Ye Chao, Jiang Bo, Zhang Xuegong, Liu Jun S
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 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.
Bioinformatics. 2015 Jun 1;31(11):1842-4. doi: 10.1093/bioinformatics/btv021. Epub 2015 Jan 20.
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.
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).
生物信息学和遗传学中的许多统计问题都可以表述为分类变量与连续变量之间关联的检验。提出了一种用于非参数依赖性检验的动态切片方法,与传统方法(如柯尔莫哥洛夫-斯米尔诺夫检验)相比,该方法已被证明具有更高的功效。我们引入了一个R包dslice,以促进动态切片方法在生物信息学应用(如数量性状基因座研究和基因集富集分析)中的使用。
dslice用Rcpp实现,可在综合R存档网络中获取。该包根据GNU通用公共许可证(版本2或更高版本)分发。