Fast randomization of large genomic datasets while preserving alteration counts.

ABSTRACT:

Motivation

Studying combinatorial patterns in cancer genomic datasets has recently emerged as a tool for identifying novel cancer driver networks. Approaches have been devised to quantify, for example, the tendency of a set of genes to be mutated in a 'mutually exclusive' manner. The significance of the proposed metrics is usually evaluated by computing P-values under appropriate null models. To this end, a Monte Carlo method (the switching-algorithm) is used to sample simulated datasets under a null model that preserves patient- and gene-wise mutation rates. In this method, a genomic dataset is represented as a bipartite network, to which Markov chain updates (switching-steps) are applied. These steps modify the network topology, and a minimal number of them must be executed to dr

SUBMITTER: Gobbi A 

PROVIDER: S-EPMC4147926 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature