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Abstract SummaryChromosomal copy number variation (CNV) refers to a polymorphism that a DNA segment presents deletion or duplication in the population. The computational algorithms developed to identify this type of variation are usually of high computational complexity. Here we present a user-friendly R package, modSaRa, designed to perform copy number variants identification. The package is developed based on a change-point based method with optimal computational complexity and desirable accuracy. The current version of modSaRa package is a comprehensive tool with integration of preprocessing steps and main CNV calling steps. Availability and ImplementationmodSaRa is an R package written in R, C ++ and Rcpp and is now freely available for download at http://c2s2.yale.edu/software/modSaRa. Supplementary informationSupplementary data are available at Bioinformatics online. 

Summary Many high dimensional classification techniques have been proposed in the literature based on sparse linear discriminant analysis. To use them efficiently, sparsity of linear classifiers is a prerequisite. However, this might not be readily available in many applications, and rotations of data are required to create the sparsity needed. We propose a family of rotations to create the sparsity required. The basic idea is to use the principal components of the sample covariance matrix of the pooled samples and its variants to rotate the data first and then to apply an existing high dimensional classifier. This rotate-and-solve procedure can be combined with any existing classifiers and is robust against the level of sparsity of the true model. We show that these rotations do create the sparsity that is needed for high dimensional classifications and we provide theoretical understanding why such a rotation works empirically. The effectiveness of the method proposed is demonstrated by several simulated and real data examples, and the improvements of our method over some popular high dimensional classification rules are clearly shown.