IterClust: a statistical framework for iterative clustering analysis.

ABSTRACT: Motivation:In a scenario where populations A, B1 and B2 (subpopulations of B) exist, pronounced differences between A and B may mask subtle differences between B1 and B2. Results:Here we present iterClust, an iterative clustering framework, which can separate more pronounced differences (e.g. A and B) in starting iterations, followed by relatively subtle differences (e.g. B1 and B2), providing a comprehensive clustering trajectory. Availability and implementation:iterClust is implemented as a Bioconductor R package. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Ding H 

PROVIDER: S-EPMC6084607 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature