title: dslice: an R package for nonparametric testing of associations with application in QTL and gene set analysis.
abstract: Many statistical problems in bioinformatics and genetics can be formulated as the testing of associations between a categorical variable and a continuous variable. A dynamic slicing method was proposed for non-parametric dependence testing, which has been demonstrated to have higher powers compared with traditional methods such as Kolmogorov-Smirnov test. We introduce an R package dslice to facilitate the use of dynamic slicing method in bioinformatic applications such as quantitative trait loci study and gene set enrichment analysis.
  
  
  METHODS
  dslice is implemented in Rcpp and available in the Comprehensive R Archive Network. The package is distributed under the GNU General Public License (version 2 or later).
authors.affiliation: MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing 100084, China, Department of Statistics, Harvard University, Cambridge, MA 02138, USA, School of Life Sciences, Tsinghua University, Beijing 100084, China and Center of Statistics, Tsinghua University, Beijing 100084, China.
authors.forename: Chao
authors.initials: C
authors.lastname: Ye
authors.affiliation: MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing 100084, China, Department of Statistics, Harvard University, Cambridge, MA 02138, USA, School of Life Sciences, Tsinghua University, Beijing 100084, China and Center of Statistics, Tsinghua University, Beijing 100084, China.
authors.forename: Bo
authors.initials: B
authors.lastname: Jiang
authors.affiliation: MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing 100084, China, Department of Statistics, Harvard University, Cambridge, MA 02138, USA, School of Life Sciences, Tsinghua University, Beijing 100084, China and Center of Statistics, Tsinghua University, Beijing 100084, China MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing 100084, China, Department of Statistics, Harvard University, Cambridge, MA 02138, USA, School of Life Sciences, Tsinghua University, Beijing 100084, China and Center of Statistics, Tsinghua University, Beijing 100084, China.
authors.forename: Xuegong
authors.initials: X
authors.lastname: Zhang
authors.affiliation: MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing 100084, China, Department of Statistics, Harvard University, Cambridge, MA 02138, USA, School of Life Sciences, Tsinghua University, Beijing 100084, China and Center of Statistics, Tsinghua University, Beijing 100084, China MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing 100084, China, Department of Statistics, Harvard University, Cambridge, MA 02138, USA, School of Life Sciences, Tsinghua University, Beijing 100084, China and Center of Statistics, Tsinghua University, Beijing 100084, China.
authors.forename: Jun S
authors.initials: JS
authors.lastname: Liu
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods; D015870:Gene Expression; D014644:Genetic Variation; D006801:Humans; D051379:Mice; D010641:Phenotype; D040641:Quantitative Trait Loci; D012984:Software; D018709:Statistics, Nonparametric