Showing papers by "Alexander Krasnitz published in 2014"

Computational methods for DNA copy-number analysis of tumors.

Abstract: Study of DNA copy-number variation is a key part of cancer genomics. With the help of a comprehensive multistep computational procedure described here, copy-number profiles of tumor tissues or individual tumor cells may be generated and interpreted, starting with data acquired by next-generation sequencing. Several of the methods presented are specifically designed to handle cancer-related copy-number profiles. These include accounting for variation of ploidy and distilling somatic copy number alterations from the inherited background.

Significant distinct branches of hierarchical trees: a framework for statistical analysis and applications to biological data.

Abstract: One of the most common goals of hierarchical clustering is finding those branches of a tree that form quantifiably distinct data subtypes. Achieving this goal in a statistically meaningful way requires (a) a measure of distinctness of a branch and (b) a test to determine the significance of the observed measure, applicable to all branches and across multiple scales of dissimilarity. We formulate a method termed Tree Branches Evaluated Statistically for Tightness (TBEST) for identifying significantly distinct tree branches in hierarchical clusters. For each branch of the tree a measure of distinctness, or tightness, is defined as a rational function of heights, both of the branch and of its parent. A statistical procedure is then developed to determine the significance of the observed values of tightness. We test TBEST as a tool for tree-based data partitioning by applying it to five benchmark datasets, one of them synthetic and the other four each from a different area of biology. For each dataset there is a well-defined partition of the data into classes. In all test cases TBEST performs on par with or better than the existing techniques. Based on our benchmark analysis, TBEST is a tool of choice for detection of significantly distinct branches in hierarchical trees grown from biological data. An R language implementation of the method is available from the Comprehensive R Archive Network: http://www.cran.r-project.org/web/packages/TBEST/index.html .

Significantly distinct branches of hierarchical trees: A framework for statistical analysis and applications to biological data

Abstract: We formulate a method termed Tree Branches Evaluated Statistically for Tightness (TBEST) for identifying significantly distinct tree branches in hierarchical clusters. For each branch of the tree a measure of tightness is defined as a rational function of heights, both of the branch and of its parent. A statistical procedure is then developed to determine the significance of the observed values of tightness. We test TBEST as a tool for tree-based data partitioning by applying it to four benchmark datasets, each from a different area of biology and each with a well-defined partition of the data into classes. In all cases TBEST performs on par with or better than the existing techniques.