000235:genetics